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README.md

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+---
+configs:
+  - config_name: single_hop
+    data_files:
+      - split: train
+        path: single_hop/train-*.parquet
+      - split: test
+        path: single_hop/test-*.parquet
+  - config_name: multi_hop
+    data_files:
+      - split: train
+        path: multi_hop/train-*.parquet
+      - split: test
+        path: multi_hop/test-*.parquet
+license: mit
+task_categories:
+  - question-answering
+language:
+  - en
+tags:
+  - tool-use
+  - agents
+  - llm
+  - benchmark
+pretty_name: When2Tool
+size_categories:
+  - 1K<n<10K
+---
+
+# When2Tool
+
+Benchmark dataset for **"LLM Agents Already Know When to Call Tools — Even Without Reasoning"** ([arXiv:2605.09252](https://arxiv.org/abs/2605.09252)).
+
+## Overview
+
+When2Tool is a benchmark of 18 environments designed to study *when* LLM agents should call tools. Tasks range from trivially solvable without tools to impossible without them, across three categories of tool necessity:
+
+- **Computational Scale** (5 envs): Calculator, Statistics, Counting, Matrix, Prime
+- **Knowledge Boundaries** (5 envs): Retriever, Historical Year, Game Rule, Hash, Decoding
+- **Execution Reliability** (5 envs): List Manipulation, DateTime, Code Executor, Schedule, Regex Match
+- **Multi-hop** (3 envs): Calculator, Retriever, Code Executor (3-step chains)
+
+Each environment has three difficulty levels (easy, medium, hard) that create a clear decision boundary between tool-necessary and tool-unnecessary tasks.
+
+## Dataset Structure
+
+### Configs
+
+- `single_hop`: 15 single-hop environments (900 train / 2,250 test)
+- `multi_hop`: 3 multi-hop environments with 3-step chains (180 train / 450 test)
+
+### Fields
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `id` | int | Unique task identifier |
+| `difficulty` | str | `easy`, `medium`, or `hard` |
+| `multi_step` | bool | Whether the task requires multiple tool calls |
+| `instruction` | str | The task instruction given to the agent |
+| `env_name` | str | Environment name (e.g., `CalculatorEnv`) |
+| `tools` | str (JSON) | Available tools for this environment |
+| `parameters` | str (JSON) | Environment parameters (e.g., corpus for retriever) |
+| `answer` | str | Expected final answer |
+| `steps` | str (JSON) | Intermediate steps for multi-hop tasks |
+| `tags` | str (JSON) | Environment and task type tags |
+
+### Loading
+
+```python
+from datasets import load_dataset
+
+# Single-hop tasks
+ds = load_dataset("Trustworthy-ML-Lab/When2Tool", "single_hop")
+
+# Multi-hop tasks
+ds_mh = load_dataset("Trustworthy-ML-Lab/When2Tool", "multi_hop")
+
+# Access a sample
+print(ds["test"][0]["instruction"])
+print(ds["test"][0]["env_name"])
+```
+
+## Citation
+
+```bibtex
+@article{sun2026when2tool,
+  title={LLM Agents Already Know When to Call Tools -- Even Without Reasoning},
+  author={Sun, Chung-En and Liu, Linbo and Yan, Ge and Wang, Zimo and Weng, Tsui-Wei},
+  journal={arXiv preprint arXiv:2605.09252},
+  year={2026},
+  url={https://arxiv.org/abs/2605.09252}
+}
+```

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