activity
20242026
most citedFact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification

1 citations · 2 across the 4 of their papers we have counts for

collaborators

6 papers

cs.AI2026

Distilling Agentic Systems: A Roadmap across Models, Artifacts, and Harnesses

Ziluowen Luo, Senzhang Wang, Chaozhuo Li +9

Modern agents increasingly rely on memories, tools, and execution logic, so their competence extends beyond model parameters. This shift exposes a limitation of conventional knowle…

cs.CR2026

ClawKeeper: Comprehensive Safety Protection for OpenClaw Agents Through Skills, Plugins, and Watchers

Songyang Liu, Chaozhuo Li, Chenxu Wang +8

OpenClaw has rapidly established itself as a leading open-source autonomous agent runtime, offering powerful capabilities including tool integration, local file access, and shell c…

cs.CL2026★ 1 cited

The Devil Behind Moltbook: Anthropic Safety is Always Vanishing in Self-Evolving AI Societies

Chenxu Wang, Chaozhuo Li, Songyang Liu +10

The emergence of multi-agent systems built from large language models (LLMs) offers a promising paradigm for scalable collective intelligence and self-evolution. Ideally, such syst…

cs.AI2025★ 1 cited

Fact in Fragments: Deconstructing Complex Claims via LLM-based Atomic Fact Extraction and Verification

Liwen Zheng, Chaozhuo Li, Zheng Liu +4

Fact verification plays a vital role in combating misinformation by assessing the veracity of claims through evidence retrieval and reasoning. However, traditional methods struggle…

cs.CL2025

Loki's Dance of Illusions: A Comprehensive Survey of Hallucination in Large Language Models

Chaozhuo Li, Pengbo Wang, Chenxu Wang +7

Edgar Allan Poe noted, "Truth often lurks in the shadow of error," highlighting the deep complexity intrinsic to the interplay between truth and falsehood, notably under conditions…

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…