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This paper introduces a controlled evaluation framework that separates answer coverage from repeatable task specialization in LLM-generated harnesses on MATH-500, finding that additional coverage largely comes from repeated executions of the same program rather than genuine specialization gains.
This paper investigates whether online skill and memory modules for web agents are worth their token cost under a fixed inference budget, finding that a budget-matched vanilla baseline often matches or outperforms augmented methods across three domains and models.
This article highlights five common ways AI teams waste inference budget and offers engineering levers to improve efficiency, targeting startups scaling AI models.