most citedCMRAG: Co-modality-based visual document retrieval and question answering

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cs.CL2026

ActTraitBench: Quantifying the Knowledge-Decision Gap in Large Language Models via Human-Grounded Behavioral Validation

Yutong Yang, Chenxi Miao, Weikang Li +1

While Large Language Models (LLMs) can convincingly simulate personas in explicit self-reports, they often deviate in implicit behavioral decisions, revealing a substantial Knowled…

cs.CL2026

Trait-Aware Policy Optimization for Autoregressive Multi-Trait Essay Scoring

Zhengyang Wang, Sanwoo Lee, Jiaxin Wang +3

Multi-trait essay scoring aims to provide fine-grained evaluation of writing quality across multiple dimensions. However, how to effectively post-train autoregressive scoring model…

cs.CL2026

DuCCAE: A Hybrid Engine for Immersive Conversation via Collaboration, Augmentation, and Evolution

Xin Shen, Zhishu Jiang, Jiaye Yang +13

Immersive conversational systems in production face a persistent trade-off between responsiveness and long-horizon task capability. Real-time interaction is achievable for lightwei…

cs.CL2026

LM-Lexicon: Improving Definition Modeling via Harmonizing Semantic Experts

Yang Liu, Jiaye Yang, Weikang Li +3

We introduce LM-Lexicon, an innovative definition modeling approach that incorporates data clustering, semantic expert learning, and model merging using a sparse mixture-of-experts…

cs.CL2026

Decide Then Retrieve: A Training-Free Framework with Uncertainty-Guided Triggering and Dual-Path Retrieval

Wang Chen, Guanqiang Qi, Weikang Li +3

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, but existing approaches indiscriminately trigger retrieval and rely…

cs.CL2025★ 1 cited

CMRAG: Co-modality-based visual document retrieval and question answering

Wang Chen, Wenhan Yu, Guanqiang Qi +5

Retrieval-Augmented Generation (RAG) has become a core paradigm in document question answering tasks. However, existing methods have limitations when dealing with multimodal docume…