Verify, Don't Trust: Agentic Model Development for Video Discovery Retrieval at Scale
Large language model (LLM) agents can propose, implement, and evaluate model changes. Autoresearch loops demonstrate this capability through minutes-scale iterations on a self-contained program. Online autoresearch instead spans asynchronous systems, hours-long variants, and weeks-long campaigns that can influence a product. A completed run can still support an invalid conclusion when a code change is a no-op, data windows leak, evaluator semantics drift, or the two arms traverse different serving funnels. We present EvoPilot, a human-gated method for long-horizon online autoresearch. Role-specific agents execute each round through a versioned domain skill and typed adapter. Durable recor...
| Authors | Hao Fu, Baiting Zhu, Minglei Chen et al. |
|---|---|
| Primary category | cs.IR |
| Published | 18 September 2026 |
| Last updated at source | 18 September 2026 |
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