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This work proposes A-B-D to infer traits bottom-up from behavioral data (B-data), namely how agents act on their environment and communicate with users, as recorded in existing trajectories, and offers a new lens for understanding AI personality.
ContextEvo, a framework that learns a context policy from long-horizon trajectories and reconstructs the model-visible context at key decision points, identifies context-related failures, and applies targeted policy updates, is introduced.
EvoRS is introduced, a self-evolving RL framework that evolves the reward system from on-policy experience, representing it as an executable Reward-DAG, and is updated from on-policy rollouts and reward traces to maintain train-time reliability.
This work proposes SocialRL, a multi-turn reinforcement learning framework using PPO that propagates delayed outcome rewards back to each turn, enabling long-horizon planning and demonstrates the effectiveness of SocialRL across synthetic and real social scenes, as well as standard and challenging social scenarios.
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