TokenPilot: Cache-Efficient Context Management for LLM Agents

2026-06-15

AI Summary

This work addresses the escalating inference costs in long-horizon LLM agent execution caused by accumulating context, where existing pruning or memory eviction strategies often disrupt prompt cache continuity, leading to prefix mismatches and cache invalidation. To tackle this, the authors propose TokenPilot, a dual-granularity context management framework that globally employs ingestion-aware compression to stabilize the prompt prefix and filter environmental noise, while locally applying a lifetime-aware eviction mechanism to conservatively offload context once its task relevance diminishes. TokenPilot is the first approach to achieve efficient context compression without compromising cache continuity, effectively balancing textual sparsity and cache efficacy. Experiments on PinchBench and Claw-Eval demonstrate that TokenPilot reduces inference costs by up to 61%/56% in isolated mode and 61%/87% in continuous mode, with no degradation in task performance, and has been integrated into LightMem2.

Abstract

As LLM agents are deployed in long-horizon sessions, context accumulation drives up inference costs. Existing approaches utilize text pruning or dynamic memory eviction to minimize token footprints; however, their unconstrained sequence mutations alter layouts, introducing prefix mismatches and cache invalidation. This reveals a critical trade-off between text sparsity and prompt cache continuity. To address this, we present TokenPilot, a dual-granularity context management framework. Globally, Ingestion-Aware Compaction acts as a framework harness to stabilize prompt prefixes and eliminate open-world environmental noise at the ingestion gate. Locally, Lifecycle-Aware Eviction monitors the ongoing residual utility of context segments, enforcing a conservative batch-turn schedule to offload content segments only when task relevance expires. Experiments on PinchBench and Claw-Eval under both isolated and continuous modes demonstrate that TokenPilot reduces costs by 61% and 56% in isolated mode, and 61% and 87% in continuous mode, while maintaining competitive performance compared to prior systems. TokenPilot has been integrated into LightMem2 at https://github.com/zjunlp/LightMem2.

Problem

• context management
• LLM agents
• prompt cache
• token efficiency
• cache invalidation

Innovation

• cache-efficient
• context management
• LLM agents
• dual-granularity
• prompt cache continuity