TokenPilot: Cache-Efficient Context Management for LLM Agents
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 th...
| Authors | Buqiang Xu, Zirui Xue, Dianmou Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 June 2026 |
| Last updated at source | 28 August 2026 |
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