Neural LoFi models deep learning as layer-wise spectral filtering that selects maximal low-degree correlations, yielding a tractable surrogate for hierarchical representation learning beyond the lazy regime.
A theory for emergence of complex skills in language models
8 Pith papers cite this work. Polarity classification is still indexing.
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Two steps of gradient descent on first-layer weights in linear-width two-layer networks produce a spiked random matrix with floor(alpha2/(1/2-alpha1)) outliers, each a learned direction, and batch reuse allows capturing directions with information exponent exceeding one.
The ghost mechanism derives a 1D canonical model of abrupt learning in RNNs from ghost points of saddle-node bifurcations, predicting an inverse-power-law critical learning rate and gradient-based failure modes.
Skill neologisms are optimized soft tokens that enhance specific LLM skills and support zero-shot composition on synthetic and Skill-Mix tasks.
A theoretical attacker-defender game in LLM adversarial prompting yields a best-response attack related to existing methods, reveals attacker advantages at equilibrium, and derives a provably optimal defense with stronger empirical performance.
Power-law data sampling creates beneficial asymmetry in the loss landscape that lets models acquire high-frequency skill compositions first, enabling more efficient learning of rare long-tail skills than uniform distributions.
Recovering an orthogonal basis from model activations yields a model-native skill characterization that improves reasoning Pass@1 by up to 41% via targeted data selection and supports inference steering, outperforming human-characterized alternatives.
Introduces ANAI framework with Autonomy Index (AIx), Infrastructure Coupling Coefficient (ICC), and Technological Transition Potential (TTP) to model AI-driven infrastructural transition via nonlinear coevolution and recursive feedback loops.
citing papers explorer
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Deep Learning as Neural Low-Degree Filtering: A Spectral Theory of Hierarchical Feature Learning
Neural LoFi models deep learning as layer-wise spectral filtering that selects maximal low-degree correlations, yielding a tractable surrogate for hierarchical representation learning beyond the lazy regime.
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Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent
Two steps of gradient descent on first-layer weights in linear-width two-layer networks produce a spiked random matrix with floor(alpha2/(1/2-alpha1)) outliers, each a learned direction, and batch reuse allows capturing directions with information exponent exceeding one.
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A ghost mechanism: An analytical model of abrupt learning in recurrent networks
The ghost mechanism derives a 1D canonical model of abrupt learning in RNNs from ghost points of saddle-node bifurcations, predicting an inverse-power-law critical learning rate and gradient-based failure modes.
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Skill Neologisms: Towards Skill-based Continual Learning
Skill neologisms are optimized soft tokens that enhance specific LLM skills and support zero-shot composition on synthetic and Skill-Mix tasks.
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A Theoretical Game of Attacks via Compositional Skills
A theoretical attacker-defender game in LLM adversarial prompting yields a best-response attack related to existing methods, reveals attacker advantages at equilibrium, and derives a provably optimal defense with stronger empirical performance.
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The Power of Power Law: Asymmetry Enables Compositional Reasoning
Power-law data sampling creates beneficial asymmetry in the loss landscape that lets models acquire high-frequency skill compositions first, enabling more efficient learning of rare long-tail skills than uniform distributions.
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Characterizing Model-Native Skills
Recovering an orthogonal basis from model activations yields a model-native skill characterization that improves reasoning Pass@1 by up to 41% via targeted data selection and supports inference steering, outperforming human-characterized alternatives.
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AI-Native Autonomous Infrastructure (ANAI): A Formal Framework for the Next General-Purpose Technology
Introduces ANAI framework with Autonomy Index (AIx), Infrastructure Coupling Coefficient (ICC), and Technological Transition Potential (TTP) to model AI-driven infrastructural transition via nonlinear coevolution and recursive feedback loops.