HypoExplore uses LLMs for hypothesis-driven evolutionary search with a Trajectory Tree and Hypothesis Memory Bank to discover lightweight vision architectures, reaching 94.11% accuracy on CIFAR-10 from an 18.91% baseline and generalizing to other datasets including state-of-the-art on MedMNIST.
Alphago moment for model architecture discovery
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UNVERDICTED 4representative citing papers
Glia deploys a multi-agent LLM workflow with reasoning, experimentation, and analysis agents to generate interpretable algorithms for request routing, scheduling, and auto-scaling in distributed GPU clusters, reaching human-expert performance levels.
SOLAR introduces a self-optimizing agent using meta-learning on model weights and RL-driven strategy discovery for lifelong adaptation in LLMs, claiming superior performance on reasoning tasks across domains.
Growth of equational discoveries follows power laws whose exponent and saturation behavior are conditional on the substrate, with a heuristic saturating model fitting some real-world cases better than pure power laws.
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Glia: A Human-Inspired AI for Automated Systems Design and Optimization
Glia deploys a multi-agent LLM workflow with reasoning, experimentation, and analysis agents to generate interpretable algorithms for request routing, scheduling, and auto-scaling in distributed GPU clusters, reaching human-expert performance levels.