M-SQE: Multilingual Skill Quality Estimation for Enhancing Language Equality in Agentic Skill Use

2026-09-16

AI Summary

本文针对多语言技能检索质量不均问题，提出M-SQE框架，通过评估技能内在质量和任务实用性来提升低资源语言如斯瓦希里语和印地语的技能使用成功率。

Abstract

Agent skills, reusable procedural documents that extend LLM agents beyond their parametric memory, have become an important interface for deploying agents on real-world tasks. Community-maintained skill libraries built around this interface are growing rapidly. However, this ecosystem remains deeply English-centric: our audit finds that low-resource languages such as Swahili and Hindi have no in-language skill content, so retrieval often returns a skill written in a different language than the query, degrading accuracy and recall. A practical solution is to synthesize in-language skills for retrieval but the quality can be unreliable, so relevance in this setting alone often surfaces a related but unusable candidate. To address this, we propose M-SQE, a post-retrieval Multilingual Skill Quality Estimation framework that scores candidates via a Theory view for intrinsic quality and an Action view for task-grounded utility, unified into a domain-conditioned final score. We evaluate M-SQE across three skill-use domains: general, tool-use, and cultural tasks. Empirically, we build three-layer candidate skill pools mirroring today's ecosystem, where M-SQE's task success exceeds existing baseline's average by at least +3.5 points across three different retrievers. Particularly, M-SQE lifts the lowest-resource languages most (+12.9pp on Hindi and +5.6pp on Swahili) and achieves strong performance across all six culture regions, thereby moving agentic skill use toward linguistic and cultural equality.

Problem

• Multilingual
• Skill Quality Estimation
• Language Equality
• Low-resource Languages
• Agentic Skill Use

Innovation

• Multilingual Skill Quality Estimation
• Language Equality
• Low-Resource Languages
• Skill Retrieval