FALCA is proposed, a falsification-aware framework that formulates deep search as a propose-falsify process and improves long-horizon deep search performance over backbone and harness ablations, while reducing confirmation bias and error accumulation through more effective falsification and research-state revision.
Zi-Jun Dou, Kun Luo, Chao-Fan Li et al.· 0 citations
Results show that long-horizon reflective data is an effective route toward self-improving agents, and synthesize long-horizon improvement trajectories from machine learning and algorithmic programming tasks, two domains that offer verifiable feedback and reward sustained iteration.
Hong-Jin Qian, Chao-Fan Li, Kun Luo 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.· 1 citation
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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