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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...
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.
This work proposes a fine-tuning-free listwise selector that improves selection accuracy while maintaining efficiency and compatibility with existing large language models, and builds reusable structured memories instead of learning selection behavior as model parameters.
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