Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models. Recent work has shown that targeted mid-training can strengthen reasoning-intensive abilities such as math and science, and can also improve agentic capabilities in software-engineering settings. In this w...
Fengqing Jiang, Yi-Te Wang, Bo-Yi Liu et al.· 0 citations
While autonomous agents have made significant strides in"deep research"by iteratively navigating the open web to synthesize information, real-world problem-solving is rarely confined to a single environment. Complex analytical tasks inherently require agents to weave together evidence from both ambiguous unstructured t...
Ruo-Fan Wu, Pei-Ran Xu, Xiao-Long Li et al.· 0 citations
The results show that restricting peer reads during evidence gathering and strengthening commitment boundaries before a hypothesis is shared can broaden search and improve long-horizon multi-agent deep research.
Soyoung Yoon, Bo-Yi Liu, Yi-Te Wang et al.· 0 citations
ReASearch is presented, a unified framework for reasoning-driven optimization in which the agent autonomously decides what to evaluate, how to diagnose failures, which edits to make, and when to verify or restart.
Jun-Bo Li, Bo-Yi Liu, Can-Wen Xu et al.· 1 citation
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