Add files using upload-large-folder tool

.gitattributes

@@ -58,3 +58,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+raw/textworld.jsonl filter=lfs diff=lfs merge=lfs -text
+raw/alfworld.jsonl filter=lfs diff=lfs merge=lfs -text
+raw/textworldexpress.jsonl filter=lfs diff=lfs merge=lfs -text
+raw/scienceworld.jsonl filter=lfs diff=lfs merge=lfs -text
+raw/jericho.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,226 @@
+---
+license: cc-by-4.0
+language:
+- en
+task_categories:
+- text-generation
+tags:
+- agent
+- multi-turn
+- interactive
+- text-games
+- benchmark
+- reasoning
+configs:
+- config_name: alfworld
+  data_files:
+  - split: train
+    path: "data/alfworld/train.parquet"
+  default: true
+- config_name: jericho
+  data_files:
+  - split: train
+    path: "data/jericho/train.parquet"
+- config_name: scienceworld
+  data_files:
+  - split: train
+    path: "data/scienceworld/train.parquet"
+- config_name: textworld
+  data_files:
+  - split: train
+    path: "data/textworld/train.parquet"
+- config_name: textworldexpress
+  data_files:
+  - split: train
+    path: "data/textworldexpress/train.parquet"
+---
+
+# TALES Agent Trajectories
+
+> Trajectories from **TALES: Text Adventure Learning Environment Suite** (NeurIPS 2026, Track on Evaluations and Datasets), by Christopher Zhang Cui, Xingdi Yuan, Ziang Xiao, Prithviraj Ammanabrolu and Marc-Alexandre Côté (University of California, San Diego; Microsoft Research Montréal; Johns Hopkins University). Code: https://github.com/microsoft/tale-suite
+
+Agent trajectory data from a text-adventure benchmark suite.
+
+## Leaderboard
+
+Top agents ranked by average best normalized score per game across 122 games, each repeated over 5 seeds (610 total). Scores reflect the highest normalized score achieved at any point during each playthrough, not the final score.
+
+> **Note:** The leaderboard covers the 76 configurations evaluated on all 122 games × 5 seeds. The released files also include `claude-fable-5` (high, with action tool; one seed per game) and 500-step Jericho-only runs of `gpt-6-astra` and `claude-opus-5.5` (`max_steps` = 500 in `raw/`), which are not ranked here. Both 500-step runs use maximum reasoning effort and the full interaction history in context, block save, load, restore and undo commands, and do not state the step budget in the prompt.
+>
+> **Action tool (`+tool`):** Claude models released after `claude-opus-4.6`, when run without an action tool, often do not stop after their first command and instead continue with imagined later observations, which they then treat as fact. These models are therefore evaluated with an action tool: the system prompt asks the model to submit each command by calling a `submit_command` tool, and the tool's `command` argument is sent to the game as the action. These configurations are marked `+tool` in the leaderboard and use the variant `react-high-tool` in `transcript_id`. `claude-opus-4.6` was also re-run with the tool as a control; its scores change minimally. `claude-opus-5.5` and `gpt-6-astra` do not show this behavior. `claude-opus-5.5` plays with bare-text actions; the 500-step `gpt-6-astra` runs submit actions through a structured action tool (variant `react-max-tool`) and were not re-run without it, since the tool changes scores minimally.
+>
+> **Interpreter commands (`claude-opus-5.5`):** in its 500-step runs, `claude-opus-5.5` repeatedly and deliberately tried to manipulate the underlying Frotz interpreter, sending dfrotz backslash meta-commands such as `\_` (which dfrotz reads as Return) or `\help` in place of game commands, mostly to get past in-game menus and key-press prompts. The harness for these runs rejects interpreter meta-commands, so 121 of its actions, in 36 of its 270 runs, were blocked. Each blocked step has the action `talesunsafeaction` and the reply "The rejected action was discarded; this step was wasted.", and still uses one of the 500 steps. The commands it submitted are listed in `extras/rejected_actions.jsonl` (see Rejected Actions below).
+>
+> **Missing transcript:** one transcript, `gpt-oss-20b` (high), ScienceWorldChemistryMix, seed 202411065, is missing from this release and will be added in a future update. Its score is included in the leaderboard.
