{
"GutenTAG_0": {
"image": "VisAnomBench_plot_test/test_0.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=10) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3968, 4133)"
},
"GutenTAG_1": {
"image": "VisAnomBench_plot_test/test_1.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.87, amplitude=1.47). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=3) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3983, 4114)"
},
"GutenTAG_2": {
"image": "VisAnomBench_plot_test/test_101.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.36, amplitude=1.37). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.05) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2840, 2895)"
},
"GutenTAG_3": {
"image": "VisAnomBench_plot_test/test_102.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=70, amplitude=0.98, variance-pattern-length=0.2, variance-amplitude=1.3). Anomalies: A variance change that adjusts the noise level. (variance=0.34) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1033, 1223)"
},
"GutenTAG_4": {
"image": "VisAnomBench_plot_test/test_103.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=61, amplitude=1.34, variance-pattern-length=0.13, variance-amplitude=1.21). Anomalies: A variance change that adjusts the noise level. (variance=0.67) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2127, 2274)"
},
"GutenTAG_5": {
"image": "VisAnomBench_plot_test/test_104.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.36). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.31) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(747, 856)"
},
"GutenTAG_6": {
"image": "VisAnomBench_plot_test/test_105.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.05). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1917, 1992)"
},
"GutenTAG_7": {
"image": "VisAnomBench_plot_test/test_106.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.13). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2983, 3091)"
},
"GutenTAG_8": {
"image": "VisAnomBench_plot_test/test_107.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=66, amplitude=0.77, variance-pattern-length=0.14, variance-amplitude=1.18). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1141, 1141)"
},
"GutenTAG_9": {
"image": "VisAnomBench_plot_test/test_108.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=62, amplitude=0.79, variance-pattern-length=0.22, variance-amplitude=1.44). Anomalies: A variance change that adjusts the noise level. (variance=0.99) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1272, 1363)"
},
"GutenTAG_10": {
"image": "VisAnomBench_plot_test/test_109.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.4, amplitude=1.08). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3049, 3049)"
},
"GutenTAG_11": {
"image": "VisAnomBench_plot_test/test_11.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=2.41). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2610, 2743)"
},
"GutenTAG_12": {
"image": "VisAnomBench_plot_test/test_110.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=1.68). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.1) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1486, 1600)"
},
"GutenTAG_13": {
"image": "VisAnomBench_plot_test/test_111.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=94, amplitude=1.34, variance-pattern-length=0.29, variance-amplitude=1.44). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2338, 2338)"
},
"GutenTAG_14": {
"image": "VisAnomBench_plot_test/test_113.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=92, amplitude=1.26, variance-pattern-length=0.44, variance-amplitude=1.16). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=9) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1810, 1973)"
},
"GutenTAG_15": {
"image": "VisAnomBench_plot_test/test_114.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.03) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3724, 3789)"
},
"GutenTAG_16": {
"image": "VisAnomBench_plot_test/test_115.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=84, amplitude=0.87, variance-pattern-length=0.52, variance-amplitude=1.03). Anomalies: A variance change that adjusts the noise level. (variance=0.79) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3118, 3293)"
},
"GutenTAG_17": {
"image": "VisAnomBench_plot_test/test_116.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=44, random-seed=96). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3180, 3267)"
},
"GutenTAG_18": {
"image": "VisAnomBench_plot_test/test_117.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=3). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1565, 1615)"
},
"GutenTAG_19": {
"image": "VisAnomBench_plot_test/test_118.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=88, amplitude=1.23, variance-pattern-length=0.59, variance-amplitude=1.18). Anomalies: A variance change that adjusts the noise level. (variance=0.67) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1246, 1403)"
},
"GutenTAG_20": {
"image": "VisAnomBench_plot_test/test_119.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.08). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=10) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2017, 2177)"
},
"GutenTAG_21": {
"image": "VisAnomBench_plot_test/test_12.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=48, random-seed=74). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(803, 918)"
},
"GutenTAG_22": {
"image": "VisAnomBench_plot_test/test_121.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=43, random-seed=55). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3731, 3864)"
},
"GutenTAG_23": {
"image": "VisAnomBench_plot_test/test_123.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=42, random-seed=8). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2472, 2616)"
},
"GutenTAG_24": {
"image": "VisAnomBench_plot_test/test_124.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.81, amplitude=2). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=9) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2420, 2473)"
},
"GutenTAG_25": {
"image": "VisAnomBench_plot_test/test_126.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=8). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.62) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(927, 1091)"
},
"GutenTAG_26": {
"image": "VisAnomBench_plot_test/test_127.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.79). Anomalies: A variance change that adjusts the noise level. (variance=0.64) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1765, 1878)"
},
"GutenTAG_27": {
"image": "VisAnomBench_plot_test/test_128.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=3). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.22) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3649, 3807)"
},
"GutenTAG_28": {
"image": "VisAnomBench_plot_test/test_130.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.83). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=10) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3133, 3328)"
},
"GutenTAG_29": {
"image": "VisAnomBench_plot_test/test_131.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=31, random-seed=42). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3524, 3664)"
},
"GutenTAG_30": {
"image": "VisAnomBench_plot_test/test_132.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.51). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.37) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1705, 1855)"
},
"GutenTAG_31": {
"image": "VisAnomBench_plot_test/test_133.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.72, amplitude=1.74). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=23) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3874, 4010)"
},
"GutenTAG_32": {
"image": "VisAnomBench_plot_test/test_134.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.37, amplitude=1.05). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4260, 4332)"
},
"GutenTAG_33": {
