Add GLM-5.3 Flash baseline and DeepAgents archives

README.md

@@ -11,8 +11,8 @@ short_description: Single-pass agents and DeepAgents on econometric replication
 
 # InferenceNet Agent & Harness Leaderboard
 
-The September 2026 research leaderboard displays **six models, twelve groups,
-and 12,000 archived task records**. Switch between a paired comparison, a
+The September 2026 research leaderboard displays **seven models, fourteen groups,
+and 14,000 archived task records**. Switch between a paired comparison, a
 single-pass Agent ranking, and an Agent + Harness ranking. Every group uses
 the same 1,000 Selected_1000 task IDs.
 
@@ -21,7 +21,7 @@ the same 1,000 Selected_1000 task IDs.
   calls, followed by final execution.
 
 Models: GPT-5.6 Sol, Claude Opus 4.8, Kimi K3, Gemini 3.1 Pro Preview,
-Qwen3.7-Max, and DeepSeek V4 Pro. GPT-5.5 is outside this edition.
+Qwen3.7-Max, DeepSeek V4 Pro, and GLM-5.3 Flash. GPT-5.5 is outside this edition.
 
 ## Reading the results
 
@@ -43,14 +43,14 @@ See each run's configuration before making cross-model claims.
 
 Kimi retains 5 baseline and 4 harness task-status unknowns. Qwen3.7-Max harness
 retains 6 task-status unknowns and 1 invalid-evidence record (task 453), which is
-not sealed. Historical Sol/Opus interruption counts are shown as originally
+not sealed. GLM-5.3 Flash DeepAgents retains 5 audited no-final aborts. Historical Sol/Opus interruption counts are shown as originally
 exported. An archive of 1,000 task IDs does not imply 1,000 definitive outcomes.
 
 ## Data and reproducibility
 
 - [`leaderboard-data.json`](./leaderboard-data.json): displayed data, metric
   profiles, counts, evidence status and source-file SHA-256 hashes.
-- [`leaderboard.csv`](./leaderboard.csv): 60 displayed metric rows; six models ×
+- [`leaderboard.csv`](./leaderboard.csv): 70 displayed metric rows; seven models ×
   two arms × five metrics. One metric profile is explicit on every row.
 - [`results/`](./results): frozen per-task archives, configurations and summaries.
 - [`legacy.html`](./legacy.html): original leaderboard with a historical banner.
@@ -74,7 +74,7 @@ python build_leaderboard.py --source . --output . \
   --source-revision 731111974cf4d1a3c7a4f848a899f0b0708dd12b
 ```
 
-The builder verifies all task populations and 56 metric counts against archived
+The builder verifies all task populations and 66 metric counts against archived
 flags. The four Sol/Opus local full-replication summaries have no per-task local
 flags in the archive and are preserved from their accepted summaries. This is a
 display refresh, not a fresh inference run, native artifact audit, or rescore.

build_leaderboard.py

@@ -16,6 +16,7 @@ NAMES = {
     "gemini-3.1-pro-preview": "Gemini 3.1 Pro Preview",
     "qwen3.7-max": "Qwen3.7-Max",
     "deepseek-v4-pro": "DeepSeek V4 Pro",
+    "glm-5.3-flash": "GLM-5.3 Flash",
 }
 METRICS = ("compilation_success", "partial_replication", "coefficient_direction", "significance_level")
 
@@ -25,7 +26,7 @@ def build(source, output, revision):
     index = json.loads((source / "results/index.json").read_text())
     entries = index["entries"]
     assert {(e["model"], e["arm"]) for e in entries} == {(m, a) for m in NAMES for a in ("baseline", "deepagents")}
-    assert len(entries) == 12
+    assert len(entries) == 14
     models = {m: {"id": m, "name": name, "arms": {}} for m, name in NAMES.items()}
     population = None
     dataset_revision = None
@@ -86,7 +87,7 @@ def build(source, output, revision):
         "published_date": "2026-09-17", "source_repository": "CamoAiLab/InferenceNet-Leaderboard",
         "source_revision": revision, "dataset_revision": dataset_revision,
         "task_list": "Selected_1000/1000_new.csv", "tasks_per_group": 1000,
-        "groups": 12, "records": 12000, "official_parity_verified": False,
+        "groups": 14, "records": 14000, "official_parity_verified": False,
         "models": list(models.values()), "source_sha256": source_files,
     }
     output.mkdir(parents=True, exist_ok=True)
@@ -98,8 +99,8 @@ def build(source, output, revision):
             for arm_id, arm in model["arms"].items():
                 for key, metric in arm["metrics"].items():
                     writer.writerow([model["id"], arm_id, arm["harness"], metric["profile"], key, metric["count"], 1000, metric["unknown"], metric["score"], False, revision])
-    print("Verified: 6 models, 12 arms, 12,000 unique-per-arm records; 60 displayed metric summaries.")
