CLIO treats failed paths as evidence, switching models in scientific AI workflows
WirelessLife · x · 2026-09-05
In scientific AI, the best model may matter less than knowing when to switch. CLIO's approach treats failed exploration paths as evidence, changes course accordingly, and switches underlying models when needed.
It's a counterintuitive take on research-agent design: rather than betting on a single strongest model, build an orchestration layer that learns from failure and swaps models dynamically. Link included for testing.
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