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20242026
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8 papers · 1 filter

cs.CR2026

ClawSentry: A Progressive Multi-Tier Security Monitor for Safeguarding Autonomous LLM Agents

Kai Wang, Zeming Wei, BiaoJie Zeng +7

As large language model (LLM) agents move from conversation to executing code, reading local files, and orchestrating external tools, a single agent hijacked by a malicious third-p…

cs.AI2026

Deep Interaction: An Efficient Human-AI Interaction Method for Large Reasoning Models

Hefeng Zhou, Jinxuan Zhang, Jiong Lou +4

The emergence of Chain-of-Thought (CoT) reasoning has significantly enhanced the ability of large language models (LLMs) to tackle complex, multi-step tasks. However, when errors o…

cs.CL2026

Privacy-Preserving Text Sanitization for Distributed Agents Collaboration via Disentangled Representations

Xuan Liu, Hefeng Zhou, Sicheng Chen +6

When distributed agents exchange text across organizational boundaries, privacy leakage arises not only from explicit identifiers but also from distributional signatures such as fo…

cs.AI2026

AgentSchool: An LLM-Powered Multi-Agent Simulation for Education

Yulei Ye, Wenhao Li, Zhong Wen +23

Despite the rapid deployment of LLMs into classrooms, validating educational AI remains uniquely intractable: interventions act on developing learners whose cognitive and social tr…

cs.CR2026

SkillSafetyBench: Evaluating Agent Safety under Skill-Facing Attack Surfaces

Chang Jin, An Wang, Zeming Wei +7

Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environm…

cs.CR2026

TrinityGuard: A Unified Framework for Safeguarding Multi-Agent Systems

Kai Wang, Biaojie Zeng, Zeming Wei +7

With the rapid development of LLM-based multi-agent systems (MAS), their significant safety and security concerns have emerged, which introduce novel risks going beyond single agen…

cs.CL2026

Optimizing Soft Prompt Tuning via Structural Evolution

Zhenzhen Huang, Chaoning Zhang, Haoyu Bian +8

Soft prompt tuning leverages continuous embeddings to capture task-specific information in large pre-trained language models (LLMs), achieving competitive performance in few-shot s…

cs.AI2026

Epistemic Traps: Rational Misalignment Driven by Model Misspecification

Xingcheng Xu, Jingjing Qu, Qiaosheng Zhang +4

The rapid deployment of Large Language Models and AI agents across critical societal and technical domains is hindered by persistent behavioral pathologies including sycophancy, ha…