-    print("56 metric summaries checked against task flags; 4 historical local summaries preserved as accepted.")
+    print("Verified: 7 models, 14 arms, 14,000 unique-per-arm records; 70 displayed metric summaries.")
+    print("66 metric summaries checked against task flags; 4 historical local summaries preserved as accepted.")
 
 
 if __name__ == "__main__":

index.html

@@ -3,7 +3,7 @@
 <head>
   <meta charset="utf-8">
   <meta name="viewport" content="width=device-width, initial-scale=1">
-  <meta name="description" content="InferenceNet research leaderboard: six models compared with single-pass generation and a DeepAgents harness on 1,000 econometric replication tasks.">
+  <meta name="description" content="InferenceNet research leaderboard: seven models compared with single-pass generation and a DeepAgents harness on 1,000 econometric replication tasks.">
   <meta name="color-scheme" content="dark">
   <title>InferenceNet · Agent & Harness Leaderboard</title>
   <link rel="icon" href="data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 32 32'%3E%3Crect width='32' height='32' rx='6' fill='%230c1219'/%3E%3Ctext x='5' y='23' font-family='sans-serif' font-size='21' fill='%2378dbc0'%3EiN%3C/text%3E%3C/svg%3E">

leaderboard-data.json

@@ -3,12 +3,12 @@
   "kind": "research_leaderboard",
   "published_date": "2026-09-17",
   "source_repository": "CamoAiLab/InferenceNet-Leaderboard",
-  "source_revision": "731111974cf4d1a3c7a4f848a899f0b0708dd12b",
+  "source_revision": "215a4f4111fec35a3e7fae1bf908a5bfa4907021",
   "dataset_revision": "59f9512a38e594528807744214a60ee00367434e",
   "task_list": "Selected_1000/1000_new.csv",
   "tasks_per_group": 1000,
-  "groups": 12,
-  "records": 12000,
+  "groups": 14,
+  "records": 14000,
   "official_parity_verified": false,
   "models": [
     {
@@ -742,6 +742,128 @@
           "evidence_class": "amended_comparison"
         }
       }
+    },
+    {
+      "id": "glm-5.3-flash",
+      "name": "GLM-5.3 Flash",
+      "arms": {
+        "baseline": {
+          "path": "results/glm-5.3-flash/baseline",
+          "metrics": {
+            "perfect": {
+              "count": 198,
+              "denominator": 1000,
+              "unknown": 469,
+              "failure": 333,
+              "score": 19.8,
+              "profile": "local-paper-v1",
+              "verification": "accepted_summary"
+            },
+            "compilation_success": {
+              "count": 542,
+              "denominator": 1000,
+              "unknown": 0,
+              "failure": 458,
+              "score": 54.2,
+              "profile": "hf-leaderboard-v1",
+              "verification": "archived_task_flags"
+            },
+            "partial_replication": {
+              "count": 331,
+              "denominator": 1000,
+              "unknown": 467,
+              "failure": 202,
+              "score": 33.1,
+              "profile": "hf-leaderboard-v1",
+              "verification": "archived_task_flags"
+            },
+            "coefficient_direction": {
+              "count": 486,
+              "denominator": 1000,
+              "unknown": 467,
+              "failure": 47,
+              "score": 48.6,
+              "profile": "hf-leaderboard-v1",
+              "verification": "archived_task_flags"
+            },
+            "significance_level": {
+              "count": 416,
+              "denominator": 1000,
+              "unknown": 467,
+              "failure": 117,
+              "score": 41.6,
+              "profile": "hf-leaderboard-v1",
+              "verification": "archived_task_flags"
+            }
+          },
+          "generated_at": "2026-09-16T16:32:25.336567+00:00",
+          "complete": true,
+          "harness": "none",
+          "model_call_cap": 1,
+          "task_unknown_count": null,
+          "invalid_evidence_count": 0,
+          "interruption_count": 0,
+          "evidence_class": "single_run_baseline"
+        },
+        "deepagents": {
+          "path": "results/glm-5.3-flash/deepagents",
+          "metrics": {
+            "perfect": {
+              "count": 324,
+              "denominator": 1000,
+              "unknown": 362,
+              "failure": 314,
+              "score": 32.4,
+              "profile": "local-paper-v1",
+              "verification": "accepted_summary"
+            },
+            "compilation_success": {
+              "count": 641,
+              "denominator": 1000,
+              "unknown": 5,
+              "failure": 354,
+              "score": 64.1,
+              "profile": "hf-leaderboard-v1",
+              "verification": "archived_task_flags"
+            },
+            "partial_replication": {
+              "count": 496,
+              "denominator": 1000,
