Pseudo-Formalization decomposes proofs into self-contained natural language modules for independent LLM-based Block Verification, outperforming LLM-as-judge baselines on olympiad and research math benchmarks while releasing ArxivMathGradingBench.
Learning to give checkable answers with prover-verifier games
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SEAR trains one LLM via adversarial process rewards to explore harmful reasoning paths but flip to safe outputs, reducing over-refusal while preserving safety.
DAC decomposes agentic search into cooperative searcher and generator agents with cross-agent signals (abstention reward and hard-positive augmentation), achieving strong QA benchmark performance via LoRA on a shared backbone.
citing papers explorer
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Pseudo-Formalization for Automatic Proof Verification
Pseudo-Formalization decomposes proofs into self-contained natural language modules for independent LLM-based Block Verification, outperforming LLM-as-judge baselines on olympiad and research math benchmarks while releasing ArxivMathGradingBench.
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Addressing Over-Refusal in LLMs with Competing Rewards
SEAR trains one LLM via adversarial process rewards to explore harmful reasoning paths but flip to safe outputs, reducing over-refusal while preserving safety.
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Divide and Cooperate: Role-Decomposed Multi-Agent LLM Training with Cross-Agent Learning Signals
DAC decomposes agentic search into cooperative searcher and generator agents with cross-agent signals (abstention reward and hard-positive augmentation), achieving strong QA benchmark performance via LoRA on a shared backbone.