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20242026
most citedSUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems

1 citations · 1 across the 9 of their papers we have counts for

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cs.CR2026

Sleeping Secrets: How Fine-Tuning Reawakens Privacy Risks in Language Models

Jianhong Li, Jiahao Chen, Yuwen Pu +6

Beyond adapting Large Language Models (LLMs) to specialized applications, fine-tuning has recently been shown to recover private information that is no longer accessible through di…

cs.CR2026

Safety in Self-Evolving Agents: A Survey

Jiahao Chen, Zhou Feng, Oubo Ma +28

Large language models (LLMs) exhibit strong general capabilities, yet their parameters typically remain fixed after deployment, limiting learning from new interactions. In open-end…

cs.CR2026

A Finger on the Scale: Covert Policy Steering through Agentic Skills

Jiarui Li, Jiahao Chen, Chunyi Zhou +5

Reusable agent skills extend large language model (LLM) agents with task procedures, tool-use guidance, and output constraints. Yet these skills also act as externalized behavioral…

cs.CR2026

The Shape of Ownership: Verifying LLM Provenance through Semantic Structures

Zhongrui Sun, Jiahao Chen, Oubo Ma +4

As large language models (LLMs) are increasingly redistributed, adapted, and served behind opaque APIs, model ownership can no longer be established reliably by inspecting model in…

cs.LG2026

Angel or Demon: Investigating the Plasticity Interventions' Impact on Backdoor Threats in Deep Reinforcement Learning

Oubo Ma, Ruixiao Lin, Yang Dai +4

Extensive research has highlighted the severe threats posed by backdoor attacks to deep reinforcement learning (DRL). However, prior studies primarily focus on vanilla scenarios, w…

cs.CR2026

IntraGuard: Committee-Side Defenses Against Review Outsourcing to Commercial Chatbots

Oubo Ma, Ruixiao Lin, Jiahao Chen +3

LLMs become increasingly capable, editorial boards and program committees are growing concerned about reviewers who fully outsource peer review to commercial chatbots. This concern…