+
+<!-- LEADERBOARD_START -->
+| Rank | Model | TW | TWX | ALFWorld | SciWorld | Jericho | **benchmark** |
+|------|-------|---: | ---: | ---: | ---: | ---:|---:|
+| 1 | claude-opus-5 (high) +tool | 1.000 | 0.968 | 1.000 | 0.950 | 0.222|**0.639** |
+| 2 | claude-opus-4.7 (high) +tool | 1.000 | 0.968 | 1.000 | 0.963 | 0.190|**0.628** |
+| 3 | gpt-5.4 (high) | 1.000 | 0.947 | 1.000 | 0.952 | 0.169|**0.613** |
+| 4 | claude-opus-4.6 (high) +tool | 1.000 | 0.985 | 1.000 | 0.909 | 0.178|**0.612** |
+| 5 | claude-opus-4.8 (high) +tool | 1.000 | 0.974 | 1.000 | 0.915 | 0.173|**0.610** |
+| 6 | claude-opus-4.6 (high) | 1.000 | 0.957 | 1.000 | 0.919 | 0.162|**0.604** |
+| 7 | claude-opus-4.5 (high) | 1.000 | 0.891 | 1.000 | 0.940 | 0.168|**0.603** |
+| 8 | o3 (medium) | 1.000 | 0.919 | 0.883 | 0.930 | 0.157|**0.588** |
+| 9 | gpt-5.6-terra (high) | 0.987 | 0.819 | 1.000 | 0.946 | 0.146|**0.584** |
+| 10 | gpt-5.1 (high) | 0.995 | 0.898 | 0.917 | 0.902 | 0.161|**0.583** |
+| 11 | o3 (high) | 1.000 | 0.896 | 0.817 | 0.931 | 0.161|**0.580** |
+| 12 | claude-sonnet-4.6 (high) | 1.000 | 0.905 | 1.000 | 0.887 | 0.130|**0.575** |
+| 13 | gpt-5 (high) | 1.000 | 0.755 | 0.933 | 0.918 | 0.172|**0.575** |
+| 14 | kimi-k2.6 (high) | 1.000 | 0.885 | 0.967 | 0.869 | 0.144|**0.571** |
+| 15 | gpt-5.6-luna (high) | 1.000 | 0.871 | 0.883 | 0.914 | 0.099|**0.552** |
+| 16 | o3 (low) | 0.991 | 0.898 | 0.700 | 0.883 | 0.142|**0.548** |
+| 17 | claude-4-sonnet | 0.996 | 0.784 | 0.917 | 0.870 | 0.123|**0.543** |
+| 18 | kimi-k2.5 (high) | 0.991 | 0.830 | 0.883 | 0.864 | 0.112|**0.539** |
+| 19 | claude-3.7-sonnet (1024) | 0.973 | 0.913 | 0.833 | 0.765 | 0.125|**0.525** |
+| 20 | claude-3.7-sonnet | 0.973 | 0.958 | 0.817 | 0.724 | 0.130|**0.521** |
+| 21 | claude-3.5-sonnet-latest | 0.955 | 0.816 | 0.750 | 0.823 | 0.096|**0.504** |
+| 22 | gpt-4.1 | 0.953 | 0.925 | 0.833 | 0.761 | 0.068|**0.499** |
+| 23 | grok-4.3 (high) | 0.964 | 0.611 | 0.967 | 0.832 | 0.090|**0.498** |
+| 24 | Qwen3.5-122B-A10B (1024) | 0.914 | 0.834 | 0.633 | 0.765 | 0.076|**0.468** |
+| 25 | Qwen3.5-27B (1024) | 0.920 | 0.861 | 0.750 | 0.685 | 0.077|**0.465** |
+| 26 | gpt-5-mini (high) | 0.947 | 0.619 | 0.617 | 0.827 | 0.095|**0.465** |
+| 27 | Qwen3.6-27B (1024) | 0.940 | 0.895 | 0.700 | 0.685 | 0.062|**0.459** |
+| 28 | o1 (high) | 0.978 | 0.702 | 0.283 | 0.801 | 0.103|**0.443** |
+| 29 | claude-sonnet-4.5 | 1.000 | 0.714 | 0.833 | 0.567 | 0.086|**0.435** |
+| 30 | gpt-4o | 0.836 | 0.806 | 0.567 | 0.614 | 0.056|**0.406** |
+| 31 | claude-3.5-haiku | 0.949 | 0.797 | 0.267 | 0.673 | 0.050|**0.396** |
+| 32 | Qwen3.6-35B-A3B (1024) | 0.817 | 0.734 | 0.417 | 0.598 | 0.028|**0.364** |
+| 33 | Llama-3.1-405B-Instruct | 0.909 | 0.792 | 0.317 | 0.518 | 0.061|**0.364** |
+| 34 | gemma-4-31b-it (1024) | 0.864 | 0.576 | 0.600 | 0.467 | 0.069|**0.351** |
+| 35 | gemini-2.0-flash | 0.808 | 0.761 | 0.200 | 0.571 | 0.054|**0.350** |
+| 36 | Qwen3-32B (1024) | 0.795 | 0.689 | 0.483 | 0.498 | 0.040|**0.343** |
+| 37 | Llama-3.3-70B-Instruct | 0.696 | 0.772 | 0.150 | 0.551 | 0.045|**0.328** |
+| 38 | Llama-3.1-70B-Instruct | 0.656 | 0.819 | 0.083 | 0.519 | 0.053|**0.320** |
+| 39 | Qwen2.5-72B-Instruct | 0.765 | 0.838 | 0.367 | 0.350 | 0.029|**0.307** |
+| 40 | Qwen3.5-9B (1024) | 0.733 | 0.726 | 0.183 | 0.474 | 0.035|**0.305** |
