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. Current systems mostly rely on in-run heuristics such as chronological pruning and tool-output masking, or on final self-summary near a context limit. Heuristics are cheap but blind to future dependencies; summaries preserve narrative state but often hide exact evidence, locators, and editable artifacts.

We present Self-GC, where GC denotes self-governing context while deliberately echoing garbage collection: the system does not merely reclaim unused tokens, but governs the lifecycle of agent context objects. Self-GC turns user turns, tool spans, and skill state into indexed objects; asks a side-channel planner to propose fold, mask, and prune actions; and lets the harness enforce recoverable sidecars, safe commit boundaries, and cache-aware commit. On a 33-session Hard Set, Self-GC prunes 43.

95% of prefix tokens while leaving 84. 85% of future continuations unaffected, compared with no-impact rates of 54. 55% to 69. 70% for heuristic baselines. On a 332-session production-derived suite, three planner backbones reach no-impact rates of 91. 27% to 94. 58%, while baselines remain at 77. 71% to 87. 46%. In production, an online account-level split reduces daytime average input tokens by 10% to 15%, with peak reductions near 20%.

These results point to context management as runtime lifecycle control over indexed, recoverable objects rather than post hoc text cleanup.