Multimodal large language models (MLLMs) have advanced the integration of visual and linguistic modalities, establishing themselves as the dominant paradigm for visual-language tasks. Current approaches like chain of thought (CoT) reasoning have augmented the cognitive capabilities of large language models (LLMs), yet their adaptation to MLLMs is hindered by heightened risks of hallucination in cross-modality comprehension. In this paper, we find that the thinking while looking paradigm in current multimodal CoT approaches--where reasoning chains are generated alongside visual input--fails to mitigate hallucinations caused by misleading images. To address these limitations, we propose the Visual Inference Chain (VIC) framework, a novel approach that constructs reasoning chains using textual context alone before introducing visual input, effectively reducing cross-modal biases and enhancing multimodal reasoning accuracy. Comprehensive evaluations demonstrate that VIC significantly improves zero-shot performance across various vision-related tasks, mitigating hallucinations while refining the reasoning capabilities of MLLMs. Our code repository can be found at https://github.com/Terry-Xu-666/visual_inference_chain.
Looking Back and Forth: Cross-Image Attention Calibration and Attentive Preference Learning for Multi-Image Hallucination Mitigation
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；Harbin Institute of Technology（哈尔滨工业大学）；Beijing Institute of Technology（北京理工大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学深圳分校）
Understanding and Mitigating Hallucinations in Multimodal Chain-of-Thought Models
School of Computer Science and Ningbo Institute, Northwestern Polytechnical University, China（西北工业大学计算机学院和宁波研究院）；National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, China（国家空天地海一体化大数据应用技术国家工程实验室）
GraphThinker: Reinforcing Temporally Grounded Video Reasoning with Event Graph Thinking
Queen Mary University of London（伦敦玛丽女王大学）；Samsung AI Centre Cambridge（剑桥三星人工智能中心）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Nanyang Technological University（南洋理工大学）
GraphThink: Graph-Enhanced LLM Thinking for Long-Horizon Embodied Task Planning
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学高瓴人工智能学院）；Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Tsinghua University（清华大学）；The Hong Kong Polytechnic University（香港理工大学）
A Survey on Large Language Model Hallucination via a Creativity Perspective
Institute of Computing Technology, CAS（中国科学院计算技术研究所）；University of Chinese Academy of Science（中国科学院大学）；International Digital Economy Academy, IDEA Research（国际数字经济学院IDEA研究院）
GeoReason: Aligning Thinking And Answering In Remote Sensing Vision-Language Models Via Logical Consistency Reinforcement Learning
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；Key Laboratory of Target Cognition and Application Technology, Chinese Academy of Sciences（中国科学院目标认知与应用技术重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
