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Clarus transforms the landscape of scientific collaboration by enabling autonomous agents to work together in a structured, traceable, and resource-aware manner.
An optimal knowledge distribution can significantly enhance LLM knowledge boundaries, outperforming traditional synthesis methods across multiple benchmarks.
LLM memory failures are systematic, stemming from operation-level issues like information loss and retrieval misalignment, and can be automatically corrected with prompt optimization guided by fine-grained error tracing.
Traditional research papers are costing AI agents reproducibility and understanding, but a new "Agent-Native" format that captures the full messy research process boosts performance by up to 20%.
Stop treating tests as immutable oracles: letting repair agents revise behavioral constraints during search dramatically improves issue resolution.
