arXiv AI July 13, 2026 By Ning Liu, Kalle Kujanp\"a\"a, Zhaoxuan Zhu, P Aditya Sreekar, Kaiwen Liu, Chuanneng Sun, Jorge Marchena Menendez, Matthew Bales, Tianyu Yang, Shahnawaz Alam, Rose Yu, Baoyuan Liu, Kristina Klinkner, Shervin Malmasi

Eluna: An Agentic LLM System for Automating Warehouse Operations with Reasoning and Task Execution

Read the original on arXiv AI →

arXiv:2607. 08960v1 Announce Type: cross Abstract: Warehouse operations are governed by Standard Operating Procedures (SOPs) that encode complex, multi-system decision logic, which must be executed reliably under strict time constraints, yet LLM agents lack mechanisms to enforce procedural compliance and degrade under the context overload full SOP specifications introduce.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv Machine Learning

Jul 10

Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems

arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.

By Kalle Kujanp\"a\"a, Ning Liu, Shahnawaz Alam, Yeshwanth Reddy Sura, Tianyu Yang, Kristina Klinkner, Shervin Malmasi

llms agents benchmarks

arXiv AI

2d ago

Agentic Transaction: Towards ACID-Compliant Agent Systems

arXiv:2608. 13900v1 Announce Type: cross Abstract: Large language model (LLM) agents are evolving from conversational assistants into autonomous systems that execute long-horizon tasks through reasoning, tool use, code generation, and workspace manipulation.

By Zhaoyan Sun, Xiaoxiao Wang, Guoliang Li

llms agents robotics benchmarks

arXiv Machine Learning

Jun 2

Multi-Agent Computer Use

arXiv:2606. 01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents.

By Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried

agents benchmarks

Hugging Face Trending Papers

Jul 9

Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems

Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment.

llms agents benchmarks

arXiv AI

Jun 16

EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective

arXiv:2605. 18421v2 Announce Type: replace-cross Abstract: Recent benchmarks for Large Language Model (LLM) agents mainly evaluate reasoning, planning, and execution.

By Yuyao Wang, Zhongjian Zhang, Mo Chi, Kaichi Yu, Yuhan Li, Miao Peng, Bing Tong, Chen Zhang, Yan Zhou, Jia Li

llms agents benchmarks

arXiv AI

2d ago

AgentRewind: Recoverable Execution for Long-Horizon LLM Agents

arXiv:2608. 14380v1 Announce Type: new Abstract: Many real-world tasks require LLM agents to interact with their environments over long execution horizons.

By Yu Zhuang, Kefei Chen, Yitong Duan, Shuxin Zheng, Jian Li, Xu-Yao Zhang

llms agents benchmarks safety

Related stories

Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems

Agentic Transaction: Towards ACID-Compliant Agent Systems

Multi-Agent Computer Use

Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems

EvoMemBench: Benchmarking Agent Memory from a Self-Evolving Perspective

AgentRewind: Recoverable Execution for Long-Horizon LLM Agents