+| 41 | Mistral-Large-Instruct-2407 | 0.824 | 0.683 | 0.067 | 0.461 | 0.058|**0.303** |
+| 42 | o3-mini (high) | 0.839 | 0.613 | 0.117 | 0.481 | 0.050|**0.301** |
+| 43 | MiniMax-M2.7 (1024) | 0.640 | 0.304 | 0.217 | 0.553 | 0.064|**0.278** |
+| 44 | gpt-4.1-mini | 0.621 | 0.745 | 0.050 | 0.419 | 0.034|**0.271** |
+| 45 | Llama-3.3-Nemotron-Super-49B-v1 (1024) | 0.658 | 0.789 | 0.167 | 0.265 | 0.028|**0.251** |
+| 46 | gpt-5.4-nano (high) | 0.697 | 0.679 | 0.050 | 0.358 | 0.022|**0.249** |
+| 47 | Qwen3-30B-A3B (1024) | 0.555 | 0.582 | 0.167 | 0.315 | 0.030|**0.229** |
+| 48 | gpt-4o-mini | 0.566 | 0.736 | 0.000 | 0.272 | 0.018|**0.218** |
+| 49 | claude-haiku-4.5 | 0.355 | 0.542 | 0.000 | 0.305 | 0.054|**0.199** |
+| 50 | Llama-4-Scout-17B-16E-Instruct | 0.411 | 0.684 | 0.000 | 0.270 | 0.019|**0.198** |
+| 51 | Qwen3.5-4B (1024) | 0.377 | 0.638 | 0.000 | 0.279 | 0.018|**0.191** |
+| 52 | gpt-5-nano (high) | 0.500 | 0.413 | 0.017 | 0.320 | 0.017|**0.183** |
+| 53 | gpt-oss-20b (high) | 0.606 | 0.451 | 0.000 | 0.257 | 0.016|**0.179** |
+| 54 | Llama-4-Maverick-17B-128E-Instruct-FP8 | 0.435 | 0.561 | 0.083 | 0.114 | 0.020|**0.155** |
+| 55 | Mistral-Small-Instruct-2409 | 0.561 | 0.273 | 0.000 | 0.244 | 0.014|**0.148** |
+| 56 | DeepSeek-V4-Flash (1024) | 0.313 | 0.427 | 0.017 | 0.177 | 0.046|**0.147** |
+| 57 | Llama-3.1-8B-Instruct | 0.297 | 0.503 | 0.000 | 0.158 | 0.023|**0.139** |
+| 58 | DeepSeek-R1 (1024) | 0.371 | 0.386 | 0.000 | 0.158 | 0.010|**0.124** |
+| 59 | Qwen2.5-7B-Instruct | 0.276 | 0.456 | 0.000 | 0.126 | 0.007|**0.117** |
+| 60 | Llama-3.2-3B-Instruct | 0.214 | 0.420 | 0.000 | 0.100 | 0.015|**0.104** |
+| 61 | phi-4 | 0.209 | 0.438 | 0.000 | 0.089 | 0.015|**0.103** |
+| 62 | gpt-4.1-nano | 0.128 | 0.387 | 0.000 | 0.094 | 0.036|**0.100** |
+| 63 | Mistral-Small-24B-Instruct-2501 | 0.158 | 0.230 | 0.000 | 0.158 | 0.014|**0.088** |
+| 64 | DeepSeek-R1-Distill-Llama-70B (1024) | 0.087 | 0.398 | 0.000 | 0.077 | 0.013|**0.084** |
+| 65 | Qwen3.5-2B (1024) | 0.000 | 0.312 | 0.000 | 0.033 | 0.006|**0.051** |
+| 66 | Ministral-8B-Instruct-2410 | 0.109 | 0.228 | 0.000 | 0.023 | 0.004|**0.047** |
+| 67 | Mistral-Small-3.1-24B-Instruct-2503 | 0.025 | 0.103 | 0.000 | 0.105 | 0.008|**0.045** |
+| 68 | Mixtral-8x22B-Instruct-v0.1 | 0.171 | 0.084 | 0.000 | 0.040 | 0.004|**0.037** |
+| 69 | Llama-3.2-1B-Instruct | 0.000 | 0.190 | 0.000 | 0.024 | 0.006|**0.033** |
+| 70 | Phi-3-mini-128k-instruct | 0.027 | 0.094 | 0.000 | 0.024 | 0.003|**0.022** |
+| 71 | Phi-3.5-MoE-instruct | 0.000 | 0.070 | 0.000 | 0.023 | 0.004|**0.017** |
+| 72 | Phi-4-mini-instruct | 0.000 | 0.055 | 0.000 | 0.023 | 0.005|**0.015** |
+| 73 | Mixtral-8x7B-Instruct-v0.1 | 0.000 | 0.016 | 0.000 | 0.040 | 0.003|**0.013** |
+| 74 | Phi-3.5-mini-instruct | 0.000 | 0.020 | 0.000 | 0.024 | 0.005|**0.010** |
+| 75 | Phi-3-medium-128k-instruct | 0.000 | 0.000 | 0.000 | 0.023 | 0.003|**0.007** |
+| 76 | Qwen3.5-0.8B (1024) | 0.000 | 0.000 | 0.000 | 0.023 | 0.003|**0.007** |
+
+<!-- LEADERBOARD_END -->
+
+## Overview
+
+This dataset contains agent game trajectories collected across multiple text-adventure game frameworks. Each trajectory records a full episode of an LLM-driven agent interacting with a text-based game environment, including observations, actions, scores, and (where available) thinking traces.
