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cs.AI2026
Elastic Horizon: Discovering the Effective Interaction Frontier in Agentic Reinforcement Learning
Gangyi Zhang, Junjie Meng, Letian Zhang +6
Scaling the interaction horizon-the maximum number of environment interactions per episode-improves LLM agents on long-horizon tasks, and curriculum-based methods that progressivel…
cs.AI2026
IMPACT: Attention Is the Interaction Map for Scalable Interaction-Aware World Model Training
Rongze Tang, Jianjie Fang, Zhaolu Wang +8
World models have made remarkable progress in action-conditioned future prediction for embodied agents, yet still struggle to model physically plausible interactions. Existing appr…
cs.AI2026
CAER: Causal Action Effect Reweighting for World Model Training
Jianjie Fang, Xvyuan Liu, Ziyou Wang +9
World models are becoming core infrastructure for embodied intelligence, with action-conditioned video generation providing controllable predictions of how scenes evolve after agen…