DIN-Retrieval uses domain-invariant neuron representations to retrieve cross-domain demonstrations, achieving an average 1.8-point gain over state-of-the-art methods on mathematical and logical reasoning tasks.
Folio: Natural language reasoning with first-order logic
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DeltaLogic reveals that models with strong initial logical accuracy often fail to revise beliefs correctly after minimal premise edits, showing inertia even when the gold answer should change.
ChaosBench-Logic v2 benchmark and CARE protocol show LLMs achieve MCC 0.52 on FOL deduction but only 0.05 on regime-transition reasoning across 165 dynamical systems.
OPT-BENCH trains LLMs on NP-hard optimization via quality-aware RLVR, achieving 93.1% success rate and 46.6% quality ratio on Qwen2.5-7B while outperforming GPT-4o and transferring gains to other domains.
RAP turns LLMs into dual world-model and planning agents via MCTS to generate better reasoning paths, outperforming CoT baselines and achieving 33% relative gains over GPT-4 CoT using LLaMA-33B on plan generation.
Cross-domain demonstrations enable conditional positive transfer in in-context learning beyond an example absorption threshold by repairing reasoning structures instead of relying on semantic overlap.
LogicAgent uses a semiotic-square-guided approach to enhance logical reasoning in LLMs on the new RepublicQA benchmark and others, reporting average gains of 6.25% and 7.05% respectively.
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Reasoning with Language Model is Planning with World Model
RAP turns LLMs into dual world-model and planning agents via MCTS to generate better reasoning paths, outperforming CoT baselines and achieving 33% relative gains over GPT-4 CoT using LLaMA-33B on plan generation.