neural-architecture-search llm-agents automl hybrid-search benchmark

LLM seeds architecture, NAS optimizes slots automatically

AgentNAS decomposes LLM-generated architectures into slotted scaffolds that automatically define bounded search spaces for NAS, eliminating manual search space engineering.

Summary

Removes the domain expertise bottleneck in neural architecture search—developers no longer manually define search spaces per task. The hybrid approach (LLM seed + NAS refinement) scales across modalities (vision, regression, segmentation, tagging) without task-specific tuning.

Why it matters

Implementation verdict

Replaces manual architecture design + hand-tuned NAS spaces. Requires: codebase access (promised at https URL), ability to run NAS pipeline on target hardware, LLM access for seed generation. Research-stage artifact (arXiv July 2026)—not production-ready, but reproducible benchmark results on 17 tasks suggest the pattern generalizes.

Sources

1.an LLM produces a high-quality seed architecture, then decomposes it into a "slotted architecture", a scaffold with named, interchangeable module slots that automatically defines a bounded, task-specific search space
2.AgentNAS establishes a new state of the art on 11 tasks
3.the two search mechanisms are broadly complementary: the LLM-generated seed already surpasses published baselines on the majority of tasks, and NAS delivers additional gains in most cases through combinatorial recombination across slots

Dev Signal

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