activity
20242026
collaborators

7 papers

cs.LG2026

Not All Tokens Are Worth Caching: Learning Semantic-Aware Eviction for LLM Prefix Caches

Shaoke Fang, Ziang Li, Wenfei Wu +3

Prefix caching is a key optimization in Large Language Model (LLM) serving, reusing attention Key-Value (KV) states across requests with shared prompt prefixes to reduce expensive…

cs.CR2026

How Real is Your Jailbreak? Fine-grained Jailbreak Evaluation with Anchored Reference

Songyang Liu, Chaozhuo Li, Rui Pu +5

Jailbreak attacks present a significant challenge to the safety of Large Language Models (LLMs), yet current automated evaluation methods largely rely on coarse classifications tha…

cs.CL2026

LANCET: Neural Intervention via Structural Entropy for Mitigating Faithfulness Hallucinations in LLMs

Chenxu Wang, Chaozhuo Li, Pengbo Wang +7

Large Language Models have revolutionized information processing, yet their reliability is severely compromised by faithfulness hallucinations. While current approaches attempt to…

cs.AI2025

Beyond Surface-Level Detection: Towards Cognitive-Driven Defense Against Jailbreak Attacks via Meta-Operations Reasoning

Rui Pu, Chaozhuo Li, Rui Ha +3

Defending large language models (LLMs) against jailbreak attacks is essential for their safe and reliable deployment. Existing defenses often rely on shallow pattern matching, whic…

cs.AI2025

From "Aha Moments" to Controllable Thinking: Toward Meta-Cognitive Reasoning in Large Reasoning Models via Decoupled Reasoning and Control

Rui Ha, Rui Pu, Chaozhuo Li +2

Large Reasoning Models (LRMs) can exhibit step-by-step reasoning, reflection, and backtracking, but these behaviors are often unregulated, leading to overthinking. As a result, LRM…

cs.CR2025

MirrorShield: Towards Universal Defense Against Jailbreaks via Entropy-Guided Mirror Crafting

Rui Pu, Chaozhuo Li, Rui Ha +3

Defending large language models (LLMs) against jailbreak attacks is crucial for ensuring their safe deployment. Existing defense strategies typically rely on predefined static crit…

cs.CR2024

Feint and Attack: Attention-Based Strategies for Jailbreaking and Protecting LLMs

Rui Pu, Chaozhuo Li, Rui Ha +5

Jailbreak attack can be used to access the vulnerabilities of Large Language Models (LLMs) by inducing LLMs to generate the harmful content. And the most common method of the attac…