Resource-Aware Neuro-Symbolic Reasoning for Local Small Language Models
For practitioners using small local LLMs, this work shows that symbolic verification can replace repeated sampling on structured reasoning tasks, reducing model calls and latency without sacrificing accuracy.
The paper introduces VFR-LLM, a neuro-symbolic pipeline that translates problems into finite-domain rules for symbolic reasoning, achieving 0.983 accuracy on precedence problems and 0.933 on logical-deduction tasks with a single model call, compared to 0.700 and 0.283 for serial self-consistency with five calls, respectively.
Small language models can run locally on consumer hardware, but structured reasoning often pushes users toward repeated sampling or larger models. This article studies a bounded neuro-symbolic alternative for local inference: a model translates a problem into typed finite-domain rules and constraints, a symbolic layer checks traceability and consistency, and a deterministic solver performs the reasoning step. The resulting Verifiable Formalization and Repair pipeline (VFR-LLM) tests when symbolic verification can replace repeated sampling without weakening accuracy. We evaluate it through LM Studio on Apple Silicon, using Qwen3-4B-2507 as the primary model, with Phi-4-mini-reasoning and Gemma-3n-E4B as robustness checks. On 120 generated pure-precedence problems, Qwen VFR-LLM achieves 0.983 accuracy, versus 0.700 for serial self-consistency using one model call instead of five. On a 120-instance BBH-derived extended logical-deduction subset, it reaches 0.933 versus 0.283. The advantage persists against a stronger cost-aware adaptive self-consistency baseline, which lowers sampling cost but not the single-call accuracy gap. Gemma reproduces the same model-dependent boundaries and Phi is negative on typed constraints. The contribution is bounded: finite-domain logic can replace repeated local sampling for some structured tasks, saving model calls and serial latency, with stratum-dependent token savings.