Context windows have grown a thousand-fold, yet coding agents still start every session with no memory of their past work. They re-read files, retry failed approaches, and forget root causes they esta...
arXiv:2609.23570v1 Announce Type: cross
Abstract: Coding agents operate on real repository coding tasks, and persistent memory systems promise to reuse experience across tasks. Yet existing evaluatio...
By Liyang Fan, Yingcheng Shi, Yongbin Li, Chenghao Sun, Xin Chen, Xander Xu, Hu Wei, Shiwen Ni, Min Yang, Jieping Ye
The paper introduces REM (Reusable Experience Memory), a framework that manages the lifecycle of experience for large language model agents performing long-horizon coding tasks. REM consolidates completed trajectories into compact, phase-structured documents, injects them at task start, and evicts them after a single exposure, making the memory system responsive to the agent’s needs. Experiments on repository-level bug fixing show REM outperforms state‑of‑the‑art memory systems in retrieval accuracy and task resolution while reducing cost.
By Xinyi Lisa Chen, Pedro Herrero-Vidal, Xiaohan Chen, Xiaojing Yu, Cris Liu, Bingqing Song, Bin Xu, Suleiman A. Khan, You-Lin Chen
MemTrace is a provenance‑aware memory system designed to help coding agents handle long‑horizon software evolution tasks that span multiple files and stages. It stores execution history as immutable Memory Traces linked to key artifacts (files, symbols, tests) and organizes them in a Memory Trace Graph to track execution order and dependencies. When context budgets are limited, the system keeps only compact Memory Anchors, from which the agent can reconstruct the latest execution state and retrieve only valid, relevant evidence, improving performance on three long‑horizon coding benchmarks.
"whyItMatters":"By preserving consistent task state and verifying evidence validity, MemTrace enables coding agents to outperform existing baselines on long‑horizon benchmarks, achieving significant gains in pass@1, resolved rate, and milestone scores."
By Hongming Xu, Le Zhou, ZhongHe Jin, Xiang Zhang, Bo Tang, Zhiyu Li, Xuanhe Zhou, Juncheng Zhang
arXiv:2607. 14390v1 Announce Type: cross Abstract: Coding agents now produce a growing share of a team's code, while the reasoning behind each change -- the alternatives weighed, the constraints discovered, the approaches rejected -- is trapped in assistant transcripts that vanish with the session.
By Frank Guo
MemDev-Bench is a new benchmark that evaluates how memory systems influence code agents across diverse repository histories. It pairs each task with relevance‑annotated earlier tasks from the same repository, creating 4,869 historical tasks for 555 base tasks and an automated pipeline that ensures these tasks are executable and verifiable. The study finds that memory mainly improves fine‑grained localization and editing, but can also cause failures when agents reuse memory without reassessing its applicability, and that providing raw historical trajectories reduces steps more effectively than compressed memories.
By Guorui Chen, Tao Liu, Zhenzhen Huang, Yidan Zhang, Liyu Jiang, Shuoyuan Wang, Hanchen Sun, Xu He, Dong Li, Yihan Hu, Jianye Hao, Lei Feng