"image": "VisAnomBench_plot_test/test_135.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.08, amplitude=1.18). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.3) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2015, 2203)"
},
"GutenTAG_34": {
"image": "VisAnomBench_plot_test/test_136.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=49, random-seed=71). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2561, 2664)"
},
"GutenTAG_35": {
"image": "VisAnomBench_plot_test/test_138.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=2.28). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.11) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2216, 2318)"
},
"GutenTAG_36": {
"image": "VisAnomBench_plot_test/test_139.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: An extremum that forces a local or global minimum/maximum. (min=False, local=True) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1947, 1947)"
},
"GutenTAG_37": {
"image": "VisAnomBench_plot_test/test_14.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=5). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.54) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3822, 3991)"
},
"GutenTAG_38": {
"image": "VisAnomBench_plot_test/test_140.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=8). Anomalies: A variance change that adjusts the noise level. (variance=0.86) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2417, 2565)"
},
"GutenTAG_39": {
"image": "VisAnomBench_plot_test/test_141.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.24). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1419, 1419)"
},
"GutenTAG_40": {
"image": "VisAnomBench_plot_test/test_142.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4255, 4408)"
},
"GutenTAG_41": {
"image": "VisAnomBench_plot_test/test_143.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=43, random-seed=24). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3384, 3525)"
},
"GutenTAG_42": {
"image": "VisAnomBench_plot_test/test_145.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.47). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=11) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(609, 701)"
},
"GutenTAG_43": {
"image": "VisAnomBench_plot_test/test_146.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.91). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(671, 869)"
},
"GutenTAG_44": {
"image": "VisAnomBench_plot_test/test_148.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=38, random-seed=50). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3438, 3518)"
},
"GutenTAG_45": {
"image": "VisAnomBench_plot_test/test_15.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.43). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.55) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4455, 4533)"
},
"GutenTAG_46": {
"image": "VisAnomBench_plot_test/test_150.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.07, amplitude=1.4). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=10) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3186, 3311)"
},
"GutenTAG_47": {
"image": "VisAnomBench_plot_test/test_152.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=2.49). Anomalies: A variance change that adjusts the noise level. (variance=0.92) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(649, 808)"
},
"GutenTAG_48": {
"image": "VisAnomBench_plot_test/test_153.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.31) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2138, 2199)"
},
"GutenTAG_49": {
"image": "VisAnomBench_plot_test/test_154.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.35). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.32) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1461, 1635)"
},
"GutenTAG_50": {
"image": "VisAnomBench_plot_test/test_155.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.81). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.99) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2472, 2642)"
},
"GutenTAG_51": {
"image": "VisAnomBench_plot_test/test_156.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.39, amplitude=1.2). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3416, 3416)"
},
"GutenTAG_52": {
"image": "VisAnomBench_plot_test/test_157.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=46, random-seed=8). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2208, 2270)"
},
"GutenTAG_53": {
"image": "VisAnomBench_plot_test/test_158.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=49, random-seed=7). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2540, 2702)"
},
"GutenTAG_54": {
"image": "VisAnomBench_plot_test/test_159.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=35, random-seed=57). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3337, 3510)"
},
"GutenTAG_55": {
"image": "VisAnomBench_plot_test/test_16.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.32, amplitude=1.04). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=25) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1601, 1704)"
},
"GutenTAG_56": {
"image": "VisAnomBench_plot_test/test_160.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.03). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.38) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2024, 2083)"
},
"GutenTAG_57": {
"image": "VisAnomBench_plot_test/test_163.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=2). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(672, 773)"
},
"GutenTAG_58": {
"image": "VisAnomBench_plot_test/test_164.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.92). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(703, 847)"
},
"GutenTAG_59": {
"image": "VisAnomBench_plot_test/test_165.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.86). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2050, 2137)"
},
"GutenTAG_60": {
"image": "VisAnomBench_plot_test/test_166.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.27, amplitude=2.2). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=8) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1549, 1695)"
},
"GutenTAG_61": {
"image": "VisAnomBench_plot_test/test_167.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=4.21). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2065, 2136)"
},
"GutenTAG_62": {
"image": "VisAnomBench_plot_test/test_169.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3690, 3837)"
},
"GutenTAG_63": {
"image": "VisAnomBench_plot_test/test_17.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=46, random-seed=2). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2444, 2578)"
},
"GutenTAG_64": {
"image": "VisAnomBench_plot_test/test_170.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.27, amplitude=1.21). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3546, 3546)"
},
"GutenTAG_65": {
"image": "VisAnomBench_plot_test/test_171.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=89, amplitude=0.31, variance-pattern-length=0.24, variance-amplitude=1.49). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.24) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1668, 1819)"
},
"GutenTAG_66": {
"image": "VisAnomBench_plot_test/test_172.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=6). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.85) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2877, 2982)"
},
"GutenTAG_67": {
"image": "VisAnomBench_plot_test/test_173.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=5). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.18) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3070, 3225)"
},
"GutenTAG_68": {
"image": "VisAnomBench_plot_test/test_174.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.39). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=24) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4139, 4239)"
},
"GutenTAG_69": {
"image": "VisAnomBench_plot_test/test_175.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(885, 1042)"
},
"GutenTAG_70": {
