SkillResolve-Bench: Measuring and Resolving Same-Capability Ambiguity in Agent Skill Retrieval
arXiv:2606.10388v1 Announce Type: cross Abstract: Agent skill libraries are becoming routable software assets: a retrieved skill can contribute instructions, scripts, resource bindings, and execution assumptions to an agent. This makes skill retrieval more than broad relevance matching. A retriever can find the right capability family yet expose the wrong same-capability representative. We study this failure as same-capability execution-risk retrieval. Each query pairs a helpful skill with a query-specific risky sibling that shares the capability family but can lead execution toward a stale re
The proliferation of AI agents and the development of sophisticated skill libraries necessitate robust methods for agent skill retrieval and disambiguation to prevent execution failures.
This work directly addresses a critical challenge in scaling autonomous AI agents: ensuring they reliably select and execute the correct skills, which impacts their trustworthiness and widespread adoption.
The development of benchmarks and methods like SkillResolve-Bench will improve the reliability and safety of AI agents by mitigating 'same-capability ambiguity' in skill retrieval.
- · AI Agent Developers
- · Agent Skill Library Providers
- · Automation Software Companies
- · Enterprise AI Users
- · Companies relying on unreliable 'black box' AI agents
- · Outdated skill retrieval models
AI agents become more reliable and perform robustly across diverse tasks due to improved skill selection.
Increased enterprise adoption of AI agents for complex, critical workflows as trust and performance improve.
A potential acceleration in autonomous system development across various sectors, leading to new forms of automation and productivity gains.
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Read at arXiv cs.AI