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cs.AI2026
AI-for-Science Low-code Platform with Bayesian Adversarial Multi-Agent Framework
Zihang Zeng, Jiaquan Zhang, Pengze Li +2
Large Language Models (LLMs) demonstrate potentials for automating scientific code generation but face challenges in reliability, error propagation in multi-agent workflows, and ev…
cs.AI2026
RAPO: Expanding Exploration for LLM Agents via Retrieval-Augmented Policy Optimization
Siwei Zhang, Yun Xiong, Xi Chen +4
Agentic Reinforcement Learning (Agentic RL) has shown remarkable potential in large language model-based (LLM) agents. These works can empower LLM agents to tackle complex tasks vi…
cs.LG2026
SaFeR-ToolKit: Structured Reasoning via Virtual Tool Calling for Multimodal Safety
Zixuan Xu, Tiancheng He, Huahui Yi +7
Vision-language models remain susceptible to multimodal jailbreaks and over-refusal because safety hinges on both visual evidence and user intent, while many alignment pipelines su…