8 citations · 17 across the 29 of their papers we have counts for
17 papers · 1 filter
TRACER: Verifiable Generative Provenance for Multimodal Tool-Using Agents
Bihui Yu, Caijun Jia, Jing Chi +6
Multimodal large language models increasingly solve vision-centric tasks by calling external tools for visual inspection, OCR, retrieval, calculation, and multi-step reasoning. Cur…
How RL Unlocks the Aha Moment in Geometric Interleaved Reasoning
Xiangxiang Zhang, Caijun Jia, Siyuan Li +9
Solving complex geometric problems inherently requires interleaved reasoning: a tight alternation between constructing diagrams and performing logical deductions. Although recent M…
Canvas-of-Thought: Grounding Reasoning via Mutable Structured States
Lingzhuang Sun, Yuxia Zhu, Ruitong Liu +10
While Chain-of-Thought (CoT) prompting has significantly advanced the reasoning capabilities of Multimodal Large Language Models (MLLMs), relying solely on linear text sequences re…
Guided Verifier: Collaborative Multimodal Reasoning via Dynamic Process Supervision
Lingzhuang Sun, Ruitong Liu, Yuxia Zhu +5
Reinforcement Learning (RL) has emerged as a pivotal mechanism for enhancing the complex reasoning capabilities of Multimodal Large Language Models (MLLMs). However, prevailing par…
Thinking with Drafting: Optical Decompression via Logical Reconstruction
Jingxuan Wei, Honghao He, Caijun Jia +9
Existing multimodal large language models have achieved high-fidelity visual perception and exploratory visual generation. However, a precision paradox persists in complex reasonin…
GenProve: Learning to Generate Text with Fine-Grained Provenance
Jingxuan Wei, Xingyue Wang, Yanghaoyu Liao +5
Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as users struggle to verify how a c…
ResearchPulse: Building Method-Experiment Chains through Multi-Document Scientific Inference
Qi Chen, Jingxuan Wei, Zhuoya Yao +5
Understanding how scientific ideas evolve requires more than summarizing individual papers-it demands structured, cross-document reasoning over thematically related research. In th…
LLaVA-NeuMT: Selective Layer-Neuron Modulation for Efficient Multilingual Multimodal Translation
Jingxuan Wei, Caijun Jia, Qi Chen +5
Multimodal Machine Translation (MMT) enhances translation quality by incorporating visual context, helping to resolve textual ambiguities. While existing MMT methods perform well i…
ChartReasoner: Code-Driven Modality Bridging for Long-Chain Reasoning in Chart Question Answering
Caijun Jia, Nan Xu, Jingxuan Wei +4
Recently, large language models have shown remarkable reasoning capabilities through long-chain reasoning before responding. However, how to extend this capability to visual reason…
ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering
Jingxuan Wei, Nan Xu, Junnan Zhu +4
Chart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models. While early approaches have shown promisin…
MM-Verify: Enhancing Multimodal Reasoning with Chain-of-Thought Verification
Linzhuang Sun, Hao Liang, Jingxuan Wei +5
According to the Test-Time Scaling, the integration of External Slow-Thinking with the Verify mechanism has been demonstrated to enhance multi-round reasoning in large language mod…
BEATS: Optimizing LLM Mathematical Capabilities with BackVerify and Adaptive Disambiguate based Efficient Tree Search
Linzhuang Sun, Hao Liang, Jingxuan Wei +4
Large Language Models (LLMs) have exhibited exceptional performance across a broad range of tasks and domains. However, they still encounter difficulties in solving mathematical pr…
Synth-Empathy: Towards High-Quality Synthetic Empathy Data
Hao Liang, Linzhuang Sun, Jingxuan Wei +5
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capabilities has become a crucial prerequisite. Consequently,…
Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
Linzhuang Sun, Hao Liang, Jingxuan Wei +4
In recent years, with the rapid advancements in large language models (LLMs), achieving excellent empathetic response capability has become a crucial prerequisite. Consequently, ma…
Sentence-Level or Token-Level? A Comprehensive Study on Knowledge Distillation
Jingxuan Wei, Linzhuang Sun, Yichong Leng +3
Knowledge distillation, transferring knowledge from a teacher model to a student model, has emerged as a powerful technique in neural machine translation for compressing models or…
Unraveling Key Factors of Knowledge Distillation
Jingxuan Wei, Linzhuang Sun, Xu Tan +2
Knowledge distillation, a technique for model compression and performance enhancement, has gained significant traction in Neural Machine Translation (NMT). However, existing resear…
A Survey on Image-text Multimodal Models
Ruifeng Guo, Jingxuan Wei, Linzhuang Sun +7
With the significant advancements of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), the development of image-text multimodal models has garnered wi…