Position: Reasoning is a Learnable Rule-Based Process
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Computer Science > Artificial Intelligence
Title:Position: Reasoning is a Learnable Rule-Based Process
Abstract:Autonomous reasoning is among the most scientifically and economically motivating topics in AI today. Historically the purview of symbolic AI, recent advances have mainly emerged from deep probabilistic generative models. Despite immense interest and rapid progress, the generative AI community has not clearly converged on operational definitions for reasoning and often implicitly rejects the historical treatment of this topic in logic and verifiable automated reasoning. This position contends that definitional ambiguity leaves the construct validity of reasoning evaluation unverifiable, undermining quantifiable progress toward trustworthy autonomous reasoning. We also contend that this ambiguity is addressable. To that end, we provide (1) operational definitions based on a synthesis of the literature, positioning valid and sound reasoning as a learnable rule-based process; and (2) a checklist for best practices in the communication of AI reasoning research.
| Subjects: | Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2608.12325 [cs.AI] |
| (or arXiv:2608.12325v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.12325
arXiv-issued DOI via DataCite
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| Journal reference: | Proceedings of the 43rd International Conference on Machine Learning, Seoul, South Korea. PMLR 306, 2026 |
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