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20242026
most citedA Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

3 citations · 3 across the 15 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

The Why Behind the Action: Unveiling Internal Drivers via Agentic Attribution

Chen Qian, Peng Wang, Dongrui Liu +10

Large Language Model (LLM)-based agents are widely used in real-world applications such as customer service, web navigation, and software engineering. As these systems become more…

cs.AI2026

AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security

Dongrui Liu, Qihan Ren, Chen Qian +40

The rise of AI agents introduces complex safety and security challenges arising from autonomous tool use and environmental interactions. Current guardrail models lack agentic risk…

cs.AI2025

Are Your Agents Upward Deceivers?

Dadi Guo, Qingyu Liu, Dongrui Liu +13

Large Language Model (LLM)-based agents are increasingly used as autonomous subordinates that carry out tasks for users. This raises the question of whether they may also engage in…

cs.AI20253 cited

A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence

Huan-ang Gao, Jiayi Geng, Wenyue Hua +24

Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks but remain fundamentally static, unable to adapt their internal parameters to novel task…

cs.AI2025

Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution

Jiahao Qiu, Xuan Qi, Tongcheng Zhang +15

Recent advances in large language models (LLMs) have enabled agents to autonomously perform complex, open-ended tasks. However, many existing frameworks depend heavily on manually…

cs.AI2024

Evaluating the Correctness of Inference Patterns Used by LLMs for Judgment

Lu Chen, Yuxuan Huang, Yixing Li +6

This paper presents a method to analyze the inference patterns used by Large Language Models (LLMs) for judgment in a case study on legal LLMs, so as to identify potential incorrec…