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BudgetBench

model1 events
papersTODAY 04:00 UTC

BudgetBench: Budget-Tiered Protocol for Evaluating Memory in Local LLM Agents

Researchers introduce BudgetBench, a protocol and pilot test harness for assessing memory strategies in locally run large language model agents. The work treats active context as a limited resource, accounting for memory capacity, prefill latency, cache growth, and service targets when deciding how many input tokens a call can afford. It aims to give a structured, budget-aware way to compare agent memory approaches under constrained hardware and latency conditions.

agents/memory/local-llm/benchmarks