Abstract
In multi-model artificial intelligence agent systems, performance may be affected by latency when a task is escalated from a smaller utility model to a larger frontier model, as this can involve recomputing the session’s key-value (KV) cache. A stateful orchestration gateway can monitor an agent's session to manage context across different models. For example, the gateway can use idle periods, such as during an external tool call, to predictively pre-compute the KV cache for a potential escalation. The gateway can also detect agentic loops, roll back the KV cache to a last known good state, inject a summary of the failure, and execute a bounded, single-turn escalation to the frontier model for recovery. This approach may reduce escalation latency and improve computational efficiency.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Grover, Rohan; Kondalam, Satish; Sambi, Muninder; and Ahuja, Aman, "Predictive Key-Value Cache Prefilling and Loop Recovery for Multi-Model Agent Systems", Technical Disclosure Commons, (October 26, 2026)
https://www.tdcommons.org/dpubs_series/12414