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cs.AI2026
IndustryLLM: Failure-Driven LLM Training for Industrial Procurement
Liang Ding, Zhiang Xu, Yuyang Sheng +13
Industrial procurement requires language models to bridge informal buyer jargon, sparse marketplace attributes, and authoritative engineering standards under strict safety toleranc…
cs.CV2026
From Recognition to Reasoning: Benchmarking and Enhancing MLLMs on Real-World Receipt Document Understanding
Yandi Wang, Libin Zhan, Ziwei Huang +5
Extracting structured information from visual documents (Visual Information Extraction, VIE) is a cornerstone of business automation. While recent Multimodal Large Language Models…
cs.CL2026
Self-Verification Dilemma: Experience-Driven Suppression of Overused Checking in LLM Reasoning
Quanyu Long, Kai Jie Jiang, Jianda Chen +3
Large Reasoning Models (LRMs) achieve strong performance by generating long reasoning traces with reflection. Through a large-scale empirical analysis, we find that a substantial f…