"image": "VisAnomBench_plot_test/test_177.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=4.1). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=11) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3894, 4085)"
},
"GutenTAG_71": {
"image": "VisAnomBench_plot_test/test_178.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.91). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.05) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2738, 2807)"
},
"GutenTAG_72": {
"image": "VisAnomBench_plot_test/test_179.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=48, random-seed=43). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2023, 2104)"
},
"GutenTAG_73": {
"image": "VisAnomBench_plot_test/test_180.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=72, amplitude=0.82, variance-pattern-length=0.14, variance-amplitude=1.13). Anomalies: A variance change that adjusts the noise level. (variance=0.61) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2809, 2885)"
},
"GutenTAG_74": {
"image": "VisAnomBench_plot_test/test_182.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=49, random-seed=14). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(812, 965)"
},
"GutenTAG_75": {
"image": "VisAnomBench_plot_test/test_183.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=46, random-seed=93). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3358, 3408)"
},
"GutenTAG_76": {
"image": "VisAnomBench_plot_test/test_184.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.49, amplitude=1.59). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3221, 3314)"
},
"GutenTAG_77": {
"image": "VisAnomBench_plot_test/test_185.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A variance change that adjusts the noise level. (variance=0.78) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1481, 1578)"
},
"GutenTAG_78": {
"image": "VisAnomBench_plot_test/test_187.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.37, amplitude=2.11). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.99) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3685, 3826)"
},
"GutenTAG_79": {
"image": "VisAnomBench_plot_test/test_188.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=42, random-seed=45). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2818, 3007)"
},
"GutenTAG_80": {
"image": "VisAnomBench_plot_test/test_189.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.57, amplitude=1.45). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(553, 553)"
},
"GutenTAG_81": {
"image": "VisAnomBench_plot_test/test_19.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.7). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.48) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3351, 3434)"
},
"GutenTAG_82": {
"image": "VisAnomBench_plot_test/test_190.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.18). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=9) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4075, 4193)"
},
"GutenTAG_83": {
"image": "VisAnomBench_plot_test/test_191.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=4.16). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.45) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2024, 2081)"
},
"GutenTAG_84": {
"image": "VisAnomBench_plot_test/test_192.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=4.34). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.99) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3834, 3922)"
},
"GutenTAG_85": {
"image": "VisAnomBench_plot_test/test_193.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=45, random-seed=58). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3477, 3553)"
},
"GutenTAG_86": {
"image": "VisAnomBench_plot_test/test_194.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=30, random-seed=6). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2849, 2949)"
},
"GutenTAG_87": {
"image": "VisAnomBench_plot_test/test_195.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.82). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.28) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(564, 756)"
},
"GutenTAG_88": {
"image": "VisAnomBench_plot_test/test_196.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=80, amplitude=0.92, variance-pattern-length=0.44, variance-amplitude=1.46). Anomalies: A variance change that adjusts the noise level. (variance=0.29) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3612, 3706)"
},
"GutenTAG_89": {
"image": "VisAnomBench_plot_test/test_197.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.75, amplitude=1.03). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(590, 708)"
},
"GutenTAG_90": {
"image": "VisAnomBench_plot_test/test_199.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=47, random-seed=23). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1316, 1386)"
},
"GutenTAG_91": {
"image": "VisAnomBench_plot_test/test_2.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=4.22). Anomalies: A pattern shift that re-aligns the local waveform in time. (shift_by=5, transition_window=14) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1882, 2012)"
},
"GutenTAG_92": {
"image": "VisAnomBench_plot_test/test_200.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=91, amplitude=0.35, variance-pattern-length=0.41, variance-amplitude=1.47). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1963, 1963)"
},
"GutenTAG_93": {
"image": "VisAnomBench_plot_test/test_201.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=96, amplitude=1.31, variance-pattern-length=0.14, variance-amplitude=1.23). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4303, 4303)"
},
"GutenTAG_94": {
"image": "VisAnomBench_plot_test/test_202.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=68, amplitude=1.08, variance-pattern-length=0.18, variance-amplitude=1.23). Anomalies: A variance change that adjusts the noise level. (variance=0.91) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2485, 2570)"
},
"GutenTAG_95": {
"image": "VisAnomBench_plot_test/test_203.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=65, amplitude=1.28, variance-pattern-length=0.15, variance-amplitude=1.13). Anomalies: A variance change that adjusts the noise level. (variance=0.83) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2751, 2888)"
},
"GutenTAG_96": {
"image": "VisAnomBench_plot_test/test_204.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.47). Anomalies: An extremum that forces a local or global minimum/maximum. (min=False, local=True) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1503, 1503)"
},
"GutenTAG_97": {
"image": "VisAnomBench_plot_test/test_205.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.8, amplitude=1.79). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.5) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4222, 4411)"
},
"GutenTAG_98": {
"image": "VisAnomBench_plot_test/test_207.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=96, amplitude=0.94, variance-pattern-length=0.32, variance-amplitude=1.13). Anomalies: A variance change that adjusts the noise level. (variance=0.6) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(861, 996)"
},
"GutenTAG_99": {
"image": "VisAnomBench_plot_test/test_208.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.08). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.02) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(806, 935)"
},
"GutenTAG_100": {
"image": "VisAnomBench_plot_test/test_209.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=2.02). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.1) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(583, 706)"
},
"GutenTAG_101": {
"image": "VisAnomBench_plot_test/test_210.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=46, random-seed=81). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2129, 2188)"
},
"GutenTAG_102": {
"image": "VisAnomBench_plot_test/test_211.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=10). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3298, 3440)"
