3 citations · 3 across the 18 of their papers we have counts for
19 papers
VIDEAS: Distilling Explicit Action Semantics from Demonstration Videos for World Models via Prior-Guided Simulation
Jianan Wang, Haoquan Zhai, Siyang Zhang +7
World models learn internal representations of environment dynamics to predict future states, enabling agents to optimize action plans without physical interactions. However, devel…
GANDR: Claim Auditing for Verifiable Legal Answer Generation
Chen Qian, Yimeng Wang, Yu Chen +2
In high-stakes domains such as legal practice, a language-model answer is only useful to the extent that a reader can verify each claim against the source the system cites. Current…
AtomCite: Verification and Correction of Supplied Page-Level Citations in Multi-Page Documents
Chen Qian, Yimeng Wang, Yu Chen +2
Large language models answering questions over multi-page documents are expected to cite the supporting pages, yet supplied citations are sometimes inaccurate, and current evaluati…
Tool-R0: Self-Evolving LLM Agents for Tool-Learning from Zero Data
Emre Can Acikgoz, Cheng Qian, Jonas Hübotter +3
Large language models (LLMs) are becoming the foundation for autonomous agents that can use tools to solve complex tasks. Reinforcement learning (RL) has emerged as a common approa…
TabSieve: Explicit In-Table Evidence Selection for Tabular Prediction
Yongyao Wang, Ziqi Miao, Lu Yang +4
Tabular prediction can benefit from in-table rows as few-shot evidence, yet existing tabular models typically perform instance-wise inference and LLM-based prompting is often britt…
Steer2Adapt: Dynamically Composing Steering Vectors Elicits Efficient Adaptation of LLMs
Pengrui Han, Xueqiang Xu, Keyang Xuan +12
Activation steering has emerged as a promising approach for efficiently adapting large language models (LLMs) to downstream behaviors. However, most existing steering methods rely…
AgentIF-OneDay: A Task-level Instruction-Following Benchmark for General AI Agents in Daily Scenarios
Kaiyuan Chen, Qimin Wu, Taiyu Hou +42
The capacity of AI agents to effectively handle tasks of increasing duration and complexity continues to grow, demonstrating exceptional performance in coding, deep research, and c…
LogicEnvGen: Task-Logic Driven Generation of Diverse Simulated Environments for Embodied AI
Jianan Wang, Siyang Zhang, Bin Li +4
Simulated environments play an essential role in embodied AI, functionally analogous to test cases in software engineering. However, existing environment generation methods often e…
Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction
Xingjie Gao, Pengcheng Huang, Zhenghao Liu +6
Equipping Large Language Models (LLMs) with external tools enables them to solve complex real-world problems. However, the robustness of existing methods remains a critical challen…
From "Thinking" to "Justifying": Aligning High-Stakes Explainability with Professional Communication Standards
Chen Qian, Yimeng Wang, Yu Chen +2
Explainable AI (XAI) in high-stakes domains should help stakeholders trust and verify system outputs. Yet Chain-of-Thought methods reason before concluding, and logical gaps or hal…
Beyond the Black Box: A Survey on the Theory and Mechanism of Large Language Models
Zeyu Gan, Ruifeng Ren, Wei Yao +9
The rapid emergence of Large Language Models (LLMs) has precipitated a profound paradigm shift in Artificial Intelligence, delivering monumental engineering successes that increasi…
VITA-E: Natural Embodied Interaction with Concurrent Seeing, Hearing, Speaking, and Acting
Xiaoyu Liu, Chaoyou Fu, Chi Yan +15
Current Vision-Language-Action (VLA) models are often constrained by a rigid, static interaction paradigm, which lacks the ability to see, hear, speak, and act concurrently as well…
Composing Policy Gradients and Prompt Optimization for Language Model Programs
Noah Ziems, Dilara Soylu, Lakshya A Agrawal +10
Group Relative Policy Optimization (GRPO) has proven to be an effective tool for post-training language models (LMs). However, AI systems are increasingly expressed as modular prog…
EcoLANG: Efficient and Effective Agent Communication Language Induction for Social Simulation
Xinyi Mou, Chen Qian, Wei Liu +2
Large language models (LLMs) have demonstrated an impressive ability to role-play humans and replicate complex social dynamics. While large-scale social simulations are gaining inc…
MASLab: A Unified and Comprehensive Codebase for LLM-based Multi-Agent Systems
Rui Ye, Keduan Huang, Qimin Wu +17
LLM-based multi-agent systems (MAS) have demonstrated significant potential in enhancing single LLMs to address complex and diverse tasks in practical applications. Despite conside…
NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning
Wei Liu, Siya Qi, Xinyu Wang +3
Recent advances such as DeepSeek R1-Zero highlight the effectiveness of incentive training, a reinforcement learning paradigm that computes rewards solely based on the final answer…
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering
Shuzheng Si, Haozhe Zhao, Gang Chen +9
Training LLMs on data containing unfamiliar knowledge during the instruction tuning stage can encourage hallucinations. To address this challenge, we introduce NOVA, a novel framew…
Practical token pruning for foundation models in few-shot conversational virtual assistant systems
Haode Qi, Cheng Qian, Jian Ni +6
In an enterprise Virtual Assistant (VA) system, intent classification is the crucial component that determines how a user input is handled based on what the user wants. The VA syst…
A Study on the Vulnerability of Test Questions against ChatGPT-based Cheating
Shanker Ram, Chen Qian
ChatGPT is a chatbot that can answer text prompts fairly accurately, even performing very well on postgraduate-level questions. Many educators have found that their take-home or re…