Author

Keqin Peng

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#artificial intelligence Preprint Sep 2026

IndustryLLM: Failure-Driven LLM Training for Industrial Procurement

This work introduces a failure-driven adaptation recipe spanning continued pre-training (CPT) and supervised fine-tuning (SFT) and formalizes an evidence-gated constraint-evaluation interface enforcing three-valued logic where unverified product evidence remains unknown rather than satisfied.

Liang Ding, Zhiang Xu, Yu-Yang Sheng et al. · 0 citations

Jul 2026

BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoning

A lightweight framework that dynamically terminates reasoning by estimating whether the generated prefix is sufficient for correct answering and derives robust training labels through repeated answer completions is presented, demonstrating an effective approach to more efficient LLM reasoning.

Keshu Fu, Ke-Qin Peng, Jun Bai et al. · arXiv.org · 0 citations