arxiv
PublishedOctober 21, 2026 at 4:00 AM
REM: What to Store, When to Recall, What to Forget in Experience Memory for Coding Agents
Publisher summary· verbatim
arXiv:2610.27377v1 Announce Type: new Abstract: Large language model (LLM) agents now autonomously complete long-horizon tasks, such as repository-level coding, through hundreds of think-act steps, yet the experience each run produces is rarely reused on the next. Prior memory systems concentrate on
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response7harxivLearning Transferable Latent User Preferences for Human-Aligned Decision Making7harxivRepair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents7harxivIndustryForge-27B: A Domain-Enhanced Multimodal Foundation Model for Industrial CAD7hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