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Results show that directional reasoning is substantially more difficult than parallel intersection, highlighting that bridging structured and unstructured information spaces without losing constraints remains a major open challenge for agentic systems.
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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