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

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--> [Submitted on 30 Jun 2026] Title:Representation Affects Retrieval: A Case Study of Skill Discovery and Routing in a Multimodal Agent Harness View a PDF of the paper titled Representation Affects Retrieval: A Case Study of Skill Discovery and Routing in a Multimodal Agent Harness, by Kevin Dela Rosa View PDF HTML (experimental) Abstract:A production agent harness must discover and rank, from a growing library of skills, the one most appropriate for a user's task. At small scale this selection happens in context: the LLM planner chooses among skill representations exposed in its system prompt, without an explicit embedding-based retrieval step. We treat this in-context selection as the small-N counterpart to embedding-based skill retrieval at scale, and present a case study of how Tinycloud, a production multimodal video agent harness, represents its skills for the planner. The harness ships skills under two recurring representations: tool-skills that wrap a single external API or system tool and serve as primitive vocabulary, and workflow-skills that orchestrate tool-skill calls plus a template render to produce one named deliverable. The harness exposes them via two surfaces in the system prompt: an inlined-body surface (full instructions, scripts, templates) for autoloaded skills, and a one-line listing for on-demand skills. A six-task selection ablation across three exposure regimes (all-on, default, all-off) shows that full autoload selects the gold skill on every task; all-off slows execution and produces hard discovery failures; and the production default misroutes one task because its lexical signal collides with an autoloaded tool-skill that pulls planner attention away from a listed workflow-skill. The headline finding is that in-prompt exposure of skills is not monotonically helpful: partial exposure can create lexical competition that suppresses correct selection. We connect this small-N observation to recent retrieval-based skill-routing work at large scale, and frame this contribution as a case study rather than a benchmark. Comments: 5 pages, 1 figure, 3 tables. Accepted at AgentSearch '26 workshop at SIGIR 2026 (Melbourne, Australia, July 24, 2026) Subjects: Artificial Intelligence (cs.AI) ACM classes: H.3.3; I.2.7 Cite as: arXiv:2608.20389 [cs.AI] (or arXiv:2608.20389v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.20389 arXiv-issued DOI via DataCite Submission history From: Kevin Dela Rosa [view email] [v1] Tue, 30 Jun 2026 17:09:02 UTC (57 KB) Full-text links: Access Paper: View a PDF of the paper titled Representation Affects Retrieval: A Case Study of Skill Discovery and Routing in a Multimodal Agent Harness, by Kevin Dela Rosa View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)