We have 3 of 8 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
This paper introduces a novel drop-in neurosymbolic layer designed to seamlessly integrate into existing LLM backbones enhancing logical reasoning and mitigating long-context resource consumption, and repurposes this approach to address the severe computational strain of long context inference.
Forethought is presented, a neurosymbolic reasoning system that instead treats reasoning as an explicit, verifiable program, that builds from a library of symbolic and neural primitives which are composed through a domain-specific language.
PLVR (Program Learning with Verifiable Rewards): a post training method that learns such programs directly from input-output examples and is released with the symbolic backpropagation library and a conformance checker so the method can be applied to primitive libraries other than the authors' own.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
