World models support planning, control, learning from imagined experience, and model-mediated evaluation by predicting action-conditioned consequences. Their safety cannot be inferred from predictive accuracy alone: errors in observations, state and memory, dynamics, evaluation, execution, or feedback can propagate int...
Xiao-Mao Wang, Li-Tian Zhang, Xin-Ze Shi et al.· 0 citations
Just-In-Time Agent Memory (JAM), a trainable framework for query-conditioned context construction at runtime, is proposed, where it achieves stronger task performance than AOT-style memory systems while remaining substantially more efficient than prior trained agentic memory approaches.
Bing-Yu Yan, Chao-Fan Li, Hong-Jin Qian et al.· 0 citations
EvoBreak is an experience-conditioned sequential attack that operates through individually benign attack-stage tasks and induced experiences, revealing benign experience composition as a persistent attack surface in self-evolving agents.
Bing-Yu Yan, Xiao-Ming Zhang, Chao-Zhuo Li et al.· 2 citations
MAGER is a multi-agent genetic evolution framework that automatically discovers meta-paths optimized for LLM reasoning, enabling frozen LLMs to perform structure-aware veracity reasoning and introduces a graph in-context learning strategy that retrieves semantically and structurally similar demonstrations to strengthen...
Zi-Yi Zhou, Xiao-Ming Zhang, Hui Pang et al.· 0 citations
This work introduces AREX, a family of Recursively Self-Improving (RSI) deep research agents that substantially outperforms comparable-scale baselines and remains competitive with models using substantially more activated parameters.
Shuqi Lu, Chaofan Li, Kun Luo et al.· arXiv.org· 2 citations· ⚡1
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