2 citations · 5 across the 26 of their papers we have counts for
15 papers · 1 filter
When Modalities Conflict: How Unimodal Reasoning Uncertainty Governs Preference Dynamics in MLLMs
Zhuoran Zhang, Tengyue Wang, Xilin Gong +4
Multimodal large language models (MLLMs) must resolve conflicts when different modalities provide contradictory information, a process we term modality following. Prior work measur…
Environment Inference for Learning Generalizable Dynamical System
Shixuan Liu, Yue He, Haotian Wang +4
Data-driven methods offer efficient and robust solutions for analyzing complex dynamical systems but rely on the assumption of I.I.D. data, driving the development of generalizatio…
BaseReward: A Strong Baseline for Multimodal Reward Model
Yi-Fan Zhang, Haihua Yang, Huanyu Zhang +11
The rapid advancement of Multimodal Large Language Models (MLLMs) has made aligning them with human preferences a critical challenge. Reward Models (RMs) are a core technology for…
OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing
Zhihong Chen, Xuehai Bai, Yang Shi +9
The performance of unified multimodal models for image generation and editing is fundamentally constrained by the quality and comprehensiveness of their training data. While existi…
RealUnify: Do Unified Models Truly Benefit from Unification? A Comprehensive Benchmark
Yang Shi, Yuhao Dong, Yue Ding +22
The integration of visual understanding and generation into unified multimodal models represents a significant stride toward general-purpose AI. However, a fundamental question rem…
AgriEval: A Comprehensive Chinese Agricultural Benchmark for Large Language Models
Lian Yan, Haotian Wang, Chen Tang +5
In the agricultural domain, the deployment of large language models (LLMs) is hindered by the lack of training data and evaluation benchmarks. To mitigate this issue, we propose Ag…
AM-Thinking-v1: Advancing the Frontier of Reasoning at 32B Scale
Yunjie Ji, Xiaoyu Tian, Sitong Zhao +5
We present AM-Thinking-v1, a 32B dense language model that advances the frontier of reasoning, embodying the collaborative spirit of open-source innovation. Outperforming DeepSeek-…
Not All Correct Answers Are Equal: Why Your Distillation Source Matters
Xiaoyu Tian, Yunjie Ji, Haotian Wang +5
Distillation has emerged as a practical and effective approach to enhance the reasoning capabilities of open-source language models. In this work, we conduct a large-scale empirica…
DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Training
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Although large language models (LLMs) have recently achieved remarkable performance on various complex reasoning benchmarks, the academic community still lacks an in-depth understa…
Exploring the Potential of Offline RL for Reasoning in LLMs: A Preliminary Study
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Despite significant advances in long-context reasoning by large language models (LLMs), primarily through Online Reinforcement Learning (RL) methods, these approaches incur substan…
Leveraging Reasoning Model Answers to Enhance Non-Reasoning Model Capability
Haotian Wang, Han Zhao, Shuaiting Chen +5
Recent advancements in large language models (LLMs), such as DeepSeek-R1 and OpenAI-o1, have demonstrated the significant effectiveness of test-time scaling, achieving substantial…
Dual Boost-Driven Graph-Level Clustering Network
John Smith, Wenxuan Tu, Junlong Wu +10
Graph-level clustering remains a pivotal yet formidable challenge in graph learning. Recently, the integration of deep learning with representation learning has demonstrated notabl…
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study
Yunjie Ji, Sitong Zhao, Xiaoyu Tian +5
Enhancing the reasoning capabilities of Large Language Models (LLMs) with efficiency and scalability remains a fundamental challenge in artificial intelligence research. This paper…
Think Twice: Enhancing LLM Reasoning by Scaling Multi-round Test-time Thinking
Xiaoyu Tian, Sitong Zhao, Haotian Wang +5
Recent advances in large language models (LLMs), such as OpenAI-o1 and DeepSeek-R1, have demonstrated the effectiveness of test-time scaling, where extended reasoning processes sub…
1.4 Million Open-Source Distilled Reasoning Dataset to Empower Large Language Model Training
Han Zhao, Haotian Wang, Yiping Peng +5
The AM-DeepSeek-R1-Distilled is a large-scale dataset with thinking traces for general reasoning tasks, composed of high-quality and challenging reasoning problems. These problems…