},
"GutenTAG_103": {
"image": "VisAnomBench_plot_test/test_212.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.32, amplitude=2.28). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.47) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(510, 695)"
},
"GutenTAG_104": {
"image": "VisAnomBench_plot_test/test_213.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=40, random-seed=75). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2165, 2229)"
},
"GutenTAG_105": {
"image": "VisAnomBench_plot_test/test_214.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=40, random-seed=6). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3404, 3541)"
},
"GutenTAG_106": {
"image": "VisAnomBench_plot_test/test_216.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=4.48). Anomalies: A variance change that adjusts the noise level. (variance=0.66) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4055, 4218)"
},
"GutenTAG_107": {
"image": "VisAnomBench_plot_test/test_217.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=1.76). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1672, 1777)"
},
"GutenTAG_108": {
"image": "VisAnomBench_plot_test/test_218.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A variance change that adjusts the noise level. (variance=0.73) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2918, 2999)"
},
"GutenTAG_109": {
"image": "VisAnomBench_plot_test/test_219.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.16). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=11) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3518, 3648)"
},
"GutenTAG_110": {
"image": "VisAnomBench_plot_test/test_220.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.8). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2311, 2497)"
},
"GutenTAG_111": {
"image": "VisAnomBench_plot_test/test_221.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.01, amplitude=1.34). Anomalies: A variance change that adjusts the noise level. (variance=0.38) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1431, 1493)"
},
"GutenTAG_112": {
"image": "VisAnomBench_plot_test/test_222.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=65, amplitude=0.96, variance-pattern-length=0.18, variance-amplitude=1.42). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.74) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1370, 1497)"
},
"GutenTAG_113": {
"image": "VisAnomBench_plot_test/test_223.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=65, amplitude=0.88, variance-pattern-length=0.23, variance-amplitude=1.06). Anomalies: A variance change that adjusts the noise level. (variance=0.47) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1761, 1867)"
},
"GutenTAG_114": {
"image": "VisAnomBench_plot_test/test_224.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.72). Anomalies: A variance change that adjusts the noise level. (variance=0.7) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3942, 4064)"
},
"GutenTAG_115": {
"image": "VisAnomBench_plot_test/test_225.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.39) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2251, 2384)"
},
"GutenTAG_116": {
"image": "VisAnomBench_plot_test/test_226.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=68, amplitude=0.91, variance-pattern-length=0.2, variance-amplitude=1.03). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3338, 3530)"
},
"GutenTAG_117": {
"image": "VisAnomBench_plot_test/test_227.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=51, amplitude=0.91, variance-pattern-length=0.19, variance-amplitude=1.09). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1421, 1499)"
},
"GutenTAG_118": {
"image": "VisAnomBench_plot_test/test_229.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.35, amplitude=1.35). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2147, 2306)"
},
"GutenTAG_119": {
"image": "VisAnomBench_plot_test/test_23.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.06). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=8) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(993, 1102)"
},
"GutenTAG_120": {
"image": "VisAnomBench_plot_test/test_230.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=50, random-seed=85). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2954, 3084)"
},
"GutenTAG_121": {
"image": "VisAnomBench_plot_test/test_231.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=10) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4022, 4100)"
},
"GutenTAG_122": {
"image": "VisAnomBench_plot_test/test_232.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.23). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3206, 3384)"
},
"GutenTAG_123": {
"image": "VisAnomBench_plot_test/test_233.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.49, amplitude=1.37). Anomalies: An extremum that forces a local or global minimum/maximum. (min=False, local=True) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3685, 3685)"
},
"GutenTAG_124": {
"image": "VisAnomBench_plot_test/test_234.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=66, amplitude=1.18, variance-pattern-length=0.13, variance-amplitude=1.04). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.17) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2189, 2352)"
},
"GutenTAG_125": {
"image": "VisAnomBench_plot_test/test_235.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=84, amplitude=1.4, variance-pattern-length=0.51, variance-amplitude=1.77). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=23) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2464, 2547)"
},
"GutenTAG_126": {
"image": "VisAnomBench_plot_test/test_236.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.84, amplitude=1.23). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=11) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1633, 1693)"
},
"GutenTAG_127": {
"image": "VisAnomBench_plot_test/test_237.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.65). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.24) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(937, 1023)"
},
"GutenTAG_128": {
"image": "VisAnomBench_plot_test/test_238.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=3). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.56) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2038, 2210)"
},
"GutenTAG_129": {
"image": "VisAnomBench_plot_test/test_239.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=77, amplitude=1.04, variance-pattern-length=0.14, variance-amplitude=1.12). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1889, 1889)"
},
"GutenTAG_130": {
"image": "VisAnomBench_plot_test/test_24.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=46, random-seed=83). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4386, 4483)"
},
"GutenTAG_131": {
"image": "VisAnomBench_plot_test/test_240.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.24) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3452, 3550)"
},
"GutenTAG_132": {
"image": "VisAnomBench_plot_test/test_241.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.32). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=11) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3135, 3318)"
},
"GutenTAG_133": {
"image": "VisAnomBench_plot_test/test_242.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.22). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.09) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2621, 2762)"
},
"GutenTAG_134": {
"image": "VisAnomBench_plot_test/test_243.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=8). Anomalies: A variance change that adjusts the noise level. (variance=0.35) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2001, 2122)"
},
"GutenTAG_135": {
"image": "VisAnomBench_plot_test/test_244.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1246, 1378)"
},
"GutenTAG_136": {
