arXiv cs.AI· Vishvesh Bhat, Emmanuel Anaya Gonzalez, Jay Vaghasiya, Asad Aali·· 10 小时前AI 评分43
Forethought：基于神经符号原语编程的可验证推理系统
Forethought: Verifiable Reasoning from Neurosymbolic Primitive Programming
AI 导读
Forethought 是一个由符号与神经原语通过类型化领域特定语言组合而成的神经符号推理系统，在四个标准工具调用基准上端到端准确率相对直接提示同一基座模型中位数提升 32%。每个推理步骤都带有设计时检查、执行时强制的输出契约，符号步骤天然成立，学习步骤则经契约检查与实证验证。移除契约与控制层后大部分增益消失，表明带来提升的是受检结构而非单纯的任务分解。
正文 · 原文
Abstract:Modern agentic workflows decompose 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 improving 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, cannot be checked step by step, and is costly at inference. We present Forethought, a neurosymbolic reasoning system built from a library of symbolic and neural primitives composed through a typed domain-specific language. The results are reasoning programs: concrete representations of the model's work, in which every step carries an output contract that is checked at design time and enforced at execution time. Symbolic steps hold by construction, while learned steps are contract-checked and empirically validated, giving an explicit boundary between what is guaranteed and what is measured. Instantiated as a tool-calling execution kernel and evaluated on four standard tool-calling benchmark suites, Forethought improves end-to-end accuracy by a median of 32% relative to prompting the same base model directly, and reaches the highest aggregate accuracy among the evaluated configurations, though only by a margin within single-run noise over a frontier model. Systems pairing small open models with Forethought's fixed small-model layer match that frontier model, and a pipeline with a non-reasoning responder outperforms its reasoning sibling while requiring only a single fine-tuning stage. Removing the contract-and-control layer erases most of these gains, showing that checked structure, not decomposition alone, drives the improvement. Our results show the potential of rich symbolic orchestrators for building language-model agents that are more accurate, reliable, and auditable.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2607.04096 [cs.AI] |
| (or arXiv:2607.04096v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.04096 arXiv-issued DOI via DataCite |
Submission history
From: Asad Aali [view email]
[v1]
Sun, 5 Jul 2026 03:20:23 UTC (58 KB)
[v2]
Tue, 10 Nov 2026 00:16:50 UTC (54 KB)
来源：arXiv cs.AI · arxiv.org