Agent in a Bottle: Can LLM Agents Turn Their Capabilities into Cheap Artifacts? (arxiv.org)

Researchers have introduced BOTTLED, a benchmark aimed at evaluating the ability of large language model (LLM) agents to transform their broad capabilities into more efficient, task-specific solutions, a process they refer to as "bottling." This addresses the challenge of cost-effectively deploying LLMs for large-scale, repetitive tasks, where querying them individually can incur significant expenses. The study found that while strong zero-shot performance does not guarantee effective bottling, some models demonstrated considerable cost savings. For instance, Opus 5 managed to maintain approximately 82% of its zero-shot performance in query-product relevance classification at a staggering 657 times lower cost. The significance of this work lies in its potential to enhance the practicality of LLMs in real-world applications by enabling agents to create scalable solutions more economically. The research highlights that models with similar zero-shot capabilities can exhibit dramatic differences in their bottling efficiency, suggesting a critical area for further development within the AI/ML community. Ultimately, the BOTTLED benchmark serves as a platform for assessing and optimizing how agents allocate their limited resources to generate reusable solutions, potentially transforming workflow management in AI-driven applications.