+              "unknown": 360,
+              "failure": 144,
+              "score": 49.6,
+              "profile": "hf-leaderboard-v1",
+              "verification": "archived_task_flags"
+            },
+            "coefficient_direction": {
+              "count": 617,
+              "denominator": 1000,
+              "unknown": 360,
+              "failure": 23,
+              "score": 61.7,
+              "profile": "hf-leaderboard-v1",
+              "verification": "archived_task_flags"
+            },
+            "significance_level": {
+              "count": 549,
+              "denominator": 1000,
+              "unknown": 361,
+              "failure": 90,
+              "score": 54.9,
+              "profile": "hf-leaderboard-v1",
+              "verification": "archived_task_flags"
+            }
+          },
+          "generated_at": "2026-09-19T01:55:37.161043+00:00",
+          "complete": true,
+          "harness": "DeepAgents 0.7.13",
+          "model_call_cap": 6,
+          "task_unknown_count": 5,
+          "invalid_evidence_count": 0,
+          "interruption_count": 5,
+          "evidence_class": "amended_comparison"
+        }
+      }
     }
   ],
   "source_sha256": {
@@ -780,6 +902,12 @@
     "results/deepseek-v4-pro/baseline/config.json": "4cef524f37a325029ea4ad325b163f72f9cf725f507f20cd76424d06e71e24ad",
     "results/deepseek-v4-pro/deepagents/summary.json": "231e4264b0403e12788136d01af465f86fe9ba3c3ab992510925a7ef4c2a40e9",
     "results/deepseek-v4-pro/deepagents/results.jsonl": "a006c8b0b80064295faeb3aa7395a2408f0208677853448816b361bf67e39bb6",
-    "results/deepseek-v4-pro/deepagents/config.json": "400c28e4008d5241bf50bc71c2674861846d6b6d2e7da21f2a7602a60ecc0ae9"
+    "results/deepseek-v4-pro/deepagents/config.json": "400c28e4008d5241bf50bc71c2674861846d6b6d2e7da21f2a7602a60ecc0ae9",
+    "results/glm-5.3-flash/baseline/summary.json": "72446478696658f473b7dd51d98bf615d639e7aca6011e6badb65d342dbd388a",
+    "results/glm-5.3-flash/baseline/results.jsonl": "536789e03253945e45867bac5a73a1d648dfbcec11c69c682cf252bd5f829968",
+    "results/glm-5.3-flash/baseline/config.json": "5d031f0e21410a7ee71cc74738f33b71eda5b9a2969d9222a0494a0bcaca12dd",
+    "results/glm-5.3-flash/deepagents/summary.json": "06a6465eb14a905057a0b5a673504a2ecd7cd511bf7a512353c0026ccf76c90b",
+    "results/glm-5.3-flash/deepagents/results.jsonl": "10fe6b91145e5c000945f60b9f0ad56eec097c10ac30048cbda9642dd870e687",
+    "results/glm-5.3-flash/deepagents/config.json": "aa949edd452b01a2b559c7e36d1feba4568a84daf061746015ddf553f0413e65"
   }
 }

leaderboard.csv

@@ -1,61 +1,71 @@
 model,arm,harness,metric_profile,metric,successes,denominator,unknown,score_percent,official_parity_verified,source_revision
-gpt-5.6-sol,baseline,none,local-paper-v1,perfect,338,1000,445,33.8,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,baseline,none,hf-leaderboard-v1,compilation_success,557,1000,3,55.7,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,baseline,none,hf-leaderboard-v1,partial_replication,430,1000,444,43.0,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,baseline,none,hf-leaderboard-v1,coefficient_direction,537,1000,444,53.7,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,baseline,none,hf-leaderboard-v1,significance_level,485,1000,444,48.5,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,deepagents,DeepAgents,local-paper-v1,perfect,518,1000,95,51.8,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,deepagents,DeepAgents,hf-leaderboard-v1,compilation_success,910,1000,10,91.0,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,deepagents,DeepAgents,hf-leaderboard-v1,partial_replication,699,1000,92,69.9,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,deepagents,DeepAgents,hf-leaderboard-v1,coefficient_direction,879,1000,92,87.9,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gpt-5.6-sol,deepagents,DeepAgents,hf-leaderboard-v1,significance_level,787,1000,93,78.7,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,baseline,none,local-paper-v1,perfect,184,1000,538,18.4,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,baseline,none,hf-leaderboard-v1,compilation_success,467,1000,0,46.7,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,baseline,none,hf-leaderboard-v1,partial_replication,305,1000,537,30.5,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,baseline,none,hf-leaderboard-v1,coefficient_direction,437,1000,537,43.7,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,baseline,none,hf-leaderboard-v1,significance_level,370,1000,537,37.0,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,deepagents,DeepAgents,local-paper-v1,perfect,392,1000,174,39.2,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,deepagents,DeepAgents,hf-leaderboard-v1,compilation_success,834,1000,4,83.4,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,deepagents,DeepAgents,hf-leaderboard-v1,partial_replication,621,1000,170,62.1,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,deepagents,DeepAgents,hf-leaderboard-v1,coefficient_direction,793,1000,170,79.3,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-claude-opus-4-8,deepagents,DeepAgents,hf-leaderboard-v1,significance_level,696,1000,172,69.