most citedA-MemGuard: A Proactive Defense Framework for LLM-Based Agent Memory

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cs.AI2026

SPO++: Stream-Aligned Policy Optimization for Asynchronous Agentic RL

Kai Ruan, Jinghao Lin, Qianshan Wei +2

Group-relative reinforcement learning waits for sibling rollouts of the same prompt, which is costly for long and variable tool-use trajectories. Single-stream Policy Optimization…

cs.AI2026

The Path to Self-Evolving Clinical Systems: Scaling Medical Agents from Assistance to Autonomy

Chunzheng Zhu, Lei Tian, Bohan Tan +15

The growing ability of large language models and vision-language models to jointly interpret and reason over images and text is reshaping medical imaging AI, moving it from task-sp…

cs.AI2026

Doomed from the Start: Early Abort of LLM Agent Episodes via a Recall-Controlled Probe Cascade

Kai Ruan, Zihe Huang, Ziqi Zhou +4

Large language model (LLM) agents often waste inference compute by continuing multi-step trajectories that are already doomed to fail. We study early failure prediction and inferen…

cs.AI2026

SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation

Zhengbo Jiao, Yiming Cheng, Yilei Jiang +15

Training multimodal search agents to perform multi-hop reasoning remains challenging due to a fundamental structural disconnect: existing pipelines construct training data, search…

cs.AI2026

Agentic-MME: What Agentic Capability Really Brings to Multimodal Intelligence?

Qianshan Wei, Yishan Yang, Siyi Wang +12

Multimodal Large Language Models (MLLMs) are evolving from passive observers into active agents, solving problems through Visual Expansion (invoking visual tools) and Knowledge Exp…