Self-Evolving Multimodal Skill Routing with Dual-Timescale Experience Memory

External skill libraries expand the capabilities of multimodal agents, but routing queries to the accurate multimodal skills becomes increasingly challenging as these libraries scale. Existing static routers leave post-execution feedback untapped, preventing routing policies from improving with experience. In this work, we formalize self-evolving multimodal skill retrieval, in which a router continually adapts multimodal query and skill representations through execution feedback. Our key insight is that these representations should evolve at different timescales: queries benefit from rapid adaptation leveraging past interactions of similar requests, whereas skill profiles demand stable semantic consolidation across diverse task contexts. Motivated by this, we propose MM-RouterEvolve, a new self-evolving framework that couples query-side and skill-side adaptation through a dual-timescale experience memory. It organizes multimodal queries, retrieved skills, and feedback verifying their compatibility into unified experience units. A bounded short-term memory uses recent routing outcomes to contextualize incoming queries, while a persistent long-term memory consolidates execution feedback to refine skill profiles and distinguish functionally similar skills. This design connects immediate routing adaptation with cumulative refinement of the skill library. Evaluated on two benchmarks, i.e., MM-SkillBench-SE (multimodal) and SkillRouter (text-only), MM-RouterEvolve substantially outperforms static baselines and unilateral query/skill adaptation paradigms, showing that experience-driven self-evolution of both query and skill representations yields more robust routing.