Coding agents learn to manage context windows autonomously
Researchers have developed AutoCompact, a training method that teaches coding agents to autonomously manage context windows during long repository-level tasks. Rather than treating context overflow as a binary problem, the system learns when to summarize prior work, what state to preserve, and how to resume execution. The approach uses a judge to validate compaction decisions during training, correcting flawed summaries before they propagate through trajectories. This addresses a fundamental challenge in agentic AI: as tasks grow complex, agents must distinguish between stale exploration and critical working state, making context management a learned policy rather than a heuristic.
Modelwire context
ExplainerAutoCompact treats context compaction as a learnable policy rather than a fixed rule, using a judge to validate summaries during training and prevent error propagation. This is distinct from prior work because it corrects bad compaction decisions before they cascade through long trajectories, rather than accepting or discarding context after the fact.
This builds directly on the memory efficiency work from late September. Where ReCAP (Sept 30) preserves attention-derived importance signals to avoid recomputation, and KV-streams (Sept 28) maintains cache state across compaction cycles, AutoCompact adds a layer above both: a training-time mechanism that learns *what* to compact and *when*. The three papers form a stack: AutoCompact decides policy, KV-streams handles the mechanical efficiency, and ReCAP preserves the signals. Together they address the scaling wall that long-horizon agents hit, though AutoCompact is the first to frame compaction as something agents should actively reason about rather than something infrastructure should optimize around.
If AutoCompact's learned compaction policy generalizes to repositories outside its training distribution (different codebases, unfamiliar libraries), that confirms the approach captures genuine reasoning about relevance. If performance degrades sharply on out-of-distribution tasks, the judge may be overfitting to specific compaction patterns rather than learning transferable principles.
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “AutoCompact: Learning When to Compact Context in Long-Horizon Coding Agents”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.