Publications
2026
B. S. Ruben and C. Pehlevan,
B. S. Ruben and C. Pehlevan,
C. Lauditi, C. Pehlevan*, and B. Bordelon*,
“Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer”, arXiv preprint arXiv:2605.07870, 2026.
C. Lauditi, C. Pehlevan*, and B. Bordelon*,
“Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer”, arXiv preprint arXiv:2605.07870, 2026.
M. Uzun, M. Erdogan, C. Pehlevan, and A. T. Erdogan,
“Score Broadcast and Decorrelation: A General Framework for Broadcast-Based Credit Assignment”, arXiv preprint arXiv:2605.30638 , 2026.
M. Uzun, M. Erdogan, C. Pehlevan, and A. T. Erdogan,
“Score Broadcast and Decorrelation: A General Framework for Broadcast-Based Credit Assignment”, arXiv preprint arXiv:2605.30638 , 2026.
K. Takanami and C. Pehlevan,
“An Asymptotic Theory of Chain-of-Thought in In-Context Learning”, arXiv preprint arXiv:2606.03217, 2026.
K. Takanami and C. Pehlevan,
“An Asymptotic Theory of Chain-of-Thought in In-Context Learning”, arXiv preprint arXiv:2606.03217, 2026.
A. Atanasov, B. Bordelon, J. A. Zavatone-Veth, C. Paquette, and C. Pehlevan,
“Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models”, Advances in Theoretical and Mathematical Physics, vol. 30, no. 1, p. 36, 2026.
A. Atanasov, B. Bordelon, J. A. Zavatone-Veth, C. Paquette, and C. Pehlevan,
“Two-Point Deterministic Equivalence for Stochastic Gradient Dynamics in Linear Models”, Advances in Theoretical and Mathematical Physics, vol. 30, no. 1, p. 36, 2026.
C. Lauditi, B. Bordelon, and C. Pehlevan,
“Transfer Learning in Infinite Width Feature Learning Networks”, International Conference on Learning Representations (ICLR), 2026.
C. Lauditi, B. Bordelon, and C. Pehlevan,
“Transfer Learning in Infinite Width Feature Learning Networks”, International Conference on Learning Representations (ICLR), 2026.
J. A. Zavatone-Veth and C. Pehlevan,
“A note on the dynamics of extended-context disordered kinetic spin models”, Journal of Physics A: Mathematical and Theoretical, vol. 59, no. 4, 2026.
J. A. Zavatone-Veth and C. Pehlevan,
“A note on the dynamics of extended-context disordered kinetic spin models”, Journal of Physics A: Mathematical and Theoretical, vol. 59, no. 4, 2026.
A. Atanasov, J. A. Zavatone-Veth, and C. Pehlevan,
“Scaling and renormalization in high-dimensional regression ”, J. Stat. Mech., vol. 2026, 2026.
A. Atanasov, J. A. Zavatone-Veth, and C. Pehlevan,
“Scaling and renormalization in high-dimensional regression ”, J. Stat. Mech., vol. 2026, 2026.
W. Qian and C. Pehlevan,
“Discovering alternative solutions beyond the simplicity bias in recurrent neural networks”, International Conference on Learning Representations (ICLR), 2026.
W. Qian and C. Pehlevan,
“Discovering alternative solutions beyond the simplicity bias in recurrent neural networks”, International Conference on Learning Representations (ICLR), 2026.
M. Letey, J. A. Zavatone-Veth, Y. M. Lu, and C. Pehlevan,
“Pretrain-Test Task Alignment Governs Generalization in In-Context Learning”, International Conference on Learning Representations (ICLR), 2026.
M. Letey, J. A. Zavatone-Veth, Y. M. Lu, and C. Pehlevan,
“Pretrain-Test Task Alignment Governs Generalization in In-Context Learning”, International Conference on Learning Representations (ICLR), 2026.
B. Bordelon, M. I. Letey, and C. Pehlevan,
“Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time”, International Conference on Learning Representations (ICLR), 2026.