"image": "VisAnomBench_plot_test/test_245.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.8). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.25) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2601, 2701)"
},
"GutenTAG_137": {
"image": "VisAnomBench_plot_test/test_247.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.73). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.36) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(814, 979)"
},
"GutenTAG_138": {
"image": "VisAnomBench_plot_test/test_249.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=99, amplitude=0.96, variance-pattern-length=0.38, variance-amplitude=1.02). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.04) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(612, 709)"
},
"GutenTAG_139": {
"image": "VisAnomBench_plot_test/test_25.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.39). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2442, 2442)"
},
"GutenTAG_140": {
"image": "VisAnomBench_plot_test/test_250.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.62, amplitude=1.24). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1022, 1022)"
},
"GutenTAG_141": {
"image": "VisAnomBench_plot_test/test_251.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=82, amplitude=1.37, variance-pattern-length=0.45, variance-amplitude=1.58). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=10) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1166, 1354)"
},
"GutenTAG_142": {
"image": "VisAnomBench_plot_test/test_252.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.2). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4331, 4331)"
},
"GutenTAG_143": {
"image": "VisAnomBench_plot_test/test_254.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3566, 3725)"
},
"GutenTAG_144": {
"image": "VisAnomBench_plot_test/test_255.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2294, 2481)"
},
"GutenTAG_145": {
"image": "VisAnomBench_plot_test/test_256.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=2.01). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.48) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3854, 3942)"
},
"GutenTAG_146": {
"image": "VisAnomBench_plot_test/test_257.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=5). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.09) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4045, 4199)"
},
"GutenTAG_147": {
"image": "VisAnomBench_plot_test/test_258.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.09, amplitude=1.37). Anomalies: A variance change that adjusts the noise level. (variance=0.92) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(659, 817)"
},
"GutenTAG_148": {
"image": "VisAnomBench_plot_test/test_259.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.37, amplitude=1.38). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.44) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2721, 2831)"
},
"GutenTAG_149": {
"image": "VisAnomBench_plot_test/test_260.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.34). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.09) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2550, 2608)"
},
"GutenTAG_150": {
"image": "VisAnomBench_plot_test/test_261.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=5). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=10) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2393, 2579)"
},
"GutenTAG_151": {
"image": "VisAnomBench_plot_test/test_262.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.82). Anomalies: A pattern shift that re-aligns the local waveform in time. (shift_by=3, transition_window=13) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2691, 2812)"
},
"GutenTAG_152": {
"image": "VisAnomBench_plot_test/test_263.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=2.15). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.07) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3152, 3262)"
},
"GutenTAG_153": {
"image": "VisAnomBench_plot_test/test_264.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.05, amplitude=2.72). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=9) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3438, 3567)"
},
"GutenTAG_154": {
"image": "VisAnomBench_plot_test/test_265.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.36, amplitude=1.39). Anomalies: A variance change that adjusts the noise level. (variance=0.37) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2841, 2994)"
},
"GutenTAG_155": {
"image": "VisAnomBench_plot_test/test_266.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1232, 1390)"
},
"GutenTAG_156": {
"image": "VisAnomBench_plot_test/test_267.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=5). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.38) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2065, 2241)"
},
"GutenTAG_157": {
"image": "VisAnomBench_plot_test/test_268.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=1.54). Anomalies: A variance change that adjusts the noise level. (variance=0.63) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2743, 2930)"
},
"GutenTAG_158": {
"image": "VisAnomBench_plot_test/test_270.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=68, amplitude=0.72, variance-pattern-length=0.17, variance-amplitude=1.12). Anomalies: A variance change that adjusts the noise level. (variance=0.26) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2968, 3125)"
},
"GutenTAG_159": {
"image": "VisAnomBench_plot_test/test_271.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=3). Anomalies: A variance change that adjusts the noise level. (variance=0.95) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2618, 2792)"
},
"GutenTAG_160": {
"image": "VisAnomBench_plot_test/test_272.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.38) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1930, 2066)"
},
"GutenTAG_161": {
"image": "VisAnomBench_plot_test/test_273.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=4.48). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.35) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2121, 2303)"
},
"GutenTAG_162": {
"image": "VisAnomBench_plot_test/test_275.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=40, random-seed=83). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1482, 1544)"
},
"GutenTAG_163": {
"image": "VisAnomBench_plot_test/test_277.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.46). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(517, 632)"
},
"GutenTAG_164": {
"image": "VisAnomBench_plot_test/test_278.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=50, random-seed=51). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1727, 1912)"
},
"GutenTAG_165": {
"image": "VisAnomBench_plot_test/test_279.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.45). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1807, 1856)"
},
"GutenTAG_166": {
"image": "VisAnomBench_plot_test/test_28.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=79, amplitude=1.05, variance-pattern-length=0.16, variance-amplitude=1.05). Anomalies: A variance change that adjusts the noise level. (variance=0.9) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3890, 3998)"
},
"GutenTAG_167": {
"image": "VisAnomBench_plot_test/test_280.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.15). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.2) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4092, 4196)"
},
"GutenTAG_168": {
"image": "VisAnomBench_plot_test/test_281.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=41, random-seed=11). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1078, 1271)"
},
"GutenTAG_169": {
"image": "VisAnomBench_plot_test/test_282.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.83). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(700, 700)"
},
"GutenTAG_170": {
"image": "VisAnomBench_plot_test/test_283.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.85, amplitude=1.05). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.47) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3482, 3621)"
