3 citations · 4 across the 11 of their papers we have counts for
12 papers
Of Errors and Echoes: Responsibility-Aware Credit Assignment for Tool-Use Agents
Xingjian Diao, Tianyu Yang, Wenjun Huang +8
Reinforcement learning improves tool-use agents, yet fine-grained credit assignment remains challenging even with step-level feedback. An observed error may be inherited from an ea…
DesignAgent3D: Interactive 3D Scene Editing via Designer-like Multimodal Reasoning
Xiujin Liu, Tianyu Yang, Yilun Zhao +1
Text guided 3D scene editing provides an intuitive interface for modifying reconstructed environments, but remains difficult because natural language design requests are often sema…
Reason Before You Retrieve: Agentic Planning for Multi-modal RAG
Tianyu Yang, Shir Simon, Zhenzhen Li +2
Multimodal retrieval-augmented generation (mRAG) aims to answer image-text queries with external knowledge, but most existing systems still retrieve directly from raw multimodal in…
TellOnce: Correcting Coding Agents Once, Improving Their Future Actions
Yujun Zhou, Kehan Guo, Haomin Zhuang +8
Interactive LLM agents increasingly work with users across tasks, yet remembering a previous correction does not ensure that the agent changes its behavior in the future. In a diag…
A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning
Tianyu Yang, Sihong Wu, Yilun Zhao +6
Multimodal Mathematical Reasoning (MMR) has recently attracted increasing attention for its capability to solve mathematical problems involving both textual and visual modalities.…
The Role of Computing Resources in Publishing Foundation Model Research
Yuexing Hao, Yue Huang, Haoran Zhang +8
Cutting-edge research in Artificial Intelligence (AI) requires considerable resources, including Graphics Processing Units (GPUs), data, and human resources. In this paper, we eval…
Evolving Language Models without Labels: Majority Drives Selection, Novelty Promotes Variation
Yujun Zhou, Zhenwen Liang, Haolin Liu +7
Large language models (LLMs) can improve through reinforcement learning with verifiable rewards (RLVR), but many settings lack reference answers or external verifiers. Label-free s…
SaSR-Net: Source-Aware Semantic Representation Network for Enhancing Audio-Visual Question Answering
Tianyu Yang, Yiyang Nan, Lisen Dai +3
Audio-Visual Question Answering (AVQA) is a challenging task that involves answering questions based on both auditory and visual information in videos. A significant challenge is i…
Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification
Zhenwen Liang, Ye Liu, Tong Niu +3
Despite significant advancements in the general capability of large language models (LLMs), they continue to struggle with consistent and accurate reasoning, especially in complex…
MathChat: Benchmarking Mathematical Reasoning and Instruction Following in Multi-Turn Interactions
Zhenwen Liang, Dian Yu, Wenhao Yu +4
Large language models (LLMs) have demonstrated impressive capabilities in mathematical problem solving, particularly in single turn question answering formats. However, real world…
Defending Jailbreak Prompts via In-Context Adversarial Game
Yujun Zhou, Yufei Han, Haomin Zhuang +4
Large Language Models (LLMs) demonstrate remarkable capabilities across diverse applications. However, concerns regarding their security, particularly the vulnerability to jailbrea…
Manipulating Predictions over Discrete Inputs in Machine Teaching
Xiaodong Wu, Yufei Han, Hayssam Dahrouj +3
Machine teaching often involves the creation of an optimal (typically minimal) dataset to help a model (referred to as the `student') achieve specific goals given by a teacher. Whi…