B. Bordelon, M. I. Letey, and C. Pehlevan,
“Theory of Scaling Laws for In-Context Regression: Depth, Width, Context and Time”, International Conference on Learning Representations (ICLR), 2026.
A. Meterez, D. Morwani, J. Wu, C.-A. Oncescu, C. Pehlevan, and S. Kakade,
“Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling”, International Conference on Learning Representations (ICLR), 2026.
A. Meterez, D. Morwani, J. Wu, C.-A. Oncescu, C. Pehlevan, and S. Kakade,
“Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling”, International Conference on Learning Representations (ICLR), 2026.
M. Yaghoubi et al.,
“Predictive Coding of Reward in the Hippocampus”, Nature, 2026.
M. Yaghoubi et al.,
“Predictive Coding of Reward in the Hippocampus”, Nature, 2026.
I. Halder and C. Pehlevan,
“Demystifying LLM-as-a-Judge: Analytically Tractable Model for Inference-Time Scaling”, International Conference on Machine Learning (ICML), 2026.
I. Halder and C. Pehlevan,
“Demystifying LLM-as-a-Judge: Analytically Tractable Model for Inference-Time Scaling”, International Conference on Machine Learning (ICML), 2026.
B. Bordelon and C. Pehlevan,
“Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning”, arXiv preprint arXiv:2601.01010 , 2026.
B. Bordelon and C. Pehlevan,
“Disordered Dynamics in High Dimensions: Connections to Random Matrices and Machine Learning”, arXiv preprint arXiv:2601.01010 , 2026.
T. Jiang, B. Bordelon, C. Pehlevan, and B. Hanin,
“Hyperparameter Transfer with Mixture-of-Expert Layers”, International Conference on Machine Learning (ICML), 2026.
T. Jiang, B. Bordelon, C. Pehlevan, and B. Hanin,
“Hyperparameter Transfer with Mixture-of-Expert Layers”, International Conference on Machine Learning (ICML), 2026.
A. Meterez*, P. A. Nair*, D. Morwani*, C. Pehlevan, and S. Kakade,
“Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging”, arXiv preprint arXiv:2602.03702, 2026.
A. Meterez*, P. A. Nair*, D. Morwani*, C. Pehlevan, and S. Kakade,
“Anytime Pretraining: Horizon-Free Learning-Rate Schedules with Weight Averaging”, arXiv preprint arXiv:2602.03702, 2026.
Y. Liu, Z. Liu, C. Pehlevan, and J. Gore,
“Universal One-third Time Scaling in Learning Peaked Distributions”, International Conference on Machine Learning (ICML), 2026.
Y. Liu, Z. Liu, C. Pehlevan, and J. Gore,
“Universal One-third Time Scaling in Learning Peaked Distributions”, International Conference on Machine Learning (ICML), 2026.
B. Wang, J. Zavatone-Veth, and C. Pehlevan,
“A Random Matrix Theory Perspective on the Consistency of Diffusion Models”, International Conference on Machine Learning (ICML) (Oral), 2026.
B. Wang, J. Zavatone-Veth, and C. Pehlevan,
“A Random Matrix Theory Perspective on the Consistency of Diffusion Models”, International Conference on Machine Learning (ICML) (Oral), 2026.
W. L. Tong, E. Cakar, and C. Pehlevan,
“Boule or Baguette? A Study on Task Topology, Length Generalization, and the Benefit of Reasoning Traces ”, arXiv preprint arXiv:2602.14404, 2026.
W. L. Tong, E. Cakar, and C. Pehlevan,
“Boule or Baguette? A Study on Task Topology, Length Generalization, and the Benefit of Reasoning Traces ”, arXiv preprint arXiv:2602.14404, 2026.
D. G. Clark*, B. Bordelon*, J. A. Zavatone-Veth*, and C. Pehlevan,
D. G. Clark*, B. Bordelon*, J. A. Zavatone-Veth*, and C. Pehlevan,
I. Halder, A. Banerjee, and C. Pehlevan,
“Jailbreak Scaling Laws for Large Language Models: Polynomial-Exponential Crossover”, arXiv preprint arXiv:2603.11331, 2026.