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,baseline,none,local-paper-v1,perfect,230,1000,481,23.0,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,baseline,none,hf-leaderboard-v1,compilation_success,526,1000,5,52.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,baseline,none,hf-leaderboard-v1,partial_replication,366,1000,481,36.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,baseline,none,hf-leaderboard-v1,coefficient_direction,494,1000,481,49.4,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,baseline,none,hf-leaderboard-v1,significance_level,431,1000,481,43.1,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,390,1000,159,39.0,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,867,1000,4,86.7,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,635,1000,156,63.5,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,coefficient_direction,804,1000,156,80.4,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-kimi-k3,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,709,1000,157,70.9,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,baseline,none,local-paper-v1,perfect,246,1000,462,24.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,baseline,none,hf-leaderboard-v1,compilation_success,543,1000,0,54.3,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,baseline,none,hf-leaderboard-v1,partial_replication,366,1000,460,36.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,baseline,none,hf-leaderboard-v1,coefficient_direction,506,1000,460,50.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,baseline,none,hf-leaderboard-v1,significance_level,437,1000,460,43.7,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,391,1000,155,39.1,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,850,1000,0,85.0,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,632,1000,153,63.2,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,coefficient_direction,811,1000,153,81.1,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,718,1000,154,71.8,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,baseline,none,local-paper-v1,perfect,198,1000,519,19.8,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,baseline,none,hf-leaderboard-v1,compilation_success,486,1000,0,48.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,baseline,none,hf-leaderboard-v1,partial_replication,315,1000,518,31.5,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,baseline,none,hf-leaderboard-v1,coefficient_direction,454,1000,518,45.4,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,baseline,none,hf-leaderboard-v1,significance_level,396,1000,518,39.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,248,1000,481,24.8,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,523,1000,7,52.3,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,379,1000,481,37.9,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,coefficient_direction,488,1000,481,48.8,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-qwen3.7-max,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,433,1000,481,43.3,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,baseline,none,local-paper-v1,perfect,129,1000,723,12.9,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,baseline,none,hf-leaderboard-v1,compilation_success,281,1000,0,28.1,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,baseline,none,hf-leaderboard-v1,partial_replication,195,1000,723,19.5,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,baseline,none,hf-leaderboard-v1,coefficient_direction,260,1000,723,26.0,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,baseline,none,hf-leaderboard-v1,significance_level,236,1000,723,23.6,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,183,1000,648,18.3,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,352,1000,0,35.2,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,270,1000,648,27.0,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,coefficient_direction,338,1000,648,33.8,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
-deepseek-v4-pro,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,308,1000,648,30.8,False,731111974cf4d1a3c7a4f848a899f0b0708dd12b
