3 papers
cs.CR2026
FedGraph: Defending Federated Large Language Model Fine-Tuning Against Backdoor Attacks via Graph-Based Aggregation
Xi Chen, Chunyi Zhou, Rui Zeng +3
Federated fine-tuning of large language models (LLMs) allows multiple clients to collaboratively adapt models without sharing raw data, offering strong privacy benefits. However, t…
cs.AI2026
From Existing Methods to New Research Ideas: Two Perspectives on Recursive Self-Improvement
Leijun Zhou, Yanke Yu, Zhihao Liu +10
Recursive self-improvement (RSI) offers a route to AI systems that participate in their own continued development. Recent methods allow language agents to retain changes to their p…
cs.AI2026
GDPevo: Evaluating Agent Self-Evolution on Real Business Tasks
Leijun Zhou, Zhihao Liu, Xiang Qu +9
Agent self-evolution updates an agent's persistent state from prior experience and reuses it to solve related tasks more effectively. Evaluating self-evolution is difficult: existi…