arXiv:2510.00615v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as agents in dynamic real-world environments, where success depends on maintaining precise records of actions and observations. However, the resulting unbounded context growth in long-horizon agentic tasks makes two critical bottlenecks: prohibitive inference memory costs and reasoning degradation due to irrelevant information. Existing compression methods fail to fully address this, often relying on brittle heuristics or requiring parameter updates impractical for proprietary or la
The proliferation of LLMs in agentic roles is creating urgent technical bottlenecks around context management, which this research directly addresses.
Efficient context compression is critical for scaling AI agents to long-horizon, real-world tasks, directly impacting the viability and performance of autonomous systems.
New methods for optimizing context windows will enable LLM agents to operate more effectively over longer periods and complex tasks, reducing resource demands and improving reliability.
- · AI agent developers
- · Cloud computing providers
- · LLM application designers
- · Companies with inefficient LLM deployments
- · Legacy context management techniques
Increased efficiency and capability of AI agents in complex environments.
Acceleration of AI agent deployment across various industries due to reduced operational costs and improved performance.
Enhanced competition among AI agent providers, leading to more sophisticated and cost-effective autonomous solutions.
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Read at arXiv cs.CL