+gpt-5.6-sol,baseline,none,local-paper-v1,perfect,338,1000,445,33.8,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gpt-5.6-sol,baseline,none,hf-leaderboard-v1,compilation_success,557,1000,3,55.7,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gpt-5.6-sol,baseline,none,hf-leaderboard-v1,partial_replication,430,1000,444,43.0,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gpt-5.6-sol,baseline,none,hf-leaderboard-v1,coefficient_direction,537,1000,444,53.7,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gpt-5.6-sol,baseline,none,hf-leaderboard-v1,significance_level,485,1000,444,48.5,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
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+gpt-5.6-sol,deepagents,DeepAgents,hf-leaderboard-v1,significance_level,787,1000,93,78.7,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,baseline,none,local-paper-v1,perfect,184,1000,538,18.4,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,baseline,none,hf-leaderboard-v1,compilation_success,467,1000,0,46.7,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,baseline,none,hf-leaderboard-v1,partial_replication,305,1000,537,30.5,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,baseline,none,hf-leaderboard-v1,coefficient_direction,437,1000,537,43.7,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,baseline,none,hf-leaderboard-v1,significance_level,370,1000,537,37.0,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,deepagents,DeepAgents,local-paper-v1,perfect,392,1000,174,39.2,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,deepagents,DeepAgents,hf-leaderboard-v1,compilation_success,834,1000,4,83.4,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,deepagents,DeepAgents,hf-leaderboard-v1,partial_replication,621,1000,170,62.1,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,deepagents,DeepAgents,hf-leaderboard-v1,coefficient_direction,793,1000,170,79.3,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+claude-opus-4-8,deepagents,DeepAgents,hf-leaderboard-v1,significance_level,696,1000,172,69.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+kimi-k3,baseline,none,local-paper-v1,perfect,230,1000,481,23.0,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+kimi-k3,baseline,none,hf-leaderboard-v1,compilation_success,526,1000,5,52.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+kimi-k3,baseline,none,hf-leaderboard-v1,partial_replication,366,1000,481,36.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
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+kimi-k3,baseline,none,hf-leaderboard-v1,significance_level,431,1000,481,43.1,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+kimi-k3,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,390,1000,159,39.0,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+kimi-k3,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,867,1000,4,86.7,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+kimi-k3,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,635,1000,156,63.5,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
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+kimi-k3,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,709,1000,157,70.9,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gemini-3.1-pro-preview,baseline,none,local-paper-v1,perfect,246,1000,462,24.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gemini-3.1-pro-preview,baseline,none,hf-leaderboard-v1,compilation_success,543,1000,0,54.3,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
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+gemini-3.1-pro-preview,baseline,none,hf-leaderboard-v1,coefficient_direction,506,1000,460,50.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gemini-3.1-pro-preview,baseline,none,hf-leaderboard-v1,significance_level,437,1000,460,43.7,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,391,1000,155,39.1,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,850,1000,0,85.0,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,632,1000,153,63.2,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,coefficient_direction,811,1000,153,81.1,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+gemini-3.1-pro-preview,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,718,1000,154,71.8,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,baseline,none,local-paper-v1,perfect,198,1000,519,19.8,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,baseline,none,hf-leaderboard-v1,compilation_success,486,1000,0,48.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,baseline,none,hf-leaderboard-v1,partial_replication,315,1000,518,31.5,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,baseline,none,hf-leaderboard-v1,coefficient_direction,454,1000,518,45.4,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,baseline,none,hf-leaderboard-v1,significance_level,396,1000,518,39.