9 papers
Magnifying What Matters: Attention-Guided Adaptive Rendering for Visual Text Comprehension
Shenglai Zeng, Qirui Wang, Kai Guo +3
Visual Text Comprehension (VTC) renders text into images for a vision-language model (VLM) to read, sidestepping LLM context-window limits and powering applications from long-page…
Why Retrieval-Augmented Generation Fails: A Graph Perspective
Kai Guo, Xinnan Dai, Zhibo Zhang +5
Retrieval-Augmented Generation (RAG) has become a powerful and widely used approach for improving large language models by grounding generation in retrieved evidence. However, RAG…
When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression
Xinnan Dai, Kai Yang, Cheng Luo +3
Reasoning hallucinations in large language models (LLMs) often appear as fluent yet unsupported conclusions that violate either the given context or underlying factual knowledge. A…
Fix Before Search: Benchmarking Agentic Query Visual Pre-processing in Multimodal Retrieval-augmented Generation
Jiankun Zhang, Shenglai Zeng, Kai Guo +4
Multimodal Retrieval-Augmented Generation (MRAG) has emerged as a key paradigm for grounding MLLMs with external knowledge. While query pre-processing (e.g., rewriting) is standard…
Attn-GS: Attention-Guided Context Compression for Efficient Personalized LLMs
Shenglai Zeng, Tianqi Zheng, Chuan Tian +10
Personalizing large language models (LLMs) to individual users requires incorporating extensive interaction histories and profiles, but input token constraints make this impractica…
GraphGhost: Tracing Structures Behind Large Language Models
Xinnan Dai, Xianxuan Long, Chung-Hsiang Lo +4
Large Language Models (LLMs) exhibit strong reasoning capabilities on structured tasks, yet the internal mechanisms underlying such behaviors remain poorly understood. Existing int…