2 citations · 4 across the 24 of their papers we have counts for
27 papers
Thinking in Video: Can Video Generators Really Reason About the Real World?
Yongheng Zhang, Guang Yang, Ruihan Hou +12
Recent advances in world models and video generation have given rise to an emerging reasoning paradigm that leverages video generative models to simulate, predict, and reason about…
OMIBench: Benchmarking Olympiad-Level Multi-Image Reasoning in Large Vision-Language Model
Qiguang Chen, Chengyu Luan, Jiajun Wu +7
Large vision-language models (LVLMs) have made substantial advances in reasoning tasks at the Olympiad level. Nevertheless, current Olympiad-level multimodal reasoning benchmarks f…
Less Languages, Less Tokens: An Efficient Unified Logic Cross-lingual Chain-of-Thought Reasoning Framework
Chenyuan Zhang, Qiguang Chen, Xie Chen +6
Cross-lingual chain-of-thought (XCoT) with self-consistency markedly enhances multilingual reasoning, yet existing methods remain costly due to extensive sampling of full trajector…
Let's Think with Images Efficiently! An Interleaved-Modal Chain-of-Thought Reasoning Framework with Dynamic and Precise Visual Thoughts
Xu Liu, Yongheng Zhang, Qiguang Chen +3
Recently, Interleaved-modal Chain-of-Thought (ICoT) reasoning has achieved remarkable success by leveraging both multimodal inputs and outputs, attracting increasing attention. Whi…
Learning the Boundary of Solvability: Aligning LLMs to Detect Unsolvable Problems
Dengyun Peng, Qiguang Chen, Bofei Liu +6
Ensuring large language model (LLM) reliability requires distinguishing objective unsolvability (inherent contradictions) from subjective capability limitations (tasks exceeding mo…
The Molecular Structure of Thought: Mapping the Topology of Long Chain-of-Thought Reasoning
Qiguang Chen, Yantao Du, Ziniu Li +10
Large language models (LLMs) often fail to learn effective long chain-of-thought (Long CoT) reasoning from human or non-Long-CoT LLMs imitation. To understand this, we propose that…
From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence
Jian Yang, Xianglong Liu, Weifeng Lv +68
Large language models (LLMs) have fundamentally transformed automated software development by enabling direct translation of natural language descriptions into functional code, dri…
Beware of Reasoning Overconfidence: Pitfalls in the Reasoning Process for Multi-solution Tasks
Jiannan Guan, Qiguang Chen, Libo Qin +5
Large Language Models (LLMs) excel in reasoning tasks requiring a single correct answer, but they perform poorly in multi-solution tasks that require generating comprehensive and d…
MAC-SLU: Multi-Intent Automotive Cabin Spoken Language Understanding Benchmark
Yuezhang Peng, Chonghao Cai, Ziang Liu +10
Spoken Language Understanding (SLU), which aims to extract user semantics to execute downstream tasks, is a crucial component of task-oriented dialog systems. Existing SLU datasets…
The Universal Landscape of Human Reasoning
Qiguang Chen, Jinhao Liu, Libo Qin +14
Understanding how information is dynamically accumulated and transformed in human reasoning has long challenged cognitive psychology, philosophy, and artificial intelligence. Exist…
COIG-Writer: A High-Quality Dataset for Chinese Creative Writing with Thought Processes
Yunwen Li, Shuangshuang Ying, Xingwei Qu +16
Large language models exhibit systematic deficiencies in creative writing, particularly in non-English contexts where training data is scarce and lacks process-level supervision. W…
MMLongCite: A Benchmark for Evaluating Faithfulness of Long-Context Vision-Language Models
Keyan Zhou, Zecheng Tang, Lingfeng Ming +9
The rapid advancement of long-context vision language models (LCVLMs) has led to a significant expansion of their context windows. However, an extended context window does not guar…
Beyond Correctness: Evaluating Subjective Writing Preferences Across Cultures
Shuangshuang Ying, Yunwen Li, Xingwei Qu +21
Current preference learning methods achieve high accuracy on standard benchmarks but exhibit significant performance degradation when objective quality signals are removed. We intr…
Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
Wenbo Pan, Jie Xu, Qiguang Chen +5
Large Language Models (LLMs) should refuse to answer questions beyond their knowledge. This capability, which we term knowledge-aware refusal, is crucial for factual reliability, w…
MLDebugging: Towards Benchmarking Code Debugging Across Multi-Library Scenarios
Jinyang Huang, Xiachong Feng, Qiguang Chen +6
Code debugging is a crucial task in software engineering, which attracts increasing attention. While remarkable success has been made in the era of large language models (LLMs), cu…
X-WebAgentBench: A Multilingual Interactive Web Benchmark for Evaluating Global Agentic System
Peng Wang, Ruihan Tao, Qiguang Chen +2
Recently, large language model (LLM)-based agents have achieved significant success in interactive environments, attracting significant academic and industrial attention. Despite t…
RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning
Qiguang Chen, Libo Qin, Jinhao Liu +4
Chain-of-Thought (CoT) reasoning has proven effective in enhancing large language models (LLMs) on complex tasks, spurring research into its underlying mechanisms. However, two pri…
Visual Thoughts: A Unified Perspective of Understanding Multimodal Chain-of-Thought
Zihui Cheng, Qiguang Chen, Xiao Xu +8
Large Vision-Language Models (LVLMs) have achieved significant success in multimodal tasks, with multimodal chain-of-thought (MCoT) further enhancing performance and interpretabili…
DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective
Dengyun Peng, Yuhang Zhou, Qiguang Chen +3
Large Language Models (LLMs) have achieved remarkable success across diverse tasks, largely driven by well-designed prompts. However, crafting and selecting such prompts often requ…
Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models
Qiguang Chen, Libo Qin, Jinhao Liu +7
Recent advancements in reasoning with large language models (RLLMs), such as OpenAI-O1 and DeepSeek-R1, have demonstrated their impressive capabilities in complex domains like math…
Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thought
Qiguang Chen, Libo Qin, Jiaqi Wang +2
Chain-of-Thought (CoT) reasoning has emerged as a promising approach for enhancing the performance of large language models (LLMs) on complex reasoning tasks. Recently, a series of…
What Factors Affect Multi-Modal In-Context Learning? An In-Depth Exploration
Libo Qin, Qiguang Chen, Hao Fei +3
Recently, rapid advancements in Multi-Modal In-Context Learning (MM-ICL) have achieved notable success, which is capable of achieving superior performance across various tasks with…
KVSharer: Efficient Inference via Layer-Wise Dissimilar KV Cache Sharing
Yifei Yang, Zouying Cao, Qiguang Chen +4
The development of large language models (LLMs) has significantly expanded model sizes, resulting in substantial GPU memory requirements during inference. The key and value storage…
Wrong-of-Thought: An Integrated Reasoning Framework with Multi-Perspective Verification and Wrong Information
Yongheng Zhang, Qiguang Chen, Jingxuan Zhou +5
Chain-of-Thought (CoT) has become a vital technique for enhancing the performance of Large Language Models (LLMs), attracting increasing attention from researchers. One stream of a…