Enhancing Small Language Models Reasoning through Knowledge Graph Grounding
A new study enhances small language model (SLM) reasoning by grounding them in knowledge graphs via a neuro-symbolic agentic framework. Experiments on CLUTRR with Gemma 3 and Llama 3.2 show RGCN-derived hints improve performance by 1.5-2x, but reveal extraction bottlenecks and sequential deductive fragility.
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[Submitted on 14 Jul 2026]
Title:Enhancing Small Language Models Reasoning through Knowledge Graph Grounding
View a PDF of the paper titled Enhancing Small Language Models Reasoning through Knowledge Graph Grounding, by Dimitrios Kelesis and 1 other authors
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Abstract:Although large language models (LLMs) have set benchmarks for zero-shot reasoning, their deployment remains cost-prohibitive and environmentally taxing. Small Language Models (SLMs) offer a sustainable alternative, but prone to errors, on tasks requiring complex, multi-hop logical grounding. We investigate a neuro-symbolic agentic framework to enhance the reasoning capabilities of SLMs, specifically Gemma 3 (1B, 4B) and Llama 3.2 (3B), using the CLUTRR kinship benchmark. Our approach transforms the SLM into a minimalist agent utilizing two specialized tool calls: extract_facts for symbolic triplet extraction and get_hint for expert reasoning via a Relational Graph Convolutional Network (RGCN). We evaluate these models across two configurations, both in an Oracle scenario with ground-truth triplets and a Realistic scenario relying on self-extracted knowledge. Our results reveal that while RGCN-derived hints provide a 1.5 - 2x performance gain over story-only baselines, the system is constrained by the extraction bottleneck and sequential deductive fragility, where early extraction errors compound over multi-hop chains. Furthermore, we identify a "distraction effect" in specific architectures where noisy, self-generated facts degrade performance despite the presence of expert hints. This work characterizes the challenges of symbolic grounding in low-resource agentic systems and provides a roadmap for iterative verification in neuro-symbolic agentic pipelines.
Comments: Presented at the 2nd Causal Neuro-symbolic Artificial Intelligence (Causal NeSy): Toward Agentic LLMs with Neuro-Symbolic and Graph Based Reasoning Workshop @ ESWC2026
Subjects:
Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2607.14149 [cs.AI]
(or arXiv:2607.14149v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.14149
arXiv-issued DOI via DataCite
Submission history
From: Konstantinos Bougiatiotis [view email] [v1] Tue, 14 Jul 2026 13:07:46 UTC (1,468 KB)
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