+
+## Frameworks
+
+- **alfworld**
+- **jericho**
+- **scienceworld**
+- **textworld**
+- **textworldexpress**
+
+## Dataset Structure
+
+Each row is a single trajectory with the following fields:
+
+| Field | Description |
+|---|---|
+| `transcript_id` | Unique identifier for the trajectory |
+| `framework` | Game framework (e.g., textworld, jericho) |
+| `game` | Specific game name |
+| `model` | LLM used to drive the agent |
+| `agent_type` | Agent architecture (e.g., zero-shot) |
+| `score` | Final score achieved |
+| `max_score` | Maximum possible score |
+| `normalized_score` | Score normalized to [0, 1] |
+| `total_steps` | Number of agent actions taken |
+| `turns` | Full conversation history (JSON string) |
+| `score_progression` | Score at each step (JSON string) |
+
+> **Note:** `thinking_available` is true when thinking tokens were reported (`total_thinking_tokens` > 0), not when reasoning text is present. Some models report thinking tokens but return no reasoning text (e.g., `gpt-5` and `o3`), and some rows include reasoning text in `thinking` without a reported count (e.g., `Qwen/Qwen3-32B` and `deepseek-ai/DeepSeek-R1`). For `deepseek-ai/DeepSeek-R1-Distill-Llama-70B`, `total_thinking_tokens` is estimated from the reasoning length (characters ÷ 4). API models that do not return their raw reasoning either return no reasoning text or a summary of it; for `claude-opus-4.5`, `claude-opus-4.6`, `claude-sonnet-4.6`, `claude-opus-5.5` and `gpt-6-astra`, `thinking` holds this summary, not the raw reasoning.
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load a specific framework
+ds = load_dataset("ccui46/tale_suite_trajectories", "alfworld")
+
+# Load all frameworks
+for fw in ["alfworld", "jericho", "scienceworld", "textworld", "textworldexpress"]:
+    ds = load_dataset("ccui46/tale_suite_trajectories", fw)
+    print(f"{fw}: {len(ds['train'])} trajectories")
+```
+
+## Raw Data
+
+Raw JSONL files (one line per trajectory, metadata flattened) are available under `raw/` for direct download.
+
+## Rejected Actions
+
+`extras/rejected_actions.jsonl` lists, one JSON object per line, the 259 actions that the harness of the 500-step `gpt-6-astra` and `claude-opus-5.5` runs rejected. In the transcripts these steps show only a placeholder action: `talesunsafeaction` for malformed input or interpreter commands, and `talesblockedcommand` for blocked save, load, restore and undo commands.
+
+| Field | Description |
+|---|---|
+| `transcript_id`, `model`, `game`, `seed` | The trajectory, as in `data/jericho` |
+| `step`, `turn_index` | The step, and the index of its agent turn in `turns` |
+| `action` | The placeholder recorded in `turns` |
+| `proposed_action` | What the model submitted (null in the two redacted games) |
+| `rejection_type` | `unsafe_action` or `blocked_state_command` |
+| `rejection_reason` | Why the harness rejected it (e.g., `Frotz interpreter meta-command`, `empty or whitespace-only action`) |
+| `action_source` | Where the harness read the action from: `tool` for `gpt-6-astra`, `unknown` for `claude-opus-5.5`; null for blocked commands |
+| `sent_to_game` | Whether the placeholder was sent to the game. It was only for the 6 blocked `claude-opus-5.5` commands, which the game answered as an unknown verb; otherwise nothing reached the game and the step was wasted |
+
+In the `gpt-6-astra` runs, an empty action is the action tool's `<ENTER>` key, which is sent to the game as a bare Return.
+
+## Redacted Games
+
+For two Jericho games, *The Hitchhiker's Guide to the Galaxy* (`JerichoEnvHhgg`) and *Adventureland* (`JerichoEnvAdventureland`), the observations and the model reasoning / reasoning summaries are redacted. In these 782 trajectories, every environment turn reads `[observation withheld; regenerate with your own copy of the story file]`, and on agent turns `raw_output` holds only the action and `thinking` is null. Actions, seeds, scores and token counts are unchanged, so `thinking_available` and the token counts still describe the original run.
+
+To regenerate the observations, load the game with your own copy of the story file and the transcript's `seed`, then send the actions (`content` of the agent turns) to the game engine in order.
+
+If the authors or other interested parties would prefer that the observations and reasoning be redacted for other games, please contact us at textworld@microsoft.com or czcui@ucsd.edu.
