Add dataset card with test split and planned 1024-episode training set

README.md

@@ -0,0 +1,46 @@
+---
+language:
+- en
+pretty_name: AdaExploreBench
+size_categories:
+- n<1K
+tags:
+- benchmark
+- llm-agents
+- exploration
+- adaptation
+- rule-induction
+configs:
+- config_name: default
+  data_files:
+  - split: test
+    path: data/test.jsonl
+---
+
+# AdaExploreBench
+
+AdaExploreBench evaluates proactive and adaptive exploration in LLM agents. Agents design probe cases, infer hidden decision rules from utility feedback, and adapt to unannounced environmental changes.
+
+[Code](https://github.com/HKUST-KnowComp/AdaExploreBench) · [Eight-model overview](https://hkust-knowcomp.github.io/AdaExploreBench/)
+
+## Splits
+
+| Split | Episodes | Availability |
+| --- | ---: | --- |
+| `test` | 256 | Available in this release |
+| `train` | 1,024 planned | In preparation; no training data is included yet |
+
+The test set is balanced across finance, healthcare, cybersecurity, and manufacturing, and across additive aggregation, conjunctive gating, disjunctive sufficiency, and lexicographic priority rules. Each dynamic episode has eight numeric fields, three active factors per phase, and four ordered actions. An unannounced change replaces one factor and reverses another factor's direction.
+
+## Files
+
+- `data/test.jsonl` contains one complete episode per line.
+- `manifest.json` records the split size, schema version, domain and rule composition, and SHA-256 checksum.
+
+Episodes contain the evaluator's full task specification, latent mechanisms, evaluation cases, and branch definitions. Evaluated agents receive the task and feedback through the benchmark runtime, which controls access to hidden information.
+
+Use the code repository's `scripts/run_benchmark.py` with the downloaded JSONL path. Test episodes are intended for evaluation; keep them separate from model training and prompt development.
+
+## Training release
+
+A 1,024-episode training split is being prepared for this dataset repository. The current release defines only the available `test` split. The Dataset Card and split metadata will be updated when training data is released.