In this report, we introduce Sage-A1.5, a 35B Mixture-of-Experts agentic model that builds on A1 to advance long-horizon problem solving through adaptive capability composition. Compared with A1, Sage-A1.5 learns not only to solve individual tasks, but also to organize work and revise its approach during execution. Sag...
Wenjie Lou, Tian-Shuo Peng, Shi-Yang Feng 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
Results validate the effectiveness of combining autonomous, socially grounded data governance with preference-aligned training in practical search systems and demonstrate significant improvements in both offline relevance metrics and online user engagement indicators, along with enhanced annotation efficiency.
Tao Su, Jin-Jing Hu, Xiao Wang et al.· arXiv.org· 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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