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  "text": "What is actually limiting model routers for coding tasks?\n\nMost routers treat picking a model as a static, one-off classification.\n\nThis paper identifies the real bottleneck as information deficit. Simply augmenting a vanilla LLM router with task-dimension-level performance statistics yields a 15.3% relative gain, surpassing a heuristic router built on the same priors.\n\nMotivated by that, the authors propose Agent-as-a-Router, which formalizes routing as a Context to Action to Feedback to Context loop. It closes the information gap by accumulating execution-grounded experience during deployment, so the router improves from real outcomes instead of guessing once.\n\nWhy does it matter?\n\nIf you orchestrate multiple coding models by cost and skill, treating routing as a learning loop rather than a one-shot classifier is the lever. This means that routing becomes an agentic process that updates its priors as it runs.\n\nPaper: https://t.co/717q9Ep45M\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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