},
"GutenTAG_171": {
"image": "VisAnomBench_plot_test/test_284.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=2). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3886, 3981)"
},
"GutenTAG_172": {
"image": "VisAnomBench_plot_test/test_286.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.28). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1791, 1927)"
},
"GutenTAG_173": {
"image": "VisAnomBench_plot_test/test_287.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=86, amplitude=0.47, variance-pattern-length=0.17, variance-amplitude=1.84). Anomalies: A variance change that adjusts the noise level. (variance=0.36) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(624, 702)"
},
"GutenTAG_174": {
"image": "VisAnomBench_plot_test/test_288.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=9) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1681, 1812)"
},
"GutenTAG_175": {
"image": "VisAnomBench_plot_test/test_289.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.52, amplitude=1.95). Anomalies: A variance change that adjusts the noise level. (variance=0.5) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(957, 1015)"
},
"GutenTAG_176": {
"image": "VisAnomBench_plot_test/test_29.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.16). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=8) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2142, 2292)"
},
"GutenTAG_177": {
"image": "VisAnomBench_plot_test/test_290.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.87). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=16) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3313, 3454)"
},
"GutenTAG_178": {
"image": "VisAnomBench_plot_test/test_291.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: An extremum that forces a local or global minimum/maximum. (min=False, local=True) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1986, 1986)"
},
"GutenTAG_179": {
"image": "VisAnomBench_plot_test/test_292.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.66). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=23) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2160, 2293)"
},
"GutenTAG_180": {
"image": "VisAnomBench_plot_test/test_293.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.58, amplitude=2.8). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3306, 3407)"
},
"GutenTAG_181": {
"image": "VisAnomBench_plot_test/test_294.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=62, amplitude=0.71, variance-pattern-length=0.1, variance-amplitude=1.1). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.24) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4041, 4222)"
},
"GutenTAG_182": {
"image": "VisAnomBench_plot_test/test_295.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=64, amplitude=1.14, variance-pattern-length=0.19, variance-amplitude=1.25). Anomalies: An extremum that forces a local or global minimum/maximum. (min=True, local=False) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2673, 2673)"
},
"GutenTAG_183": {
"image": "VisAnomBench_plot_test/test_297.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.55). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.18) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2972, 3072)"
},
"GutenTAG_184": {
"image": "VisAnomBench_plot_test/test_298.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.51, amplitude=2.22). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=8) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1324, 1384)"
},
"GutenTAG_185": {
"image": "VisAnomBench_plot_test/test_299.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A variance change that adjusts the noise level. (variance=0.84) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(811, 906)"
},
"GutenTAG_186": {
"image": "VisAnomBench_plot_test/test_3.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=5). Anomalies: A variance change that adjusts the noise level. (variance=0.33) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1014, 1094)"
},
"GutenTAG_187": {
"image": "VisAnomBench_plot_test/test_30.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=46, random-seed=12). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2280, 2409)"
},
"GutenTAG_188": {
"image": "VisAnomBench_plot_test/test_32.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=2.36). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3716, 3868)"
},
"GutenTAG_189": {
"image": "VisAnomBench_plot_test/test_33.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.47). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.27) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3215, 3285)"
},
"GutenTAG_190": {
"image": "VisAnomBench_plot_test/test_34.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.6). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.18) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(865, 925)"
},
"GutenTAG_191": {
"image": "VisAnomBench_plot_test/test_35.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=4.06). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.24) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4300, 4459)"
},
"GutenTAG_192": {
"image": "VisAnomBench_plot_test/test_39.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=50, random-seed=29). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2213, 2316)"
},
"GutenTAG_193": {
"image": "VisAnomBench_plot_test/test_4.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=1.92). Anomalies: A variance change that adjusts the noise level. (variance=0.28) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3537, 3667)"
},
"GutenTAG_194": {
"image": "VisAnomBench_plot_test/test_40.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=42, random-seed=10). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3435, 3574)"
},
"GutenTAG_195": {
"image": "VisAnomBench_plot_test/test_41.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.78, amplitude=1.7). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.47) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2269, 2396)"
},
"GutenTAG_196": {
"image": "VisAnomBench_plot_test/test_42.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=50, random-seed=50). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(779, 851)"
},
"GutenTAG_197": {
"image": "VisAnomBench_plot_test/test_44.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.02, amplitude=2.36). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.15) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(753, 873)"
},
"GutenTAG_198": {
"image": "VisAnomBench_plot_test/test_45.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.7, amplitude=1.4). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=11) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3869, 4009)"
},
"GutenTAG_199": {
"image": "VisAnomBench_plot_test/test_46.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.86). Anomalies: A variance change that adjusts the noise level. (variance=0.68) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2370, 2471)"
},
"GutenTAG_200": {
"image": "VisAnomBench_plot_test/test_47.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=71, amplitude=1.35, variance-pattern-length=0.15, variance-amplitude=1.02). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=15) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3441, 3505)"
},
"GutenTAG_201": {
"image": "VisAnomBench_plot_test/test_48.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.1, amplitude=1.23). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.52) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2242, 2299)"
},
"GutenTAG_202": {
"image": "VisAnomBench_plot_test/test_50.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=50, random-seed=88). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4217, 4289)"
},
"GutenTAG_203": {
