Agent Behavior Maps: Locating Agents by What They Would Do, Without Running Them
LLM agents with similar success rates can follow different action sequences. An Agent Behavior Map represents LLM-harness combinations using centered action log-likelihoods on shared reference trajectories, without requiring new environment rollouts from the target agent. We derive error bounds relating squared coordinate distances to trajectory KL under explicit assumptions, and across eight cross-scored models the distances track the directly measured trajectory KL in rank order. Across 100 LLMs and four environments, ALFWorld-based coordinates support performance prediction and identify behavioral similarities. In ALFWorld, these similarities persist after controlling for success rates...
| Authors | Shota Fujikawa, Takuya Tamura, Kosuke Akimoto et al. |
|---|---|
| Primary category | cs.LG |
| Published | 21 October 2026 |
| Last updated at source | 21 October 2026 |
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