arXiv AI By Yuxuan Chen, Rongpeng Li, Zhifeng Zhao, Yuntao Liu, Xing Xu, Honggang Zhang

Agentic Router: An Execution-Grounded Continual Learning Approach With Memory

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arXiv:2608. 09184v1 Announce Type: new Abstract: Large language model (LLM) agents provide a promising interface for command-line-based network operations, but a plausible command may still fail or introduce operational risk after execution.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Jul 16

AgentCompass: A Unified Evaluation Infrastructure for Agent Capabilities

arXiv:2607. 13705v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, the need for unified evaluation infrastructure becomes critical.

By Zichen Ding, Jiaye Ge, Shufan Jiang, Kai Chen, Mo Li, Qingqiu Li, Zehao Li, Zonglin Li, Tiaohao Liang, Shudong Liu, Zerun Ma, Zixing Shang, Wenhui Tian, Zun Wang, Liwei Wu, Zhenyu Wu, Jun Xu, Bowen Yang, Dingbo Yuan, Qi Zhang, Songyang Zhang, Peiheng Zhou, Dongsheng Zhu
arXiv AI
Sep 2

Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems

The paper introduces Gated-Memory Routing, a method for efficient collaboration in multi‑agent large language model systems. It uses a learned execution memory with write and retrieval gates to keep only non‑redundant reasoning steps, and an adaptive halting controller to stop execution when enough evidence is gathered. Experiments on five reasoning and code‑generation benchmarks show the approach achieves higher accuracy and reduces inference cost by 31.9% compared to the strongest baseline.

By Rakibul Hasan Rajib, Mengxing Zheng, Qian Lou