Datasets:

respanai
/

behavior-benchmark

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

Respan Respan

Behavior Benchmark

Does this LLM trace show this behavior? A multilingual benchmark on real chat and agent traces.
_{1,990 traces · 34,885 trace–behavior pairs · 19 languages · v1.0.0}

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

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), 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.

Citation

@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}
}