Tag
The paper proposes a neuro-symbolic framework that decouples reasoning into symbolic validity and semantic groundedness, using a verifier and a trained PRM to improve reliability in scientific reasoning tasks for LLMs.
This paper introduces CALVER, a training-free symbolic verifier that scores structured causal reasoning traces against Pearl's criteria to select the best candidate, outperforming plurality voting and other selection methods on causal reasoning benchmarks where multiple valid answers exist.
SPL (Structured Prompt Language) is a declarative language that unifies deterministic and probabilistic computation modes for LLM workflows, enabling specification of computations that run across local and cloud nodes. It includes an extensive cookbook and experimental validation.
Google DeepMind's AlphaProof Nexus combines LLM-driven proof generation with machine verification using Lean, solving nine out of 353 open Erdős problems, two of which had been open for 56 years, at a cost of only a few hundred dollars per problem.