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,248,1000,481,24.8,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,523,1000,7,52.3,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,379,1000,481,37.9,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,coefficient_direction,488,1000,481,48.8,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+qwen3.7-max,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,433,1000,481,43.3,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,baseline,none,local-paper-v1,perfect,129,1000,723,12.9,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,baseline,none,hf-leaderboard-v1,compilation_success,281,1000,0,28.1,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,baseline,none,hf-leaderboard-v1,partial_replication,195,1000,723,19.5,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,baseline,none,hf-leaderboard-v1,coefficient_direction,260,1000,723,26.0,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,baseline,none,hf-leaderboard-v1,significance_level,236,1000,723,23.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,183,1000,648,18.3,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,352,1000,0,35.2,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,270,1000,648,27.0,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,coefficient_direction,338,1000,648,33.8,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+deepseek-v4-pro,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,308,1000,648,30.8,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,baseline,none,local-paper-v1,perfect,198,1000,469,19.8,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,baseline,none,hf-leaderboard-v1,compilation_success,542,1000,0,54.2,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,baseline,none,hf-leaderboard-v1,partial_replication,331,1000,467,33.1,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,baseline,none,hf-leaderboard-v1,coefficient_direction,486,1000,467,48.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,baseline,none,hf-leaderboard-v1,significance_level,416,1000,467,41.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,deepagents,DeepAgents 0.7.13,local-paper-v1,perfect,324,1000,362,32.4,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,compilation_success,641,1000,5,64.1,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,partial_replication,496,1000,360,49.6,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,coefficient_direction,617,1000,360,61.7,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021
+glm-5.3-flash,deepagents,DeepAgents 0.7.13,hf-leaderboard-v1,significance_level,549,1000,361,54.9,False,215a4f4111fec35a3e7fae1bf908a5bfa4907021

leaderboard.js

@@ -79,7 +79,7 @@ function applyLanguage() {
 }
 
 function validate(candidate) {
-  if (candidate.schema_version !== 1 || candidate.models?.length !== 6 || candidate.groups !== 12 || candidate.records !== 12000 || candidate.tasks_per_group !== 1000 || candidate.official_parity_verified !== false || !/^[a-f0-9]{40}$/.test(candidate.source_revision)) throw new Error('Invalid leaderboard snapshot');
+  if (candidate.schema_version !== 1 || candidate.models?.length !== 7 || candidate.groups !== 14 || candidate.records !== 14000 || candidate.tasks_per_group !== 1000 || candidate.official_parity_verified !== false || !/^[a-f0-9]{40}$/.test(candidate.source_revision)) throw new Error('Invalid leaderboard snapshot');
   const ids = new Set();
   for (const model of candidate.models) {
     if (!/^[a-z0-9.-]+$/.test(model.id) || ids.has(model.id) || typeof model.name !== 'string') throw new Error('Invalid model');

results/README.md

@@ -10,6 +10,7 @@
 | Gemini 3.1 Pro Preview | [1,000 题](gemini-3.1-pro-preview/baseline/) | [1,000 题](gemini-3.1-pro-preview/deepagents/) |
 | Qwen3.7-Max | [1,000 题](qwen3.7-max/baseline/) | [1,000 题](qwen3.7-max/deepagents/) |
 | DeepSeek V4 Pro | [1,000 题](deepseek-v4-pro/baseline/) | [1,000 题](deepseek-v4-pro/deepagents/) |
+| GLM-5.3 Flash | [1,000 题](glm-5.3-flash/baseline/) | [1,000 题](glm-5.3-flash/deepagents/) |
 
 这里先保存完成的实验产物。页面仍只读取根目录 `/results.csv`，本次没有更新它或修改页面代码。等各模型结果齐备，再核对数据、评分和协议后统一整理正式榜单。
 
@@ -27,8 +28,13 @@ K3 保留两组 5 / 4 条任务 unknown，Gemini 无任务 unknown；每组固
 
 ## Qwen3.7-Max 与 DeepSeek V4 Pro
 
-新增四组、4,000 条记录后，存档覆盖六个模型、12 组、12,000 条记录。GPT-5.5 不在本次范围。
+新增四组、4,000 条记录后，存档覆盖七个模型、14 组、14,000 条记录。GPT-5.5 不在本次范围。
 
 Qwen3.7-Max 原生完整复现率为 19.8% / 24.8%，DeepSeek V4 Pro 为 12.9% / 18.3%；本地 HF 四维与原生五维分开保存。Qwen DeepAgents 保留 6 条任务 unknown 和 task 453 的 1 条 invalid evidence：999 条有效封存、1 条无效证据，共 1,000 个固定题号。无效证据不标为失败或有效封存，指标均为空；未重跑或删除。DeepSeek 双组没有任务 unknown 或 invalid。
 
 运行 `python3 -B results/verify_qwen_deepseek.py` 可核对新增四组逐题记录、配对表、36 项汇总和归档清单。这是已接受产物的离线导出验证，不代表重新进行原生审计或官方评分。网页根目录 results.csv 与页面代码由后续网页更新单独处理。
+
+
+## GLM-5.3 Flash
+
+新增单次生成与 DeepAgents 两组、2,000 条记录。DeepAgents 由多次 continuation 批次和一次显式 6 槽位审计合并而成：任务 218 重放已封存最终程序；任务 1、425、785、980、1019 裁定为 `aborted_no_final`。固定分母仍为 1,000，未重发模型请求。

