AI Research
BudgetBench: A Budget-Tiered Protocol and Pilot Harness for Memory Strategy Evaluation in Local Large Language Model Agents
Standardizing memory management and token cost evaluation for efficient local AI agent deployment.
For local large language model agents, active context is a scarce resource: memory capacity, prefill latency, cache growth, and service objectives all constrain how many input tokens each call can afford. We present BudgetBench, an active-budget protocol and reference harness tha...
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ArXiv CS.LG
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ArXiv CS.LG
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BudgetBench introduces a critical framework for optimizing local LLM agent performance by balancing compute efficiency against memory constraints. This development is essential for deploying reliable, resource-aware AI agents on edge hardware where context window costs are significant.
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