I. Halder, A. Banerjee, and C. Pehlevan,
“Jailbreak Scaling Laws for Large Language Models: Polynomial-Exponential Crossover”, arXiv preprint arXiv:2603.11331, 2026.
A. Lee*, G. Kumar*, B. Bordelon, and C. Pehlevan,
“CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning”, International Conference on Machine Learning (ICML), 2026.
A. Lee*, G. Kumar*, B. Bordelon, and C. Pehlevan,
“CompleteP for RL: Maintaining Feature Learning When Scaling Deep Reinforcement Learning”, International Conference on Machine Learning (ICML), 2026.
2025
J. A. Zavatone-Veth, S. Yang*, J. A. Rubinfien*, and C. Pehlevan,
“How does training shape the Riemannian geometry of neural network representations?”, NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations (NeurReps) Proceedings (Oral), 2025.
J. A. Zavatone-Veth, S. Yang*, J. A. Rubinfien*, and C. Pehlevan,
“How does training shape the Riemannian geometry of neural network representations?”, NeurIPS 2025 Workshop on Symmetry and Geometry in Neural Representations (NeurReps) Proceedings (Oral), 2025.
S. Yang, P. Liu, and C. Pehlevan,
“Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space ”, Transactions on Machine Learning Research, 2025.
S. Yang, P. Liu, and C. Pehlevan,
“Convex Relaxation for Solving Large-Margin Classifiers in Hyperbolic Space ”, Transactions on Machine Learning Research, 2025.
S. Yang, J. A. Zavatone-Veth, and C. Pehlevan,
“Spectral regularization for adversarially-robust representation learning”, 2025 Asilomar Conference on Signals, Systems, and Computers, 2025.
S. Yang, J. A. Zavatone-Veth, and C. Pehlevan,
“Spectral regularization for adversarially-robust representation learning”, 2025 Asilomar Conference on Signals, Systems, and Computers, 2025.
A. Atanasov*, A. Meterez*, J. B. Simon*, and C. Pehlevan,
“The Optimization Landscape of SGD Across the Feature Learning Strength”, International Conference on Learning Representations (ICLR), 2025.
A. Atanasov*, A. Meterez*, J. B. Simon*, and C. Pehlevan,
“The Optimization Landscape of SGD Across the Feature Learning Strength”, International Conference on Learning Representations (ICLR), 2025.
E. Attias, C. Pehlevan, and D. Obeid,
“Pixel-Based Similarities as an Alternative to Neural Data for Improving Convolutional Neural Network Adversarial Robustness ”, 2025 Asilomar Conference on Signals, Systems, and Computers, 2025.
E. Attias, C. Pehlevan, and D. Obeid,
“Pixel-Based Similarities as an Alternative to Neural Data for Improving Convolutional Neural Network Adversarial Robustness ”, 2025 Asilomar Conference on Signals, Systems, and Computers, 2025.
W. Tong and C. Pehlevan,
“MLPs Learn In-Context on Regression and Classification Tasks”, International Conference on Learning Representations (ICLR), 2025.
W. Tong and C. Pehlevan,
“MLPs Learn In-Context on Regression and Classification Tasks”, International Conference on Learning Representations (ICLR), 2025.
H. Cui, C. Pehlevan, and Y. M. Lu,
“A solvable model of learning generative diffusion: theory and insights”, Advances in Neural Information Processing Systems (NeurIPS), 2025, 2025.
H. Cui, C. Pehlevan, and Y. M. Lu,
“A solvable model of learning generative diffusion: theory and insights”, Advances in Neural Information Processing Systems (NeurIPS), 2025, 2025.
B. Bordelon and C. Pehlevan,
“Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer”, International Conference on Machine Learning (ICML), 2025.
B. Bordelon and C. Pehlevan,
“Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer”, International Conference on Machine Learning (ICML), 2025.
