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Stepwise Reasoning Enhancement for LLMs via External Subgraph Generation

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arXiv:2606. 04454v1 Announce Type: new Abstract: Large language models have shown strong performance in natural language generation and downstream reasoning tasks, but they still struggle with logical consistency, factual grounding, and interpretability in complex multi-step reasoning.

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Authors: Xin Zhang, Yang Cao, Baoxing Wu, Kai Song, Siying Li
Released: 2026-06-04 (1mo ago)
Topic: Related to current project
Relation to my work: Read next because Stepwise Reasoning Enhancement for LLMs via External Subgraph Generation overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: strong, rect, eval, line, rate, candidate, language, model. Source: arxiv cs.CL (NLP).
arXiv: 2606.04454

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