QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A parameter-free decomposition in MoE models separates routing control from content, showing that expert trajectories cluster tokens by semantic function across languages and forms, making paths rather than experts the natural unit of interpretability.
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.
citing papers explorer
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Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis
QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.
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Polysemantic Experts, Monosemantic Paths: Routing as Control in MoEs
A parameter-free decomposition in MoE models separates routing control from content, showing that expert trajectories cluster tokens by semantic function across languages and forms, making paths rather than experts the natural unit of interpretability.
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Position: Agentic AI System Is a Foreseeable Pathway to AGI
Agentic AI systems with DAG topologies are claimed to deliver exponentially superior generalization and sample efficiency compared to monolithic scaling for achieving AGI.