results/glm-5.3-flash/baseline/README.md

@@ -0,0 +1,11 @@
+# glm-5.3-flash / baseline
+
+已完成的 1,000 题单次生成基线结果归档；不自动更新正式榜单。
+
+- `results.jsonl` / `results.csv`：逐题预测、执行/评分状态和四维指标；两种格式内容相同。
+- `summary.json`：固定 1,000 题分母、未知计数，以及分别保存的四维导出和旧诊断指标。
+- `config.json`：数据 revision、完整任务 ID 清单、生成/执行预算及来源哈希。
+
+模型为 `glm-5.3-flash`，使用 Chat Completions 非流式接口，每题一次生成尝试。失败、无预测与未知记录均保留，完成计数包含失败和未知，不能当作成功率。四维导出依据榜单页面文字解释，尚未验证与实际官方评分器完全一致；正式整理榜单时再统一核对。
+
+JSON null / CSV 空单元格表示未知或不可用，不表示 0。原始输入数据、隐藏参考答案、完整提示词/API 轨迹、凭据和本机路径未包含在这个归档中。

results/glm-5.3-flash/baseline/config.json

@@ -0,0 +1,1052 @@
+{
+  "schema_version": 1,
+  "model": "glm-5.3-flash",
+  "arm": "baseline",
+  "harness": "none",
+  "source_run_name": "glm53flash-full",
+  "archived_at": "2026-09-16T16:32:25.336567+00:00",
+  "dataset": {
+    "repo_id": "CamoAiLab/InferenceNet",
+    "revision": "59f9512a38e594528807744214a60ee00367434e",
+    "csv_sha256": "5338c8af548b35dab0e92a63b0acfebb0bc29c7901da4088851025f5709d6bb3",
+    "expected_tasks": 1000,
+    "task_ids": [
+      1,
+      2,
+      3,
+      4,
+      5,
+      6,
+      7,
+      8,
+      9,
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+  "generation": {
+    "provider": "chat-completions",
+    "model": "glm-5.3-flash",
+    "reasoning_effort": "none",
+    "max_output_tokens": 32768,
+    "timeout": 600,
+    "stream": false,
+    "prompt_profile": "legacy",
+    "schema_context_chars": 24000,
+    "api_base_env": "INFERENCENET_API_BASE",
+    "api_key_env": "INFERENCENET_API_KEY"
+  },
+  "execution": {
+    "timeout": 600,
+    "memory": "6g",
+    "cpus": 2.0,
+    "tmpfs": "256m",
+    "docker_context": "default",
+    "image": "inferencenet-eval:20260911"
+  },
+  "campaign": {
+    "workers": 2,
+    "execution_workers": 1,
+    "max_attempts": 1,
+    "stop_after_errors": 3
+  },
+  "source_repository": {
+    "url": "https://github.com/YTZSR/chatpilot_evaluator.git",
+    "commit": "92e56c82d1c91b1297d08dd331e449362eca30f8",
+    "branch": "dev-gyc-eval"
+  },
+  "protocol_sha256": "1bdb923bebc38ab4fb23319b88da171953500ad9d1172bac44e2b609c4019590",
+  "report_sha256": "e13e9b5d78b5c35957faa8aa75202f293a932da936cfbb79c1f63415fdc6473b",
+  "original_limits_for_this_arm": {
+    "model_calls": 1
+  }
+}

results/glm-5.3-flash/baseline/summary.json

@@ -0,0 +1,121 @@
+{
+  "schema_version": 1,
+  "kind": "inferencenet-offline-leaderboard-export",
+  "generated_at": "2026-09-16T16:32:25.336567+00:00",
+  "model": "glm-5.3-flash",
+  "metric_profile": "hf-leaderboard-v1",
+  "definitions": {
+    "compilation_success": "Trusted evidence of complete code execution; no prediction JSON gate.",
+    "partial_replication": "Coefficient relative error <= .05; no standard-error or p-value gate.",
+    "coefficient_direction": "Matching strictly positive or strictly negative coefficient signs.",
+    "significance_level": "Equal p-value category: p < .01, p < .05, p < .1, otherwise; no direction gate."
+  },
+  "assumptions": {
+    "official_rules": "Webpage interpretation only; official scorer parity is not verified.",
+    "compilation_evidence": "Caller supplies True, False, or None from verified execution evidence, including exit status, timeout, termination confirmation, and execution errors. Missing or uncertain evidence maps to None.",
+    "prediction_evidence": "Only execution_status=succeeded and scoring_status=scored rows are evaluated. The current caller requires a complete valid prediction triplet; invalid outputs are not recovered here.",
+    "coefficient_boundary": "5% includes equality. Numeric decimal representations are compared exactly without epsilon.",
+    "zero_reference_coefficient": "A zero reference coefficient is unknown for relative error and positive/negative direction; it does not block significance. A zero prediction against a nonzero reference is incorrect.",
+    "significance_categories": "Four categories use strict .01, .05, and .1 boundaries. Both p-values must be numeric and within [0, 1]. These boundaries and the absent direction gate are explicit local choices.",
+    "field_validation": "Each metric requires only its own finite numeric fields. Booleans, numeric strings, and inequalities are not coerced. Standard errors do not affect any of these three formulas.",
+    "denominator": "All planned tasks remain in every denominator. Confirmed compilation failures are False; missing or uncertain execution evidence is unknown. For the three result metrics, failed execution, missing predictions, unscored rows, and unsupported reference fields are unknown. Unknowns contribute no successes and are counted separately.",
+    "attempt_selection": "The caller supplies one row per task under its frozen attempt policy."
