TraceDance: An Automated System for Building Agent Behavior Benchmarks from Real-World Agent Deployment Traces
An agent can complete a task while exhibiting undesirable behavior during execution. Developers need tests for the specific behaviors encountered in deployment, beyond fixed benchmark suites. We present TraceDance, an agent system that constructs targeted benchmarks from deployment traces for user-specified undesirable behaviors. For efficient construction, Anchor-and-Confirm combines programmable retrieval with candidate-level confirmation by a Flash large language model (LLM), while the Anchor Synthesis Loop generates and revises specifications for custom behaviors. The benchmarks use decision-point continuation to evaluate an LLM's next turn at a recorded decision point with a behavior...
| Authors | Dehai Min, Daoan Zhang, Yiming Zeng et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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