6 citations · 9 across the 17 of their papers we have counts for
18 papers
RARE: Decoupling Representation Steering from Expert Routing in Mixture-of-Experts Language Models
Zhibo Zhang, Zhen Ouyang, Ling Shi +1
Representation engineering offers a lightweight means of controlling language-model behavior by modifying intermediate hidden states, but its direct application to Mixture-of-Exper…
TRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
Zhibo Zhang, Zhen Ouyang, Ling Shi +1
Agent Skills package reusable natural language procedures with executable resources, enabling software agents to acquire task specific capabilities without model adaptation. Automa…
Defense Against LLM Backdoors using Critical Neuron Isolation Pruning
Yuxi Li, Zhibo Zhang, Kailong Wang +3
Large language models (LLMs) are vulnerable to backdoor attacks, where hidden triggers induce malicious outputs. Existing defenses generally fall into inference-time detection or t…
A Temporal Reasoning Benchmarking Framework for LRMs via Difficulty-controlled and Dynamic Test Generation
Shide Zhou, Kailong Wang, Ling Shi +1
Defining the reasoning boundaries and ensuring the reliability of Large Reasoning Models (LRMs) remains a critical challenge. Current benchmarks primarily rely on static datasets s…
R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
Aijia Cheng, Kailong Wang, Ling Shi +1
Function calling empowers large language models (LLMs) to interface with external tools, yet existing RL-based approaches suffer from misalignment between reasoning processes and t…
RASET: Router-Agnostic Safety-Critical Expert Tuning Exposes Localized Safety Enforcement Failures in Mixture-of-Experts LLMs
Zhibo Zhang, Yuxi Li, Zhen Ouyang +2
Mixture-of-Experts (MoE) LLMs rely on sparse, router-driven expert activation, yet how safety alignment interacts with routed expert specialization remains underexplored. A common…
STEAMROLLER: A Multi-Agent System for Inclusive Automatic Speech Recognition for People who Stutter
Ziqi Xu, Yi Liu, Yuekang Li +3
People who stutter (PWS) face systemic exclusion in today's voice-driven society, where access to voice assistants, authentication systems, and remote work tools increasingly depen…
Beyond Correctness: Exposing LLM-generated Logical Flaws in Reasoning via Multi-step Automated Theorem Proving
Xinyi Zheng, Ningke Li, Xiaokun Luan +4
Large Language Models (LLMs) have demonstrated impressive reasoning capabilities, leading to their adoption in high-stakes domains such as healthcare, law, and scientific research.…
Boosting Pointer Analysis With LLM-Enhanced Allocation Function Detection
Baijun Cheng, Kailong Wang, Ling Shi +5
Pointer analysis is foundational for many static analysis tasks, yet its effectiveness is often hindered by imprecise modeling of heap allocations, particularly in C/C++ programs w…
Enhancing Semantic Understanding in Pointer Analysis using Large Language Models
Baijun Cheng, Kailong Wang, Ling Shi +4
Pointer analysis has been studied for over four decades. However, existing frameworks continue to suffer from the propagation of incorrect facts. A major limitation stems from thei…
Circumventing Safety Alignment in Large Language Models Through Embedding Space Toxicity Attenuation
Zhibo Zhang, Yuxi Li, Kailong Wang +3
Large Language Models (LLMs) have achieved remarkable success across domains such as healthcare, education, and cybersecurity. However, this openness also introduces significant se…
Exposing the Ghost in the Transformer: Abnormal Detection for Large Language Models via Hidden State Forensics
Shide Zhou, Kailong Wang, Ling Shi +1
The widespread adoption of Large Language Models (LLMs) in critical applications has introduced severe reliability and security risks, as LLMs remain vulnerable to notorious threat…
Detecting LLM Fact-conflicting Hallucinations Enhanced by Temporal-logic-based Reasoning
Ningke Li, Yahui Song, Kailong Wang +4
Large language models (LLMs) face the challenge of hallucinations -- outputs that seem coherent but are actually incorrect. A particularly damaging type is fact-conflicting halluci…
Model-Editing-Based Jailbreak against Safety-aligned Large Language Models
Yuxi Li, Zhibo Zhang, Kailong Wang +2
Large Language Models (LLMs) have transformed numerous fields by enabling advanced natural language interactions but remain susceptible to critical vulnerabilities, particularly ja…
GlitchProber: Advancing Effective Detection and Mitigation of Glitch Tokens in Large Language Models
Zhibo Zhang, Wuxia Bai, Yuxi Li +6
Large language models (LLMs) have achieved unprecedented success in the field of natural language processing. However, the black-box nature of their internal mechanisms has brought…
Understanding the Effectiveness of Coverage Criteria for Large Language Models: A Special Angle from Jailbreak Attacks
Shide Zhou, Tianlin Li, Kailong Wang +4
Large language models (LLMs) have revolutionized artificial intelligence, but their increasing deployment across critical domains has raised concerns about their abnormal behaviors…
Semantic-Enhanced Indirect Call Analysis with Large Language Models
Baijun Cheng, Cen Zhang, Kailong Wang +6
In contemporary software development, the widespread use of indirect calls to achieve dynamic features poses challenges in constructing precise control flow graphs (CFGs), which fu…
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing
Shide Zhou, Tianlin Li, Yihao Huang +4
Deep Neural networks (DNNs), extensively applied across diverse disciplines, are characterized by their integrated and monolithic architectures, setting them apart from conventiona…