| --- |
| license: other |
| license_name: mixed-source-licenses |
| language: |
| - en |
| - ar |
| - de |
| - es |
| - fr |
| - hi |
| - id |
| - it |
| - ja |
| - ko |
| - nl |
| - pl |
| - pt |
| - ru |
| - sv |
| - th |
| - tr |
| - vi |
| - zh |
| task_categories: |
| - text-classification |
| annotations_creators: |
| - machine-generated |
| pretty_name: Respan Behavior Benchmark |
| size_categories: |
| - 10K<n<100K |
| tags: |
| - evaluation |
| - llm |
| - agents |
| - behavior-classification |
| - observability |
| configs: |
| - config_name: default |
| default: true |
| data_files: |
| - split: core |
| path: core.parquet |
| - split: multilingual |
| path: multilingual.parquet |
| --- |
| |
| <p align="center"> |
| <img class="dark:hidden" src="respan-wordmark-black.png" alt="Respan" width="176"> |
| <img class="hidden dark:block" src="respan-wordmark-white.png" alt="Respan" width="176"> |
| </p> |
|
|
| <h1 align="center">Behavior Benchmark</h1> |
|
|
| <p align="center"> |
| Does this LLM trace show this behavior? A multilingual benchmark on real chat and agent traces.<br> |
| <sub>1,990 traces · 34,885 trace–behavior pairs · 19 languages · v1.0.0</sub> |
| </p> |
|
|
| --- |
|
|
| Each row pairs a trace (one LLM call: its input messages, output, and call |
| metadata) with a behavior written in plain language. A system predicts one of: |
|
|
| - `present`: the trace shows the behavior; |
| - `absent`: the trace shows it did not happen; |
| - `not_observable`: the trace doesn't contain enough to decide. |
|
|
| Behaviors cover agentic process, tool use, style, user and assistant |
| semantics, quantitative metadata (latency, tokens, cost), relational |
| properties, grounding, and language. |
|
|
| ## Quick start |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("respanai/behavior-benchmark") # splits: core, multilingual |
| |
| ex = ds["core"][0] |
| ex["input"], ex["output"], ex["metadata"] # the trace, in OpenAI chat format |
| ex["behavior_definition"], ex["label"] # the question and its answer |
| ``` |
|
|
| | split | traces | rows | contents | |
| |---|---:|---:|---| |
| | `core` | 1,475 | 26,495 | English chat, tool-use, search, and coding-agent traces | |
| | `multilingual` | 515 | 8,390 | Traces in 18 additional languages, plus English controls | |
|
|
| Labels: `absent` 87.6%, `present` 10.4%, `not_observable` 2.0%. |
|
|
| ## Fields |
|
|
| | field | description | |
| |---|---| |
| | `cell_id` | Unique row ID | |
| | `task_id`, `span_id` | Trace ID (shared by all rows on the same trace) | |
| | `suite` | `core` or `multilingual` (same as the split) | |
| | `source`, `source_dataset`, `source_license` | Upstream dataset and its license | |
| | `input` | Messages sent to the model: `role`, `content`, `tool_calls` (`id`, `name`, JSON `arguments`), `tool_call_id`, `name` | |
| | `output` | The model's reply, same shape as a message | |
| | `metadata` | `model`, `finish_reason`, `status`, `latency_ms`, `time_to_first_token_ms`, `prompt_tokens`, `completion_tokens`, `cost_usd`, `temperature`, `top_p`, `seed`, `stream`, `tools_defined` (null when unknown) | |
| | `language` | Language code of the trace | |
| | `def_id`, `behavior_type`, `behavior_definition` | Behavior ID, family, and text | |
| | `label` | `present`, `absent`, or `not_observable` | |
|
|
| ## Labels |
|
|
| Labels come from three methods. |
|
|
| | method | `core` | `multilingual` | |
| |---|---:|---:| |
| | Teachers: `gpt-5.6-sol` and `claude-opus-5` agree | 24,074 | 5,541 ¹ | |
| | Tie-break: the teachers disagreed and `gpt-6-astra` decided | 2,421 | ¹ | |
| | Construction: set by how the trace was built | — | 2,851 | |
|
|
| ¹ The multilingual count covers both teacher rows and tie-breaks. Rows with no |
| majority were dropped. Construction labels cover language behaviors, and |
| language-conditioned behaviors whose language condition is false. |
|
|
| These are model labels, not ground truth. `gpt-5.6-sol`, `claude-opus-5`, |
| `gpt-6-astra` and `claude-opus-5.5` helped make the labels, so they can't be |
| fairly evaluated on this benchmark. |
|
|
| ## Evaluation |
|
|
| Report F1 on `present` for `core` and `multilingual` separately, plus pooled. |
| For confidence intervals, bootstrap over `task_id` rather than rows. State the |
| model version, prompt, and any decision thresholds used. |
|
|
| ## Limitations |
|
|
| - Teachers saw a length-limited rendering of each trace, so a few |
| `not_observable` labels may be decidable from the full trace. |
| - No negated or omission-style behaviors. |
| - Models from the same family as a teacher may have an advantage. |
|
|
| ## Sensitive content and license |
|
|
| Traces come from public datasets and may contain personal data, fake |
| credentials, or expired signed URLs from the originals. We scanned for them |
| ([`release_stats.json`](release_stats.json)), but a scan can't catch |
| everything. Don't use the data to identify people, and don't try to use any |
| credential-like string. |
|
|
| Each row keeps its upstream license in `source_license`; see [LICENSE](LICENSE). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{respan_behavior_benchmark_2026, |
| title = {Respan Behavior Benchmark}, |
| author = {Respan}, |
| year = {2026}, |
| howpublished = {Hugging Face dataset}, |
| url = {https://huggingface.co/datasets/respanai/behavior-benchmark} |
| } |
| ``` |
|
|
