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
The proliferation of increasingly complex LLM agent applications necessitates more sophisticated context management beyond simplistic chronological or summary-based heuristics, as current methods lead to significant information loss.
Improving context management for long-horizon LLM agents is critical for their reliability, efficiency, and ability to handle multi-step, complex tasks, directly impacting their commercial viability and adoption.
The proposed 'Self-Governing Context' (Self-GC) offers a more robust method for LLM agents to manage and utilize diverse types of context, moving beyond basic textual history to structured data and artifacts.
- · AI software developers
- · Companies adopting LLM agents
- · Enterprise productivity software
- · Legacy AI agent frameworks
- · Systems relying on naive context handling
LLM agents become more capable of executing complex, multi-stage workflows without forgetting crucial details or misinterpreting prior outputs.
This capability enhancement drives faster adoption of autonomous agents in white-collar sectors, further eroding the need for human intervention in routine cognitive tasks.
The increased reliability and functionality of agents accelerate the convergence towards Artificial General Intelligence, as advanced context management is a key component for true autonomy.
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Read at arXiv cs.AI