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LLMs are rapidly transforming peer review, but critical gaps remain in ensuring quality, fairness, and ethical considerations across the entire workflow.
Unleashing multiple independently-optimized agents within a shared tree search dramatically boosts code generation performance, surpassing single-agent limitations.
Intrinsic reward signals in unsupervised RL for LLMs inevitably collapse due to sharpening of the model's prior, but external rewards grounded in computational asymmetries offer a path to sustained scaling.