"image": "VisAnomBench_plot_test/test_51.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=50, amplitude=1.45, variance-pattern-length=0.16, variance-amplitude=1.04). Anomalies: A variance change that adjusts the noise level. (variance=0.3) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(831, 898)"
},
"GutenTAG_204": {
"image": "VisAnomBench_plot_test/test_53.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=37, random-seed=39). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1063, 1188)"
},
"GutenTAG_205": {
"image": "VisAnomBench_plot_test/test_54.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.84, amplitude=1.45). Anomalies: A variance change that adjusts the noise level. (variance=0.98) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2087, 2165)"
},
"GutenTAG_206": {
"image": "VisAnomBench_plot_test/test_55.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.02, amplitude=1.4). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1075, 1229)"
},
"GutenTAG_207": {
"image": "VisAnomBench_plot_test/test_57.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4272, 4334)"
},
"GutenTAG_208": {
"image": "VisAnomBench_plot_test/test_58.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.4, amplitude=1.12). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.27) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2798, 2888)"
},
"GutenTAG_209": {
"image": "VisAnomBench_plot_test/test_6.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.89, amplitude=1.15). Anomalies: A variance change that adjusts the noise level. (variance=0.28) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2996, 3146)"
},
"GutenTAG_210": {
"image": "VisAnomBench_plot_test/test_61.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=32, random-seed=9). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2779, 2955)"
},
"GutenTAG_211": {
"image": "VisAnomBench_plot_test/test_62.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=1.68). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.37) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1687, 1768)"
},
"GutenTAG_212": {
"image": "VisAnomBench_plot_test/test_63.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.87). Anomalies: A variance change that adjusts the noise level. (variance=0.73) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4195, 4299)"
},
"GutenTAG_213": {
"image": "VisAnomBench_plot_test/test_64.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=2). Anomalies: An extremum that forces a local or global minimum/maximum. (min=False, local=True) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2362, 2362)"
},
"GutenTAG_214": {
"image": "VisAnomBench_plot_test/test_65.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.91, amplitude=1.05). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.26) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2140, 2328)"
},
"GutenTAG_215": {
"image": "VisAnomBench_plot_test/test_66.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.48, amplitude=1.18). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=15) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2453, 2634)"
},
"GutenTAG_216": {
"image": "VisAnomBench_plot_test/test_67.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.94, amplitude=1.31). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=4) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2711, 2809)"
},
"GutenTAG_217": {
"image": "VisAnomBench_plot_test/test_69.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.07, amplitude=1.36). Anomalies: An extremum that forces a local or global minimum/maximum. (min=False, local=True) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2412, 2412)"
},
"GutenTAG_218": {
"image": "VisAnomBench_plot_test/test_7.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=10). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3828, 3954)"
},
"GutenTAG_219": {
"image": "VisAnomBench_plot_test/test_70.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=41, random-seed=38). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1613, 1690)"
},
"GutenTAG_220": {
"image": "VisAnomBench_plot_test/test_71.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.17, amplitude=1.48). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=10) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3606, 3736)"
},
"GutenTAG_221": {
"image": "VisAnomBench_plot_test/test_73.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.77, amplitude=2.37). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.47) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3070, 3231)"
},
"GutenTAG_222": {
"image": "VisAnomBench_plot_test/test_74.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=1.98). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.4) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1810, 2004)"
},
"GutenTAG_223": {
"image": "VisAnomBench_plot_test/test_75.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=49, random-seed=96). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4012, 4086)"
},
"GutenTAG_224": {
"image": "VisAnomBench_plot_test/test_76.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.21). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.45) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1720, 1887)"
},
"GutenTAG_225": {
"image": "VisAnomBench_plot_test/test_78.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=1.91). Anomalies: A frequency change that alters the pace of oscillation. (frequency_factor=2.42) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3888, 4082)"
},
"GutenTAG_226": {
"image": "VisAnomBench_plot_test/test_79.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=3). Anomalies: A variance change that adjusts the noise level. (variance=1) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1914, 2109)"
},
"GutenTAG_227": {
"image": "VisAnomBench_plot_test/test_8.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.33, amplitude=2.12). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.16) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(521, 704)"
},
"GutenTAG_228": {
"image": "VisAnomBench_plot_test/test_80.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.08). Anomalies: A variance change that adjusts the noise level. (variance=0.28) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3334, 3475)"
},
"GutenTAG_229": {
"image": "VisAnomBench_plot_test/test_81.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=3.57). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=12) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3210, 3378)"
},
"GutenTAG_230": {
"image": "VisAnomBench_plot_test/test_82.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=1.8). Anomalies: A variance change that adjusts the noise level. (variance=0.69) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1994, 2094)"
},
"GutenTAG_231": {
"image": "VisAnomBench_plot_test/test_83.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=5). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=9) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1389, 1528)"
},
"GutenTAG_232": {
"image": "VisAnomBench_plot_test/test_84.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: An ECG-like waveform resembling cardiac electrical activity. (frequency=1.56). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=1.58) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3002, 3185)"
},
"GutenTAG_233": {
"image": "VisAnomBench_plot_test/test_85.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=2). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.29) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3811, 3923)"
},
"GutenTAG_234": {
"image": "VisAnomBench_plot_test/test_86.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.18). Anomalies: A variance change that adjusts the noise level. (variance=0.92) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1140, 1251)"
},
"GutenTAG_235": {
"image": "VisAnomBench_plot_test/test_87.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=90, amplitude=1.08, variance-pattern-length=0.5, variance-amplitude=1.86). Anomalies: A trend change that adds a slowly varying component. (oscillation={kind: sine}) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3621, 3758)"
