23 citations · 156 across the 51 of their papers we have counts for
17 papers · 1 filter
EpiGuard: An Autonomous Infectious-Disease Early Warning Agent Platform
Tonglong Wei, Zheng Yuan, Lingshuai Lin +3
Infectious disease outbreaks often spread rapidly before signs of widespread transmission become apparent, leaving a big challenge for rapid response and effective intervention. Ho…
OpenESM: Energy-Steered Models Are Scalable Foundation Learners
Yuan Pu, Yudong Lu, Ting Wang +6
Energy-based models have shown promising properties for domain-specific tasks but remain challenging in modeling general-purpose scenarios. To bridge this gap, we design energy-ste…
Before Agent Tells The Lie: Has Deception Already Been Represented?
Xinling Li, Dadi Guo, Qingyu Liu +5
Large language model (LLM)-based agents can exhibit deceptive behavior during task execution, including hiding failures, fabricating results, or falsely signaling task completion.…
Faithful Activation Verbalization: Reducing Hallucinations in LLM Representation Interpretation
Haiyan Zhao, Zirui Hei, Wei Shi +3
Activation verbalization methods such as Activation Oracle and Natural Language Autoencoders decode hidden representations of large language models into human-readable natural lang…
: An End-to-End Agent Auditing Engine
Haoning Wang, Mingxun Zhang, Chenyue Yu +5
With the rapid advancement of large language models (LLMs), harnesses have become essential infrastructure for deploying agents across a wide range of domains. The fast-evolving ha…
Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence
Yuyuan Feng, Zhishang Xiang, Chaobin Yang +32
LLMs have evolved from language generators to autonomous agents capable of complex, long-horizon tasks. This evolution has produced paradigms including Prompt Engineering to elicit…
Do LLMs Know Their Vulnerable Scenarios?
Ziheng Peng, Huiqi Deng, Haoran Jing +5
Safety-aligned large language models are trained to refuse harmful requests, yet embedding the same requests in particular scenarios can bypass their safeguards. Existing red-teami…
Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control
Haoze Liu, Run Liu, Haiying Xu +6
Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making. Existing LLM per…
CAi Copilot: Reducing Operational Workload in Molecular Design through Intent-Driven Agentic Workflows
Zhu Wang, Jiangyu Chen, Yingjun Shang +4
Early-stage molecular design is an iterative process, not just a task of generating molecules. Researchers turn broad goals into design strategies, refine candidates, assess many p…
RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning
Yongliang Miao, Fengyuan Liu, Wei Shi +4
Supervised fine-tuning (SFT) is a prevailing method for adapting large language models to reasoning tasks by imitating offline expert demonstrations, often treating a single expert…
PrivacyPeek: Auditing What LLM-Based Agents Acquire, Not Just What They Say
Mingxuan Zhang, Jiahui Han, Dadi Guo +5
LLM-based agents are rapidly advancing, autonomously invoking external tools to complete multi-step tasks for users. However, agents often acquire more sensitive information than t…
SkillsInjector: Dynamic Skill Context Construction for LLM Agents
Yanchao Li, Wanhao Liu, Ben Gao +5
LLM agents now draw on growing skill libraries to handle complex tasks. However, injecting more skills does not always improve task completion and can even degrade it. Existing met…
To Call or Not to Call: Diagnosing Intrinsic Over-Calling Bias in LLM Agents
Wei Shi, Ziheng Peng, Sihang Li +4
LLM agents exhibit a consistent tendency to over-call, invoking tools even in situations where none is needed. On the When2Call benchmark, six models from three families show high…
What Do EEG Foundation Models Capture from Human Brain Signals?
Ling Tang, Qian Chen, Jilin Mei +6
Clinical electroencephalogram (EEG) analysis rests on a hand-crafted feature catalog refined over decades, \emph{e.g.,} band power, connectivity, complexity, and more. Modern EEG f…
Epistemic Traps: Rational Misalignment Driven by Model Misspecification
Xingcheng Xu, Jingjing Qu, Qiaosheng Zhang +4
The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent behavioral pathologies including sycophancy, ha…
Cleansing the Artificial Mind: A Self-Reflective Detoxification Framework for Large Language Models
Kaituo Zhang, Zhimeng Jiang, Na Zou
Recent breakthroughs in Large Language Models (LLMs) have revealed remarkable generative capabilities and emerging self-regulatory mechanisms, including self-correction and self-re…
A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data
Kaituo Zhang, Mingzhi Hu, Hoang Anh Duy Le +9
Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities. By transforming data from a scarce resource into a controllable asset, LL…