When user queries are incomplete, large language models should ask targeted questions before committing to an answer, a capability we call proactive elicitation, yet evaluation on real user-assistant queries across seven LLMs (8B to frontier) shows that fewer than 11% of underspecified queries trigger a follow-up quest...
Rong-Yu Zhang, Mu-Tian Bao, Jin-Jian Zhang et al.· Proceedings of the 2026 Conf...· 0 citations
InterTab is a structure-aware framework for CoT reasoning over table images that interleaves chain-of-thought with tool calls that crop structure-aligned table regions, and improves the average accuracy of its backbone from 68.28% to 73.17% and achieves the best average performance among all compared methods.
Hanqian Li, Si-Rui Huang, Chen Ling et al.· 0 citations
Reinforcement Fine-Tuning (RFT) has enabled medical Multimodal Large Language Models (MLLMs) to produce Chain-of-Thought (CoT) reasoning for visual question answering, yet these models suffer from $\textit{confidence miscalibration}$---a systematic gap between expressed certainty and actual diagnostic accuracy that und...
Yuetian Du, Yu-Cheng Wang, Zhenyuan Chen et al.· Lecture notes in computer sc...· 0 citations
MetaRAG is proposed, a belief-action aligned policy optimization framework for agentic RAG that consistently improves the accuracy-efficiency trade-off over strong RL-based agentic RAG baselines, with gains that transfer to deep research settings, different optimizers, and multiple model backbones.
Qiuyi Qi, Tian Liang, Jiamu Wang et al.· 0 citations
