Signals 4 · free daily AI digest

AutoCompact: Learning When to Compact Context in Long-Horizon Coding Agents

Paper recorded by Signals 4 on 2026-10-01 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-01 on arXiv · recorded by Signals 4 on 2026-10-02

Category: cs.CL · 自然语言处理 · first seen 2026-10-02

Abstract

Coding agents solve repository-level software engineering tasks through long trajectories of code inspection, search, editing, and testing. As a task progresses, earlier exploration becomes stale, so managing context is more than avoiding overflow: an agent must decide when to compact, what working state to preserve, and how to continue from it. We introduce AutoCompact, which trains a coding agen

Read on arXiv →

#1 most recent of 311 cs.CL papers we have recorded · ↓ older: Keyword Harnesses Fail Open: A Cheap Diagnostic Ladder for Tool-Use Cl
Cite this page: AutoCompact: Learning When to Compact Context in Long-Horizon Coding Agents: the #1 most recent of 311 cs.CL papers we have recorded (as of 2026-10-01). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/autocompact-learning-when-to-compact-context-in-long-horizon-coding-agents.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.CL papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions