Fast and Easy Infinite Neural Networks in Python

CVPR 2024-Improved Implicit Neural Representation with Fourier Reparameterized Training

ICML2025-Inductive Gradient Adjustment for Spectral Bias in Implicit Neural Representations

Existing literature about training-data analysis.

A unified framework for attributing model behavior to model components, training data, and training dynamics.

Official repository for "FOCUS: First Order Concentrated Updating Scheme"

Code for "What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers" (NeurIPS 2025)

Code for 'Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics'

Code for "Effect of equivariance on training dynamics"

Source code for <Probability Consistency in Large Language Models: Theoretical Foundations Meet Empirical Discrepancies>

Official repository for the EMNLP 2024 paper "How Hard is this Test Set? NLI Characterization by Exploiting Training Dynamics"

External LLM intelligence monitors & diagnoses MoE expert ecology during training — preventing routing collapse without auxiliary loss engineering. 16 Experts, 3 Tiers, Top-2 Gating, Claude-in-the-Loop.

TMLR 2026 | Mechanistic interpretability: attention-head binding (EB*) as a marker of concept emergence. 7 models, 5 architectures (Pythia 160M–2.8B, OLMo-1B, CRFM GPT-2, SmolLM3-3B, Qwen2.5-1.5B), 41 terms.

Cross-Family Convergence of Neural Network Weight Skeletons. Companion to Zenodo paper (10.5281/zenodo.19652706).

A two-parameter Weibull lens on transformer weights — diagnose weight-magnitude distributions (shape k, scale λ) across 7 model families, and explain how λ evolves under AdamW training. Library (npm-weibull-py) + benchmark database + companion code for arXiv:2605.18898 and 2606.19367.

A plug-in debugger and visualizer for RL reward functions. Detects reward hacking, tracks training health, and renders a live terminal dashboard.

Code and data for: Three Phases of Expert Routing — How Load Balance Evolves During MoE Training

Code for "Abrupt Learning in Transformers: A Case Study on Matrix Completion" (NeurIPS 2024)

Reimplementation of the Sliced Information Plane (SIP) framework from Wongso, Ghosh, and Motani (2025) for analyzing deep neural network training dynamics. The repo uses Sliced Mutual Information (SMI) to obtain scalable, finite dependence estimates in high‑dimensional, deterministic settings, and applies them to MNIST MLP experiments.

σFlow-PDE: A drop-in H-Bar training engine that escapes the σ-trap in neural PDE solvers via live σ/δ/α ODE integration, autonomous phase curriculum, and auto-falsification.