3 citations · 3 across the 18 of their papers we have counts for
11 papers · 1 filter
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…