arXiv AI July 13, 2026 By Sanjana Pedada, Aditya Dhavala, Neelraj Patil

Shared Selective Persistent Memory for Agentic LLM Systems

arXiv:2607. 09493v1 Announce Type: new Abstract: Agentic LLM systems that generate code through multi-turn tool use face a fundamental context problem: each session starts from zero, discarding the configuration choices, domain constraints, data schemas, and tool-use patterns that made previous sessions productive.

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

arXiv AI

1d ago

From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems

arXiv:2608. 15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state.

By Chaokun Chang, Yukun Zhou, Kaihua Fu, Dakai An, Tianyu Feng, Hanfeng Lu, Sheng Yao, Pu Guo, Yinghao Yu, Yizhou Shan, Bo Li, Binhang Yuan, Wei Wang

llms agents benchmarks

arXiv Machine Learning

Jul 14

ToolAtlas: Learning Once, Reusing Everywhere with Tool-Side Memory

arXiv:2607. 11126v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools served by shared providers and accessed by heterogeneous downstream agents.

By Yue Fang, Zhibang Yang, Fangkai Yang, Xiaoting Qin, Liqun Li, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

llms agents benchmarks

arXiv AI

1d ago

AstronOS: A Unified Execution Model and Runtime for Long-Horizon Agentic Systems

arXiv:2608. 16381v1 Announce Type: new Abstract: Agentic systems often organize execution and state around a single conversation, model invocation, or agent instance, even when real work spans many calls and stages.

By Zhenhang Nie (iFLYTEK Co., Ltd., Hefei, China), Gui Zheng (iFLYTEK Co., Ltd., Hefei, China), Xudong Sun (iFLYTEK Co., Ltd., Hefei, China), Tailong Zhu (iFLYTEK Co., Ltd., Hefei, China), Bin Zhang (iFLYTEK Co., Ltd., Hefei, China)

agents benchmarks

arXiv AI

Jul 2

Self-GC: Self-Governing Context for Long-Horizon LLM Agents

arXiv:2607. 00692v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate tool results, files, plans, and user constraints that are too structured to be treated as a disposable text suffix.

By Xubin Hao, Hongjin Meng, Xin Yin, Jiawei Zhu, Chenpeng Cao

llms agents efficiency

arXiv Machine Learning

Aug 4

Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale

arXiv:2608. 00101v1 Announce Type: cross Abstract: AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution, creating a workload different from chatbots.

By Banruo Liu, Haoran Qiu, \'I\~nigo Goiri, Rodrigo Fonseca, Ricardo Bianchini, Esha Choukse

llms agents efficiency

arXiv AI

Jun 6

TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management

arXiv:2606. 06337v1 Announce Type: new Abstract: Large language model (LLM) deployments for long-horizon tasks face a fundamental constraint: context windows are finite while productive work sessions are not.

By Shweta Mishra

llms benchmarks

Related stories

From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems

ToolAtlas: Learning Once, Reusing Everywhere with Tool-Side Memory

AstronOS: A Unified Execution Model and Runtime for Long-Horizon Agentic Systems

Self-GC: Self-Governing Context for Long-Horizon LLM Agents

Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale

TokenMizer: Graph-Structured Session Memory for Long-Horizon LLM Context Management