+
+## Citation
+
+```bibtex
+@inproceedings{cui2026tales,
+  title     = {{TALES}: Text Adventure Learning Environment Suite},
+  author    = {Cui, Christopher Zhang and Yuan, Xingdi and Xiao, Ziang and Ammanabrolu, Prithviraj and C{\^o}t{\'e}, Marc-Alexandre},
+  booktitle = {40th Conference on Neural Information Processing Systems (NeurIPS 2026), Track on Evaluations and Datasets},
+  year      = {2026}
+}
+```
+
+## License
+
+[CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

croissant.json

@@ -0,0 +1,1652 @@
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+      "@type": "@vocab"
+    },
+    "dct": "http://purl.org/dc/terms/",
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+      "includes": "textworldexpress/*/*.parquet"
+    },
+    {
+      "@type": "cr:FileObject",
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+      "name": "rejected-actions",
+      "description": "Actions rejected by the harness in the 500-step gpt-6-astra and claude-opus-5.5 runs, one JSON object per line; see Rejected Actions in the dataset card.",
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+      "encodingFormat": "application/jsonlines",
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+  ],
+  "recordSet": [
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+      "key": {
+        "@id": "alfworld_splits/split_name"
+      },
+      "@id": "alfworld_splits",
+      "name": "alfworld_splits",
+      "description": "Splits for the alfworld config.",
+      "field": [
+        {
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+          "@id": "alfworld_splits/split_name",
+          "dataType": "sc:Text"
+        }
+      ],
+      "data": [
+        {
+          "alfworld_splits/split_name": "train"
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "alfworld",
+      "description": "ccui46/tale_suite_trajectories - 'alfworld' subset\n\nAdditional information:\n- 1 skipped column: tags",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/split",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "fileProperty": "fullpath"
+            },
+            "transform": {
+              "regex": "alfworld/(?:partial-)?(train)/.+parquet$"
+            }
+          },
+          "references": {
+            "field": {
+              "@id": "alfworld_splits/split_name"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/transcript_id",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "transcript_id"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/framework",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "framework"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/game",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "game"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/model",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "model"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/agent_type",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "agent_type"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/total_steps",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "total_steps"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/score",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/max_score",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "max_score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/normalized_score",
+          "dataType": "cr:Float64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "normalized_score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/turns",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "turns"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/score_progression",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "score_progression"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/thinking_available",
+          "dataType": "sc:Boolean",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "thinking_available"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/total_thinking_tokens",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "total_thinking_tokens"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/llm",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "llm"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/seed",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "seed"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/episode_token_usage",
+          "dataType": "cr:Float64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "episode_token_usage"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "alfworld/model_family",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-alfworld"
+            },
+            "extract": {
+              "column": "model_family"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "dataType": "cr:Split",
+      "key": {
+        "@id": "jericho_splits/split_name"
+      },
+      "@id": "jericho_splits",
+      "name": "jericho_splits",
+      "description": "Splits for the jericho config.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "jericho_splits/split_name",
+          "dataType": "sc:Text"
+        }
+      ],
+      "data": [
+        {
+          "jericho_splits/split_name": "train"
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "jericho",
+      "description": "ccui46/tale_suite_trajectories - 'jericho' subset\n\nAdditional information:\n- 1 skipped column: tags",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/split",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "fileProperty": "fullpath"
+            },
+            "transform": {
+              "regex": "jericho/(?:partial-)?(train)/.+parquet$"
+            }
+          },
+          "references": {
+            "field": {
+              "@id": "jericho_splits/split_name"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/transcript_id",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "transcript_id"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/framework",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "framework"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/game",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "game"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/model",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "model"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/agent_type",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "agent_type"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/total_steps",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "total_steps"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/score",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/max_score",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "max_score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/normalized_score",
+          "dataType": "cr:Float64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "normalized_score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/turns",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "turns"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/score_progression",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "score_progression"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/thinking_available",
+          "dataType": "sc:Boolean",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "thinking_available"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/total_thinking_tokens",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "total_thinking_tokens"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/llm",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "llm"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/seed",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "seed"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/episode_token_usage",
