2 citations · 3 across the 15 of their papers we have counts for
15 papers
ReverseEOL: Improving Training-free Text Embeddings via Text Reversal in Decoder-only LLMs
Ailiang Lin, Zhuoyun Li, Yusong Wang +3
Recent advances in Large Language Models (LLMs) have opened new avenues for generating training-free text embeddings. However, the causal attention in decoder-only LLMs prevents ea…
QoEReasoner: An Agentic Reasoning Framework for Automated and Explainable QoE Diagnosis in RANs
Qizhe Li, Haolong Chen, Shan Dai +5
Diagnosing Quality-of-Experience (QoE) degradations in operational Radio Access Networks (RANs) is a critical but notoriously complex task, traditionally requiring labor-intensive…
SCOPE: Sequential Conformal Probing for Reliable OOD Rejection in LLM Services
Zhuoyun Li, Boxuan Wang, Changshun Wu +2
Rejecting inputs outside the defined in-distribution (IND) service scope is critical for large language model (LLM) services, where unsupported requests should be filtered before f…
Dive into Ambiguity: A*-Inspired Multi-Agents Commonsense Obfuscation Attack on LLM Prompts
Boxuan Wang, Zhuoyun Li, Xiaowei Huang +1
Large language models (LLMs) excel in reasoning and knowledge-intensive tasks but remain vulnerable to prompt-level adversarial attacks that preserve intent while triggering common…
Safety-Constrained Reinforcement Learning with Post-Training Reachability Verification for Robot Navigation
Qisong He, Xinmiao Huang, Jinwei Hu +4
Safe navigation for mobile robots demands policies that remain reliable under the high-consequence perception uncertainty of cluttered environments. Yet most existing safe reinforc…
Where Do Prompt Perturbations Break Generation? A Segment-Level View of Robustness in LoRA-Tuned Language Models
Zhuoyun Li, Boxuan Wang, Jinwei Hu +6
Large language models are sensitive to minor prompt perturbations, yet existing robustness methods usually enforce consistency at the whole-sequence level. This holistic view can h…
Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders
Ailiang Lin, Zhuoyun Li, Keyu Mao +2
Large language models (LLMs) have been widely explored for embedding generation. While recent studies show that in-context learning (ICL) effectively enhances the representational…
FragileFlow: Spectral Control of Correct-but-Fragile Predictions for Foundation Model Robustness
Zhuoyun Li, Boxuan Wang, Jinwei Hu +2
Robust adaptation of LLMs and VLMs is often evaluated by average accuracy or average consistency under perturbations. However, these averages can hide a structured failure mode: a…
Jailbreaking Vision-Language Models Through the Visual Modality
Aharon Azulay, Jan Dubiński, Zhuoyun Li +2
The visual modality of vision-language models (VLMs) is an underexplored attack surface for bypassing safety alignment. We introduce four jailbreak attacks exploiting the vision co…
Rethinking Multi-Agent Intelligence Through the Lens of Small-World Networks
Boxuan Wang, Zhuoyun Li, Xiaowei Huang +1
Large language models (LLMs) have enabled multi-agent systems (MAS) in which multiple agents argue, critique, and coordinate to solve complex tasks, making communication topology a…
Chain-of-Thought as a Lens: Evaluating Structured Reasoning Alignment between Human Preferences and Large Language Models
Boxuan Wang, Zhuoyun Li, Xinmiao Huang +2
This paper primarily demonstrates a method to quantitatively assess the alignment between multi-step, structured reasoning in large language models and human preferences. We introd…
Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignment
Jinwei Hu, Yi Dong, Shuang Ao +6
LLM-powered Multi-Agent Systems (LLM-MAS) unlock new potentials in distributed reasoning, collaboration, and task generalization but also introduce additional risks due to unguaran…
Spatial-DISE: A Unified Benchmark for Evaluating Spatial Reasoning in Vision-Language Models
Xinmiao Huang, Qisong He, Zhenglin Huang +5
Spatial reasoning ability is crucial for Vision Language Models (VLMs) to support real-world applications in diverse domains including robotics, augmented reality, and autonomous n…
Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token
Ailiang Lin, Zhuoyun Li, Yusong Wang +2
Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks. To overcome the inherent limitations of causal attention in re…
Position: Towards a Responsible LLM-empowered Multi-Agent Systems
Jinwei Hu, Yi Dong, Shuang Ao +6
The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation. Tools like LangChain a…