KARL Integration — Evolution³ / Stage 1: PATH D
Abandon regex-based skill routing entirely. Embed every skill and every incoming prompt into a shared vector space. When a new prompt arrives, find the nearest skill by **trajectory-weighted similarity** — not raw text overlap. The "learning" is not RL on model weights (that is KARL's full treatment). It is RL on the **routing layer itself**. Skills stay as SKILL.md markdown. The only thing that changes is which skill gets injected, and that decision is made by a vector space whose distances are continuously update
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What has to happen next
Attach run IDs, datasets, metrics, and reproduction commands.
Why this is not always a full paper yet
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