+          "dataType": "cr:Float64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "episode_token_usage"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "jericho/model_family",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-jericho"
+            },
+            "extract": {
+              "column": "model_family"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "dataType": "cr:Split",
+      "key": {
+        "@id": "scienceworld_splits/split_name"
+      },
+      "@id": "scienceworld_splits",
+      "name": "scienceworld_splits",
+      "description": "Splits for the scienceworld config.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld_splits/split_name",
+          "dataType": "sc:Text"
+        }
+      ],
+      "data": [
+        {
+          "scienceworld_splits/split_name": "train"
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "scienceworld",
+      "description": "ccui46/tale_suite_trajectories - 'scienceworld' subset\n\nAdditional information:\n- 1 skipped column: tags",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/split",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "fileProperty": "fullpath"
+            },
+            "transform": {
+              "regex": "scienceworld/(?:partial-)?(train)/.+parquet$"
+            }
+          },
+          "references": {
+            "field": {
+              "@id": "scienceworld_splits/split_name"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/transcript_id",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "transcript_id"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/framework",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "framework"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/game",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "game"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/model",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "model"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/agent_type",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "agent_type"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/total_steps",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "total_steps"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/score",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/max_score",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "max_score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/normalized_score",
+          "dataType": "cr:Float64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "normalized_score"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/turns",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "turns"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/score_progression",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "score_progression"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/thinking_available",
+          "dataType": "sc:Boolean",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "thinking_available"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/total_thinking_tokens",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "total_thinking_tokens"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/llm",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "llm"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/seed",
+          "dataType": "cr:Int64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "seed"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/episode_token_usage",
+          "dataType": "cr:Float64",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "episode_token_usage"
+            }
+          }
+        },
+        {
+          "@type": "cr:Field",
+          "@id": "scienceworld/model_family",
+          "dataType": "sc:Text",
+          "source": {
+            "fileSet": {
+              "@id": "parquet-files-for-config-scienceworld"
+            },
+            "extract": {
+              "column": "model_family"
+            }
+          }
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "dataType": "cr:Split",
+      "key": {
+        "@id": "textworld_splits/split_name"
+      },
+      "@id": "textworld_splits",
+      "name": "textworld_splits",
+      "description": "Splits for the textworld config.",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "textworld_splits/split_name",
+          "dataType": "sc:Text"
+        }
+      ],
+      "data": [
+        {
+          "textworld_splits/split_name": "train"
+        }
+      ]
+    },
+    {
+      "@type": "cr:RecordSet",
+      "@id": "textworld",
+      "description": "ccui46/tale_suite_trajectories - 'textworld' subset\n\nAdditional information:\n- 1 skipped column: tags",
+      "field": [
+        {
+          "@type": "cr:Field",
+          "@id": "textworld/split",
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+  "conformsTo": "http://mlcommons.org/croissant/1.1",
+  "name": "TALES Agent Trajectories",
+  "description": "Agent trajectory data from TALES (Text Adventure Learning Environment Suite), a text-adventure benchmark suite; code at https://github.com/microsoft/tale-suite. Each record contains a full episode of an LLM-driven agent interacting with a text-based game environment, including observations, actions, scores, model metadata, and score progression.",
+  "alternateName": [
+    "TALES trajectories",
+    "Trajectory dataset of TALES: Text Adventure Learning Environment Suite (NeurIPS 2026, Track on Evaluations and Datasets)"
+  ],
+  "creator": [
+    {
+      "@type": "Person",
+      "name": "Christopher Zhang Cui",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "University of California, San Diego"
+      }
+    },
+    {
+      "@type": "Person",
+      "name": "Xingdi Yuan",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "Microsoft Research Montréal"
+      }
+    },
+    {
+      "@type": "Person",
+      "name": "Ziang Xiao",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "Johns Hopkins University"
+      }
+    },
+    {
+      "@type": "Person",
+      "name": "Prithviraj Ammanabrolu",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "University of California, San Diego"
+      }
+    },
+    {
+      "@type": "Person",
+      "name": "Marc-Alexandre Côté",
+      "affiliation": {
+        "@type": "Organization",
+        "name": "Microsoft Research Montréal"
+      }
+    }
+  ],
+  "keywords": [
+    "text-generation",
+    "English",
+    "10K - 100K",
+    "parquet",
+    "optimized-parquet",
+    "Tabular",
+    "Text",
+    "Datasets",
+    "pandas",
+    "Polars",
+    "Croissant",
+    "🇺🇸 Region: US",
+    "agent",
+    "multi-turn",
+    "interactive",
+    "text-games",
+    "benchmark",
+    "reasoning"
+  ],
+  "url": "https://huggingface.co/datasets/ccui46/tale_suite_trajectories",
+  "license": "https://creativecommons.org/licenses/by/4.0/",
+  "version": "1.0.0",
+  "datePublished": "2026-11-05",
+  "citeAs": "@inproceedings{cui2026tales,\n  title     = {{TALES}: Text Adventure Learning Environment Suite},\n  author    = {Cui, Christopher Zhang and Yuan, Xingdi and Xiao, Ziang and Ammanabrolu, Prithviraj and C{\\^o}t{\\'e}, Marc-Alexandre},\n  booktitle = {40th Conference on Neural Information Processing Systems (NeurIPS 2026), Track on Evaluations and Datasets},\n  year      = {2026}\n}",
