Can LLMs Compute with Reasons?
On the map: LLM reasoning → Reasoning benchmarks and evaluation
Abstract (arXiv)
Large language models (LLMs) often struggle with complex mathematical tasks, prone to "hallucinating" incorrect answers due to their reliance on statistical patterns. This limitation is further amplified in average Small LangSLMs with limited context and training data. To address this challenge, we propose an "Inductive Learning" approach utilizing a distributed network of SLMs. This network leverages error-based learning and hint incorporation to refine the reasoning capabilities of SLMs. Our goal is to provide a framework that empowers SLMs to approach the level of logic-based applications achieved by high-parameter models, potentially benefiting any language model. Ultimately, this novel concept paves the way for bridging the logical gap between humans and LLMs across various fields.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 11 of its 34 references that are in the map. First posted on arXiv 19 Feb 2024.
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- 2018Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neuronscited by 235
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- 2025Guiding Reasoning in Small Language Models with LLM Assistance
- 2026Improving Mathematical Reasoning Capabilities in Large Language Models via Reasoning Process Error Classification
- 2026SLMJury: Can Small Language Models Judge as Well as Large Ones?
- 2025On LLM-Based Scientific Inductive Reasoning Beyond Equations
- 2025LEMMA: Learning from Errors for MatheMatical Advancement in LLMs
- 2025A Survey of Inductive Reasoning for Large Language Models
- 2025StreetMath: Study of LLMs' Approximation Behaviors
- 2024LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages