Lixin Liu
Author directory2026
Testing Behavioral Adaptation in Repeated Interactions Between LLM Agents
Lixin Liu | Yixuan Li | Xiankun Lin | Yutian Lu | Shiguang NI
Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
Lixin Liu | Yixuan Li | Xiankun Lin | Yutian Lu | Shiguang NI
Proceedings of the Second Workshop for REsearch on Agent Language Models (REALM 2026)
High aggregate scores can make language agents appear socially responsive even when they merely repeat a prompt-conditioned action. We introduce an identifiability-first framework that separates observed outcomes from evidence of adaptation. Across 3,465 released iterated-prisoner’s-dilemma simulations, 94.5% never change joint regime, leaving reciprocity largely unidentifiable. Controlled histories across nine models reveal opponent-dependent responses that vary substantially across equivalent interfaces. The released checkpoints have the smallest opponent-recency effects. Only the intervention cohort shows reliable propagation after a scripted defection. The diagnostic transfers to longer histories, a multiplayer game, and released-interface wrappers, although model rankings do not. Outcome-only evaluation conflates regime selection with adaptive behavior. Separating opportunity, response, and robustness prevents stable outcomes from being mistaken for social dynamics.