Multi-Timescale Verification for Test-Time Adaptation of Social Agents

Self-evolving agents have demonstrated the potential to improve through accumulated experience, particularly in software engineering, where executable feedback can evaluate proposed changes. For social agents in unknown environments, however, both perceived events and their behavioral explanations are uncertain. Repeated reflection can therefore reinforce errors rather than correct them. This raises a central question: how can social agents build on past interactions without compounding unreliable beliefs? We introduce the *Verified Social Evolution Loop* (VSEL), a framework for social agents to evolve through verification at multiple timescales in unseen continuous environments. Within episodes, a *Physical Consistency Verifier* checks perceived states against spatio-temporal constraints. The retained evidence is consolidated into a *decision map* and provided to the visual-language model (VLM) for event-triggered reasoning and evidence reassessment in a zero-shot manner. Across episodes, a *Hypothesis Verifier* evaluates behavioral hypotheses against accumulated support and counterevidence. Using only egocentric RGB-D observations and poses, VSEL sets new state-of-the-art (SOTA) results across benchmarks on the Habitat 3.0 platform for social agents. Particularly, in social navigation, VSEL achieves an episode success rate of 0.73, surpassing the strongest compared reinforcement-learning baseline by 70% in relative terms. Overall, our framework offers a practical route to social agents that accumulate and revise knowledge through verified experience.