arXiv:2605.23296v1 Announce Type: new Abstract: Long-horizon LLM agents accumulate growing conversation histories that eventually exceed the model's context window. Context compaction via LLM-based summarization keeps the conversation bounded, but summarization is inherently lossy and the blocking call stalls agent inference for tens of seconds. Moreover, the operator has no fine-grained control over summary volume since prompt instructions are largely ignored, and as context grows, both the amount of output tokens the model produces and the information it retains fluctuate substantially from
The increasing complexity and adoption of LLM agents are pushing the boundaries of context window management, making efficient and effective compaction a critical immediate challenge.
Efficient context management is foundational for scalable, reliable, and performant AI agents, directly impacting their commercial viability and widespread deployment.
New methods for context compaction could enable LLM agents to maintain longer, more coherent interactions without performance degradation, improving their utility in complex tasks.
- · LLM agent developers
- · Enterprises deploying AI agents
- · Cloud AI providers
- · Inefficient LLM architectures
- · Developers reliant on ad-hoc context solutions
Improved context handling will allow for more sophisticated and generalized AI agents.
This could accelerate the automation of complex white-collar workflows previously too unwieldy for current agentic systems.
More capable AI agents might reshape industry structures by collapsing multiple SaaS layers into integrated, autonomous systems.
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Read at arXiv cs.AI