Representation Affects Retrieval: A Case Study of Skill Discovery and Routing in a Multimodal Agent Harness

The paper examines how skill representations influence selection in a multimodal video agent harness called Tinycloud. It compares two types of skill representations—tool-skills and workflow-skills—and two prompt surfaces—full inlined bodies and one-line listings—across three exposure regimes. The study finds that full autoload exposure consistently selects the correct skill, while partial exposure can cause lexical competition that misroutes tasks, highlighting that in-prompt exposure is not always beneficial.

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• llms
• rag
• agents
• multimodal
• benchmarks