C. Lauditi, B. Bordelon, and C. Pehlevan,
“Adaptive kernel predictors from feature-learning infinite limits of neural networks”, International Conference on Machine Learning (ICML), 2025.
C. Lauditi, B. Bordelon, and C. Pehlevan,
“Adaptive kernel predictors from feature-learning infinite limits of neural networks”, International Conference on Machine Learning (ICML), 2025.
H. T. Chaudhry, M. Kulkarni, and C. Pehlevan,
“Test-time scaling meets associative memory: Challenges in subquadratic models”, ICLR Workshop: New Frontiers in Associative Memories, 2025.
H. T. Chaudhry, M. Kulkarni, and C. Pehlevan,
“Test-time scaling meets associative memory: Challenges in subquadratic models”, ICLR Workshop: New Frontiers in Associative Memories, 2025.
G. Kumar, B. Bordelon, J. A. Zavatone-Veth, and C. Pehlevan,
“Place Field Representation Learning During Policy Learning”, Second Workshop on Representational Alignment at ICLR 2025, 2025.
G. Kumar, B. Bordelon, J. A. Zavatone-Veth, and C. Pehlevan,
“Place Field Representation Learning During Policy Learning”, Second Workshop on Representational Alignment at ICLR 2025, 2025.
T. Kumar, B. Bordelon, C. Pehlevan, V. N. Murthy, and S. J. Gershman,
“Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex ”, International Conference on Learning Representations (ICLR), 2025.
T. Kumar, B. Bordelon, C. Pehlevan, V. N. Murthy, and S. J. Gershman,
“Do Mice Grok? Glimpses of Hidden Progress During Overtraining in Sensory Cortex ”, International Conference on Learning Representations (ICLR), 2025.
W. L. Tong and C. Pehlevan,
“Learning richness modulates equality reasoning in neural networks”, Computational Cognitive Neuroscience (CCN) Proceedings, 2025.
W. L. Tong and C. Pehlevan,
“Learning richness modulates equality reasoning in neural networks”, Computational Cognitive Neuroscience (CCN) Proceedings, 2025.
G. Kumar, A. Manoogian, W. Qian, C. Pehlevan*, and S. A. Rhoads*,
“Neurocomputational underpinnings of suboptimal beliefs in recurrent neural network-based agents”, Computational Cognitive Neuroscience (CCN) Proceedings, 2025.
G. Kumar, A. Manoogian, W. Qian, C. Pehlevan*, and S. A. Rhoads*,
“Neurocomputational underpinnings of suboptimal beliefs in recurrent neural network-based agents”, Computational Cognitive Neuroscience (CCN) Proceedings, 2025.
B. Bordelon, J. Cotler, C. Pehlevan, and J. A. Zavatone-Veth,
“Dynamically Learning to Integrate in Recurrent Neural Networks”, arXiv preprint arXiv:2503.18754, 2025.
B. Bordelon, J. Cotler, C. Pehlevan, and J. A. Zavatone-Veth,
“Dynamically Learning to Integrate in Recurrent Neural Networks”, arXiv preprint arXiv:2503.18754, 2025.
R. Zhao*, A. Meterez*, S. Kakade, C. Pehlevan, S. Jelassi, and E. Malach,
“Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining”, The Conference on Language Modeling (COLM), 2025.
R. Zhao*, A. Meterez*, S. Kakade, C. Pehlevan, S. Jelassi, and E. Malach,
“Echo Chamber: RL Post-training Amplifies Behaviors Learned in Pretraining”, The Conference on Language Modeling (COLM), 2025.
Y. M. Lu, M. I. Letey, J. A. Zavatone-Veth*, A. Maiti*, and C. Pehlevan,
“Asymptotic theory of in-context learning by linear attention ”, Proceedings of the National Academy of Sciences (PNAS), vol. 122, no. 28, 2025.
Y. M. Lu, M. I. Letey, J. A. Zavatone-Veth*, A. Maiti*, and C. Pehlevan,
“Asymptotic theory of in-context learning by linear attention ”, Proceedings of the National Academy of Sciences (PNAS), vol. 122, no. 28, 2025.