+  "rai:dataLimitations": "This dataset is intended for evaluating and analyzing baseline LLM agentic competencies in lightweight, easily adaptable text-adventure environments. It is not designed to evaluate real-world robotics, web navigation, human decision-making, clinical or safety-critical deployment, or general intelligence. The benchmark emphasizes skills such as exploration, adapting when one course of action fails, considering alternate approaches, and acting over extended horizons in constrained text environments. The leaderboard is restricted to canonical 100-step trajectories over a fixed common set of 122 games and 5 game seeds; it does not fully characterize very long-horizon behavior, alternate prompts, fine-tuned agents, or all possible game seeds. Some games, especially well-known interactive fiction titles, may appear in model pretraining data, so benchmark performance should not be interpreted as uncontaminated zero-shot generalization. Some Jericho environments contain non-normative fantasy scenarios, including violence required by the original game narrative. These are controlled fictional settings curated by the games' authors and should not be treated as approval for similar actions in realistic or open-world settings. The released files also include configurations outside this protocol, which are not ranked: claude-fable-5 (one seed per game) and 500-step Jericho runs of gpt-6-astra and claude-opus-5.5. For two Jericho games whose rights are held by third parties, The Hitchhiker's Guide to the Galaxy and Adventureland, observations and model reasoning are withheld; actions, seeds and scores are kept. The dataset is strictly not recommended for training production agents, making deployment-readiness claims, or evaluating agents in unconstrained real-world settings.",
+  "rai:dataBiases": "The dataset reflects the selected text-game frameworks, their English-language interfaces, scripted reward structures, and the benchmark authors' choice of games, seeds, prompts, and step budgets. It is biased toward text-adventure conventions such as inventory use, compass navigation, symbolic state changes, and terse command interfaces. Framework coverage is uneven because some suites contribute more games and trajectories than others. Jericho includes classic interactive fiction whose narratives may contain fantasy violence, adversarial creatures, theft, or other non-normative situations as part of authored fictional puzzles. These settings are not representative of real-world social norms or acceptable real-world behavior. The trajectories also reflect the behavior of the particular LLMs, decoding settings, and agent scaffolds used to collect them. Modern LLMs may have pretraining exposure to public walkthroughs or transcripts for famous games, which may inflate performance or produce unusually similar early action sequences. The dataset does not represent human players, multilingual users, non-English environments, or real-world populations.",
+  "rai:personalSensitiveInformation": "No personal or sensitive information about human subjects is intentionally included. The dataset consists of model-generated interactions with synthetic or scripted text-game environments. It does not intentionally contain names, contact information, national identifiers, faces, health or medical data, political or religious beliefs, socio-economic status, age, gender, or precise human geographic location data. The language category is present only in the sense that the game text and model outputs are primarily English. Some game narratives may include fictional characters, places, combat, or other authored fantasy content, but these are not records about real people. Because LLM outputs are open-ended, incidental generated text could theoretically resemble personal information, but no human participant records or private user data were collected.",
+  "rai:dataUseCases": "The dataset is intended to measure and support research on baseline LLM agentic competencies in text-adventure environments, especially exploration, adaptation after failed actions, alternate-plan generation, commonsense action selection, situated awareness, rule learning, score progression, and extended-horizon interaction through an API-style environment loop. Validated uses include reproducing the benchmark leaderboard, comparing models under the canonical 100-step common-game protocol, auditing trajectories qualitatively or with failure detectors, and developing methods that improve LLM agent performance in constrained text-adventure settings. Validity evidence comes from benchmark runs across many model families and from environment-provided scores and normalized scores. The dataset has not been validated for supervised training, RL fine-tuning, factual accuracy evaluation, safety red-teaming, multilingual evaluation, human preference modeling, real-world planning, or deployment of agents outside the included game frameworks. Because open-ended exploratory behavior in unconstrained natural language systems can drift into non-normative or harmful behavior, this dataset should be used as a controlled research platform rather than as evidence that an agent is safe to explore in open-ended real-world environments.",
+  "rai:dataSocialImpact": "The main positive impact of the dataset is to provide a lightweight, adaptable platform for studying LLM agents before moving to higher-stakes or less controlled domains. It can support reproducible evaluation of exploratory behavior, adaptation to failed actions, extended-horizon reasoning, and other baseline agentic competencies. These benefits may help the research community understand why agents get stuck, fail to infer rules, ignore observations, or over-rely on memorized game knowledge. The main risks are overinterpretation and misuse: text-adventure success should not be treated as broad real-world agency, safety, or deployment readiness. A further risk is that unconstrained exploratory agents can produce non-normative or harmful natural-language behavior when moved outside carefully controlled environments. Some Jericho games include fictional non-normative actions, such as fantasy violence needed to solve authored puzzles; for example, progress in Zork can require killing a troll with a sword. These actions occur in curated fantasy settings and would not be acceptable in environments too similar to the real world. Mitigations include using scripted game environments, documenting the 100-step leaderboard protocol, releasing complete trajectories for auditability (withholding observations and reasoning only for two third-party Jericho titles), noting contamination and fantasy-content limitations, and preserving framework-specific licensing information.",
+  "rai:hasSyntheticData": true,
+  "prov:wasDerivedFrom": [
+    {
+      "@id": "https://github.com/alfworld/alfworld",
+      "prov:label": "ALFWorld",
+      "description": "Text-based embodied household task environment used as one source framework for agent trajectories.",
+      "sc:license": "See the ALFWorld repository for license terms."
+    },
+    {
+      "@id": "https://github.com/microsoft/jericho",
+      "prov:label": "Jericho",
+      "description": "Interactive fiction environment suite used as one source framework for agent trajectories.",
+      "sc:license": "See the Jericho repository and individual game licenses for license terms."
