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. We present Forethought, 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 dom...
| Authors | Vishvesh Bhat, Jay Vaghasiya, Emmanuel Anaya Gonzalez |
|---|---|
| Primary category | cs.AI |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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