+  },
+  "definition_source": "https://huggingface.co/spaces/CamoAiLab/InferenceNet-Leaderboard/blob/bc367a8b8cabc9b06aac093406942b72cc5837a7/index.html#L616",
+  "official_parity_verified": false,
+  "evidence_class": "single_run_baseline",
+  "original_protocol_complete": true,
+  "complete": true,
+  "dataset_revision": "59f9512a38e594528807744214a60ee00367434e",
+  "protocol_sha256": "1bdb923bebc38ab4fb23319b88da171953500ad9d1172bac44e2b609c4019590",
+  "exporter_source_sha256": "92e56c82d1c91b1297d08dd331e449362eca30f8",
+  "arm": "baseline",
+  "included_in_displayed_leaderboard": false,
+  "publication_kind": "results_archive",
+  "result": {
+    "metrics": {
+      "compilation_success": {
+        "count": 542,
+        "denominator": 1000,
+        "rate": 0.542,
+        "score": 54.2,
+        "unknown_count": 0
+      },
+      "partial_replication": {
+        "count": 331,
+        "denominator": 1000,
+        "rate": 0.331,
+        "score": 33.1,
+        "unknown_count": 467
+      },
+      "coefficient_direction": {
+        "count": 486,
+        "denominator": 1000,
+        "rate": 0.486,
+        "score": 48.6,
+        "unknown_count": 467
+      },
+      "significance_level": {
+        "count": 416,
+        "denominator": 1000,
+        "rate": 0.416,
+        "score": 41.6,
+        "unknown_count": 467
+      }
+    },
+    "row": {
+      "Model ID": "glm-5.3-flash - Python",
+      "Compilation Success": 54.2,
+      "Partial Replication": 33.1,
+      "Correct Coefficient Direction": 48.6,
+      "Significant Level Correctness": 41.6
+    },
+    "columns": [
+      "Model ID",
+      "Compilation Success",
+      "Partial Replication",
+      "Correct Coefficient Direction",
+      "Significant Level Correctness"
+    ],
+    "expected_count": 1000,
+    "resolved_count": 1000,
+    "valid_prediction_count": 534,
+    "interruption_count": 0
+  },
+  "legacy_local_paper": {
+    "verified": true,
+    "profile": "local-paper-v1",
+    "metrics": {
+      "perfect": {
+        "count": 198,
+        "denominator": 1000,
+        "rate": 0.198,
+        "unknown_count": 469
+      },
+      "partial": {
+        "count": 258,
+        "denominator": 1000,
+        "rate": 0.258,
+        "unknown_count": 469
+      },
+      "coefficient_only": {
+        "count": 330,
+        "denominator": 1000,
+        "rate": 0.33,
+        "unknown_count": 469
+      },
+      "direction": {
+        "count": 484,
+        "denominator": 1000,
+        "rate": 0.484,
+        "unknown_count": 469
+      },
+      "significance": {
+        "count": 393,
+        "denominator": 1000,
+        "rate": 0.393,
+        "unknown_count": 469
+      }
+    }
+  }
+}

results/glm-5.3-flash/deepagents/README.md

@@ -0,0 +1,11 @@
+# glm-5.3-flash / deepagents
+
+1,000 个已封存槽位；成功 641，确定失败 359，审计中止 5。固定分母为 1,000。
+
+- `results.jsonl` / `results.csv`：逐题预测、执行/评分状态和四维指标；两种格式内容相同。
+- `summary.json`：固定 1,000 题分母、未知计数，以及分别保存的四维导出和旧诊断指标。
+- `config.json`：数据 revision、完整任务 ID 清单、生成/执行预算及来源哈希。
+
+本组由多次 continuation 批次和一次显式 6 槽位审计合并而成。任务 218 重放已封存最终程序；任务 1、425、785、980、1019 裁定为 `aborted_no_final`。没有重新派发模型请求。
+
+四维指标为本地实现，尚未验证与官方 scorer 完全一致。JSON null / CSV 空单元格表示未知或不可用，不表示 0。

results/glm-5.3-flash/deepagents/config.json

@@ -0,0 +1,1061 @@
+{
+  "schema_version": 1,
+  "model": "glm-5.3-flash",
+  "arm": "deepagents",
+  "harness": "DeepAgents 0.7.13",
+  "source_run_name": "glm53flash-deepagents-combined",
+  "archived_at": "2026-09-19T01:55:37.159917+00:00",
+  "dataset": {
+    "repo_id": "CamoAiLab/InferenceNet",
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+  ],
+  "original_limits_for_this_arm": {
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+  }
+}

results/glm-5.3-flash/deepagents/summary.json

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+}

results/index.json

@@ -1,7 +1,7 @@
 {
   "schema_version": 1,
   "kind": "results_archive",
-  "archived_at_utc": "2026-09-15T07:29:39.569697+00:00",
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   "entries": [
     {
@@ -75,7 +75,19 @@
       "arm": "deepagents",
       "rows": 1000,
       "path": "results/deepseek-v4-pro/deepagents"
+    },
+    {
+      "model": "glm-5.3-flash",
+      "arm": "baseline",
+      "rows": 1000,
+      "path": "results/glm-5.3-flash/baseline"
+    },
+    {
+      "model": "glm-5.3-flash",
+      "arm": "deepagents",
+      "rows": 1000,
+      "path": "results/glm-5.3-flash/deepagents"
     }
   ],
-  "updated_at_utc": "2026-09-16T17:18:47.373930+00:00"
+  "updated_at_utc": "2026-09-19T01:56:08.177246+00:00"
 }

results/manifest.sha256

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