+    },
+    {
+      "@id": "https://github.com/allenai/ScienceWorld",
+      "prov:label": "ScienceWorld",
+      "description": "Science-themed text environment suite used as one source framework for agent trajectories.",
+      "sc:license": "See the ScienceWorld repository for license terms."
+    },
+    {
+      "@id": "https://github.com/microsoft/TextWorld",
+      "prov:label": "TextWorld",
+      "description": "Text-game generation and interaction framework used as one source framework for agent trajectories.",
+      "sc:license": "See the TextWorld repository for license terms."
+    },
+    {
+      "@id": "https://github.com/cognitiveailab/TextWorldExpress",
+      "prov:label": "TextWorldExpress",
+      "description": "Fast text-game environment suite used as one source framework for agent trajectories.",
+      "sc:license": "See the TextWorldExpress repository for license terms."
+    }
+  ],
+  "prov:wasGeneratedBy": [
+    {
+      "@type": "prov:Activity",
+      "prov:type": "Collection",
+      "prov:label": "LLM agent trajectory collection",
+      "prov:description": "Trajectories were collected by running LLM-driven agents in text-adventure environments from ALFWorld, Jericho, ScienceWorld, TextWorld, and TextWorldExpress. For each episode, the agent received text observations and issued text actions through the environment interface. Logs include observations, actions, scores, normalized scores, total steps, model metadata, and score progression. Collection used simulations rather than human-subject interaction, surveys, interviews, crowdsourcing, or web scraping.",
+      "prov:wasAttributedTo": [
+        {
+          "@type": "prov:Agent",
+          "@id": "tales_authors",
+          "prov:label": "TALES authors",
+          "prov:description": "Christopher Zhang Cui, Xingdi Yuan, Ziang Xiao, Prithviraj Ammanabrolu and Marc-Alexandre Côté (University of California, San Diego; Microsoft Research Montréal; Johns Hopkins University) designed the evaluation protocol, selected the environments, ran the agents, and prepared the release."
+        },
+        {
+          "@type": "prov:SoftwareAgent",
+          "@id": "llm_agent_runners",
+          "prov:label": "LLM agent runner software",
+          "prov:description": "Automated evaluation scripts and environment wrappers used to issue model actions, collect environment observations, and store episode transcripts."
+        }
+      ]
+    },
+    {
+      "@type": "prov:Activity",
+      "prov:type": "Preprocessing",
+      "prov:label": "Canonical trajectory filtering and export",
+      "prov:description": "Raw transcripts were filtered to the canonical common evaluation set of 122 games x 5 game seeds with max_steps=100, which defines the leaderboard. The released files also include configurations outside this protocol, which are not ranked: claude-fable-5 (one seed per game) and 500-step Jericho runs of gpt-6-astra and claude-opus-5.5. Duplicate exports of the same episodes were merged into one record. Metadata was flattened, nested trajectory fields such as turns and score_progression were serialized for Parquet compatibility, and files were exported as per-framework Parquet splits plus raw JSONL files. For two Jericho games whose rights are held by third parties, The Hitchhiker's Guide to the Galaxy and Adventureland, observations and model reasoning are withheld; actions, seeds and scores are kept.",
+      "prov:wasAttributedTo": [
+        {
+          "@type": "prov:SoftwareAgent",
+          "@id": "upload_transcripts_slim.py",
+          "prov:label": "upload_transcripts_slim.py",
+          "prov:description": "Script used to select canonical trajectories, flatten records, build README metadata, and upload files to Hugging Face."
+        },
+        {
+          "@type": "prov:SoftwareAgent",
+          "@id": "generate_leaderboard.py",
+          "prov:label": "generate_leaderboard.py",
+          "prov:description": "Script used to compute canonical 100-step leaderboard scores from the selected trajectories."
+        },
+        {
+          "@type": "prov:SoftwareAgent",
+          "@id": "tales_release_scripts",
+          "prov:label": "TALES release scripts",
+          "prov:description": "Scripts that converted the newer runs to this format, merged duplicate exports, redacted the two third-party titles, and built the leaderboard for the final release."
+        }
+      ]
+    },
+    {
+      "@type": "prov:Activity",
+      "prov:type": "Annotation",
+      "prov:label": "Failure detector analysis",
+      "prov:description": "Some accompanying analyses use automated and manual failure-mode detectors for phenomena such as rule learning and situated awareness. These annotations are analysis artifacts rather than human-subject labels and are not required to load the trajectory dataset itself.",
+      "prov:wasAttributedTo": [
+        {
+          "@type": "prov:Agent",
+          "@id": "tales_authors",
+          "prov:label": "TALES authors",
+          "prov:description": "Christopher Zhang Cui, Xingdi Yuan, Ziang Xiao, Prithviraj Ammanabrolu and Marc-Alexandre Côté (University of California, San Diego; Microsoft Research Montréal; Johns Hopkins University) designed the evaluation protocol, selected the environments, ran the agents, and prepared the release."
+        }
+      ]
+    }
+  ],
+  "publisher": {
+    "@type": "Person",
+    "name": "Christopher Zhang Cui",
+    "url": "https://huggingface.co/ccui46"
+  }
+}

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