An LLM seeds and slots a task-specific architecture search space that conventional NAS then explores, reaching SOTA on 11 of 17 diverse NAS benchmarks.
AAAI Conference on Artificial Intelligence , volume=
4 Pith papers cite this work, alongside 363 external citations. Polarity classification is still indexing.
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EvoPref applies NSGA-II evolutionary optimization with archive-based diversity to populations of LoRA adapters, yielding 18% higher preference coverage and 47% lower collapse than gradient descent baselines while matching alignment quality.
Specialist agents in an autonomous research loop with lineage feedback improve ML training recipes, delivering 0.81% better validation bpb on Parameter Golf, 38.7% higher CORE on NanoChat-D12, and 4.59% lower wallclock on CIFAR-10 Airbench96 across 1797 trials with no human intervention after setup.
Sparse MoE vision models show positive accuracy gaps only when routing a substantial compute fraction ρ and using k≥2 experts at large scale; batch-axis dispatch is identified as a key failure mode.
citing papers explorer
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Agentic Neural Architecture Search
An LLM seeds and slots a task-specific architecture search space that conventional NAS then explores, reaching SOTA on 11 of 17 diverse NAS benchmarks.
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EvoPref: Multi-Objective Evolutionary Optimization Discovers Diverse LLM Alignments Beyond Gradient Descent
EvoPref applies NSGA-II evolutionary optimization with archive-based diversity to populations of LoRA adapters, yielding 18% higher preference coverage and 47% lower collapse than gradient descent baselines while matching alignment quality.
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Auto Research with Specialist Agents Develops Effective and Non-Trivial Training Recipes
Specialist agents in an autonomous research loop with lineage feedback improve ML training recipes, delivering 0.81% better validation bpb on Parameter Golf, 38.7% higher CORE on NanoChat-D12, and 4.59% lower wallclock on CIFAR-10 Airbench96 across 1797 trials with no human intervention after setup.
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When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing
Sparse MoE vision models show positive accuracy gaps only when routing a substantial compute fraction ρ and using k≥2 experts at large scale; batch-axis dispatch is identified as a key failure mode.