respanai/behavior-benchmark

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}

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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 87.4%, present 10.6%, 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

Each label is the agreement of two teacher models (gpt-5.6-sol and claude-opus-5). About 9% of rows, where they disagreed, were settled by gpt-6-astra; rows with no majority were dropped. These are model-consensus labels, not ground truth, and the three teacher models 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}
}