arXiv AI By Vishvesh Bhat, Jay Vaghasiya, Emmanuel Anaya Gonzalez

Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming

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arXiv:2607. 04096v1 Announce Type: new Abstract: Current agentic workflows usually involve decomposing user requests into sequences of tool calls with correctly resolved parameters, the results of which are processed through reasoning traces in the language model's context window.

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Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming

Current agentic workflows usually involve decomposing user requests into sequences of tool calls with correctly resolved parameters, the results of which are processed through reasoning traces in the language model's context window. The prevailing route to improve such reasoning is test-time scaling, which trains models to search over long chains of thought; but the resulting capability is entangled in model weights, is not verifiable step-by-step, and is costly at inference.

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