@inproceedings{yang-etal-2026-agents,
    title = "When Agents Look the Same: Quantifying Distillation-Induced Similarity in Tool-Use Behaviors",
    author = "Yang, Chenghao  and
      Zhang, Yuning  and
      Wen, Zhoufutu  and
      Gong, Tao  and
      Liu, Jiaheng  and
      Chu, Qi  and
      Yu, Nenghai",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.478/",
    pages = "10482--10502",
    ISBN = "979-8-89176-390-6",
    abstract = "Model distillation is a primary driver behind the rapid progress of LLM agents, yet it often leads to behavioral homogenization. Many emerging agents share nearly identical reasoning steps and failure modes, suggesting they may be distilled echoes of a few dominant teachers. Existing metrics, however, fail to distinguish mandatory behaviors required for task success from non-mandatory patterns thatreflect a model{'}s autonomous preferences. We propose two complementary metrics to isolate non-mandatory behavioral patterns: $\textbf{Response Pattern Similarity (RPS)}$ for verbal alignment and $\textbf{Action Graph Similarity (AGS)}$ for tool-use habits modeled as directed graphs. Evaluating 18 models from 8 providers on $\tau$-Bench and $\tau^2$-Bench against Claude Sonnet 4.5 (thinking), we find that within-family model pairs score 5.9 pp higher in AGS than cross-family pairs, and that Kimi-K2 (thinking) reaches 82.6{\%} $S_{\text{node}}$ and 94.7{\%} $S_{\text{dep}}$, exceeding Anthropic{'}s own Opus 4.1. A controlled distillation experiment further confirms that AGS distinguishes teacher-specific convergence from general improvement. RPS and AGS capture distinct behavioral dimensions (Pearson $r$ = 0.491), providing complementary diagnostic signals for behavioral convergence in the agent ecosystem.Our code is available at \url{https://github.com/Syuchin/AgentEcho}."
}