},
"GutenTAG_236": {
"image": "VisAnomBench_plot_test/test_88.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=60, amplitude=0.99, variance-pattern-length=0.12, variance-amplitude=1.28). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.06) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1851, 1922)"
},
"GutenTAG_237": {
"image": "VisAnomBench_plot_test/test_89.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A sawtooth wave with a linear ramp and a sharp drop or rise. (frequency=2.57, amplitude=1.78). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=3) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2953, 3093)"
},
"GutenTAG_238": {
"image": "VisAnomBench_plot_test/test_90.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=40, random-seed=51). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2626, 2700)"
},
"GutenTAG_239": {
"image": "VisAnomBench_plot_test/test_91.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A Dirichlet-kernel-based periodic waveform with adjustable complexity. (frequency=2.23). Anomalies: A variance change that adjusts the noise level. (variance=0.66) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3432, 3506)"
},
"GutenTAG_240": {
"image": "VisAnomBench_plot_test/test_93.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=62, amplitude=1.16, variance-pattern-length=0.15, variance-amplitude=1.24). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=7) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(4452, 4571)"
},
"GutenTAG_241": {
"image": "VisAnomBench_plot_test/test_94.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A process that jumps between distinct operating modes at random times. (frequency=49, random-seed=95). Anomalies: A mode-correlation change that alters the relationship between channels. on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1356, 1538)"
},
"GutenTAG_242": {
"image": "VisAnomBench_plot_test/test_95.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=81, amplitude=0.35, variance-pattern-length=0.36, variance-amplitude=1.64). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.36) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2740, 2900)"
},
"GutenTAG_243": {
"image": "VisAnomBench_plot_test/test_96.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: A mean shift that offsets the signal on the vertical axis. (offset=2.8) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(1327, 1492)"
},
"GutenTAG_244": {
"image": "VisAnomBench_plot_test/test_97.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A cylinder–bell–funnel pattern with segments switching between flat, peaked, and tapered shapes. (avg-pattern-length=94, amplitude=0.4, variance-pattern-length=0.42, variance-amplitude=1.88). Anomalies: A platform (plateau) that holds the signal at a constant level. (value=14) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2247, 2367)"
},
"GutenTAG_245": {
"image": "VisAnomBench_plot_test/test_98.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=7). Anomalies: A pattern change that replaces the local waveform with an alternative pattern. (sinusoid_k=11) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3108, 3288)"
},
"GutenTAG_246": {
"image": "VisAnomBench_plot_test/test_99.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nBase oscillations: A maximum-length sequence (pseudo-random binary pattern) often used for system identification. (complexity=4). Anomalies: An amplitude change that scales the oscillation. (amplitude_factor=2.07) on dimension 0.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(3174, 3335)"
},
"KPI_0": {
"image": "VisAnomBench_plot_test/server_res_eth1out_curve_61_seg37_13299-13498.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nOutbound network usage on interface eth1 for one server/series variant, capturing traffic bursts or drops.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2016-11-09 03:35:00, 2016-11-09 03:45:00)"
},
"KPI_1": {
"image": "VisAnomBench_plot_test/server_res_eth1out_curve_61_seg16_2815-3014.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nOutbound network usage on interface eth1 for one server/series variant, capturing traffic bursts or drops.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2016-10-03 20:50:00, 2016-10-03 21:00:00)"
},
"KPI_2": {
"image": "VisAnomBench_plot_test/server_res_eth1out_curve_61_seg33_12241-12468.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nOutbound network usage on interface eth1 for one server/series variant, capturing traffic bursts or drops.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2016-11-05 14:55:00, 2016-11-05 15:10:00)"
},
"KPI_3": {
"image": "VisAnomBench_plot_test/g_seg79_137253-139282.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nAn anonymized business KPI named 'g' i.e., a single aggregated metric (such as users, page views, or orders) tracked over time for anomaly detection\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2017-01-18 17:06:00, 2017-01-18 17:28:00), (2017-01-18 17:42:00, 2017-01-18 19:16:00), (2017-01-18 19:39:00, 2017-01-18 20:05:00), (2017-01-19 10:52:00, 2017-01-19 11:17:00)"
},
"KPI_4": {
"image": "VisAnomBench_plot_test/g_seg13_43591-44206.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nAn anonymized business KPI named 'g' i.e., a single aggregated metric (such as users, page views, or orders) tracked over time for anomaly detection\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1) True/False(start_1, end_1), (start_2, end_2), (start_3, end_3)\n(Line 2) Step 1: ... Step 2: ... Step 3: ...",
"resp": "",
"anomalies": "(2016-11-14 03:09:00, 2016-11-14 03:34:00)"
},
"KPI_5": {
"image": "VisAnomBench_plot_test/server_res_eth1out_curve_6_seg01_2182-2381.png",
"query": "You are given a **time-series plot image** (not raw values) and a short context:\nOutbound network usage on interface eth1 for one server/series variant, capturing traffic bursts or drops.\n\nYour task is to (i) decide whether anomalies are present, (ii) localize them as inclusive index/timestamp intervals\nexactly as shown on the plot axes, and (iii) provide concise, step-by-step reasoning grounded in what is visible. If citing numbers, estimate ONLY from axis ticks/labels.\n\nPlease follow these steps:\n1) Give a short image description relevant to anomaly detection (axes type; visible trend/seasonality; spikes/drops/level-shifts).\n2) Provide a brief rationale: what looks normal (baseline/seasonality/variance) vs. what visibly deviates.\n3) Produce a simple **step-by-step** reasoning (3–4 numbered steps). Each step must be specific and non-redundant.\n4) Output the final decision in the **STRICT schema** below.\n\nSTRICT schema and rules:\n• Intervals are **inclusive** of endpoints.\n• Use x-axis values **as shown** (timestamps or integer indices). Do not invent precision.\n• Merge visually contiguous anomalous points into a single interval; sort ascending; no overlaps.\n• When anomalies exist: wrap **each** (start, end) pair in its own ..., separated by \", \".\n• When no anomalies: output only the False tag (no tags at all).\n\n\nSections to include before the two final lines (use these exact headings):\n\n### Image Description:\n(1–3 sentences about the plot relevant to anomalies.)\n\n### Rationales:\n- (Concise bullet 1: expected behavior from context.)\n- (Concise bullet 2: visible deviations with axis-aware estimates when needed.)\n- (Optional concise bullet 3.)\n\n### Let's think step by step.\nStep 1: (Identify axes scale and baseline; note any seasonality.)\nStep 2: (Locate candidate abnormal regions and justify deviations.)\nStep 3: (Consolidate adjacent points into inclusive intervals; map to x-axis values; ensure sorted, non-overlapping.)\n\n====================\nFINAL PARSEABLE OUTPUT (produce exactly these two lines, then stop)\n====================\n(Line 1)