LLM reasoning traces can be compiled into reusable symbolic solvers that achieve high accuracy on program synthesis benchmarks at zero inference cost and transfer to other domains.
A compute-matched re-evaluation of trove on math
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LLM adaptive exploration via runtime code execution outperforms static query generation for information extraction from heterogeneous BIM models on the new ifc-bench v2 benchmark.
A neuro-symbolic system using LLM-guided best-first search and Isabelle tools proves up to 77.6% of theorems on the seL4 benchmark, outperforming prior LLM methods and Sledgehammer.
citing papers explorer
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ReaComp: Compiling LLM Reasoning into Symbolic Solvers for Efficient Program Synthesis
LLM reasoning traces can be compiled into reusable symbolic solvers that achieve high accuracy on program synthesis benchmarks at zero inference cost and transfer to other domains.
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BIM Information Extraction Through LLM-based Adaptive Exploration
LLM adaptive exploration via runtime code execution outperforms static query generation for information extraction from heterogeneous BIM models on the new ifc-bench v2 benchmark.
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Neuro-Symbolic Proof Generation for Scaling Systems Software Verification
A neuro-symbolic system using LLM-guided best-first search and Isabelle tools proves up to 77.6% of theorems on the seL4 benchmark, outperforming prior LLM methods and Sledgehammer.