M. Erdogan, C. Pehlevan, and A. T. Erdogan,
“Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism”, Advances in Neural Information Processing Systems (NeurIPS) (Spotlight), 2025, 2025.
M. Erdogan, C. Pehlevan, and A. T. Erdogan,
“Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism”, Advances in Neural Information Processing Systems (NeurIPS) (Spotlight), 2025, 2025.
J. A. Zavatone-Veth, B. Bordelon, and C. Pehlevan,
“Summary statistics of learning link changing neural representations to behavior”, Frontiers in Neural Circuits, vol. 19, 2025.
J. A. Zavatone-Veth, B. Bordelon, and C. Pehlevan,
“Summary statistics of learning link changing neural representations to behavior”, Frontiers in Neural Circuits, vol. 19, 2025.
B. S. Ruben, W. L. Tong, H. T. Chaudhry, and C. Pehlevan,
“No Free Lunch From Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions”, International Conference on Machine Learning (ICML), 2025.
B. S. Ruben, W. L. Tong, H. T. Chaudhry, and C. Pehlevan,
“No Free Lunch From Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions”, International Conference on Machine Learning (ICML), 2025.
J. A. Zavatone-Veth and C. Pehlevan,
“Nadaraya-Watson kernel smoothing as a random energy model ”, J. Stat. Mech., vol. 2025, 2025.
J. A. Zavatone-Veth and C. Pehlevan,
“Nadaraya-Watson kernel smoothing as a random energy model ”, J. Stat. Mech., vol. 2025, 2025.
N. Dey et al.,
“Don’t be lazy: CompleteP enables compute-efficient deep transformers”, Advances in Neural Information Processing Systems (NeurIPS), 2025, 2025.
N. Dey et al.,
“Don’t be lazy: CompleteP enables compute-efficient deep transformers”, Advances in Neural Information Processing Systems (NeurIPS), 2025, 2025.
A. Atanasov, J. A. Zavatone-Veth, and C. Pehlevan,
“Risk and cross validation in ridge regression with correlated samples ”, International Conference on Machine Learning (ICML), 2025.
A. Atanasov, J. A. Zavatone-Veth, and C. Pehlevan,
“Risk and cross validation in ridge regression with correlated samples ”, International Conference on Machine Learning (ICML), 2025.
A. Meterez, D. Morwani, C.-A. Oncescu, J. Wu, C. Pehlevan, and S. Kakade,
“A Simplified Analysis of SGD for Linear Regression with Weight Averaging”, NeurIPS Workshop OPT 2025: Optimization for Machine Learning, 2025.
A. Meterez, D. Morwani, C.-A. Oncescu, J. Wu, C. Pehlevan, and S. Kakade,
“A Simplified Analysis of SGD for Linear Regression with Weight Averaging”, NeurIPS Workshop OPT 2025: Optimization for Machine Learning, 2025.
B. Bordelon*, A. Atanasov*, and C. Pehlevan,
“How Feature Learning Can Improve Neural Scaling Laws”, International Conference on Learning Representations (ICLR) (Spotlight), 2025.
B. Bordelon*, A. Atanasov*, and C. Pehlevan,
“How Feature Learning Can Improve Neural Scaling Laws”, International Conference on Learning Representations (ICLR) (Spotlight), 2025.
B. Bordelon, A. Atanasov, and C. Pehlevan,
“How feature learning can improve neural scaling laws”, Journal of Statistical Mechanics: Theory and Experiment (JSTAT), vol. 8, 2025.
B. Bordelon, A. Atanasov, and C. Pehlevan,
“How feature learning can improve neural scaling laws”, Journal of Statistical Mechanics: Theory and Experiment (JSTAT), vol. 8, 2025.
B. Wang and C. Pehlevan,
“An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models”, Advances in Neural Information Processing Systems (NeurIPS) (Spotlight), 2025.
B. Wang and C. Pehlevan,
“An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models”, Advances in Neural Information Processing Systems (NeurIPS) (Spotlight), 2025.