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cs.AI2026

XBridge: Entity-Grounded Latent Bridge for Heterogeneous LLM Communication

Wooseong Yang, Wei-Chieh Huang, Weizhi Zhang +3

Heterogeneous multi-agent LLM systems, where agents are powered by different model families, can outperform homogeneous configurations by reducing redundant reasoning patterns. Yet…

cs.AI2026

CoEvoSkills: Self-Evolving Agent Skills via Co-Evolutionary Verification

Hanrong Zhang, Shicheng Fan, Henry Peng Zou +12

Anthropic proposes the concept of skills for LLM agents to tackle multi-step professional tasks that simple tool invocations cannot address. A tool is a single, self-contained func…

cs.AI2026

Pedagogically-Inspired Data Synthesis for Language Model Knowledge Distillation

Bowei He, Yankai Chen, Xiaokun Zhang +4

Knowledge distillation from Large Language Models (LLMs) to smaller models has emerged as a critical technique for deploying efficient AI systems. However, current methods for dist…

cs.AI2025

Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies

Yankai Chen, Xinni Zhang, Yifei Zhang +6

Brain-Computer Interfaces (BCIs) offer a direct communication pathway between the human brain and external devices, holding significant promise for individuals with severe neurolog…

cs.AI2025

PSG-Agent: Personality-Aware Safety Guardrail for LLM-based Agents

Yaozu Wu, Jizhou Guo, Dongyuan Li +9

Effective guardrails are essential for safely deploying LLM-based agents in critical applications. Despite recent advances, existing guardrails suffer from two fundamental limitati…