A complete end-to-end pipeline for LLM interpretability with sparse autoencoders (SAEs) using Llama 3.2, written in pure PyTorch and fully reproducible.

Fast XAI with interactions at large scale. SPEX can help you understand the output of your LLM, even if you have a long context!

Automates attribution-graph analysis via probe prompting: circuit-trace a prompt, auto-generate concept probes, profile feature activations, cluster supernodes.

A minimal mech-interp project for steering LLMs

Spectral Attention Divergence — dynamical systems probe for LLM inference via dual-path attention comparison and delay-coordinate attractor reconstruction

Replication of 'From Reasoning to Answer' (EMNLP 2025) — Reasoning-Focus Heads + Activation Patching on DeepSeek-R1-Distill-Qwen-7B

This project explores methods to detect and mitigate jailbreak behaviors in Large Language Models (LLMs). By analyzing activation patterns—particularly in deeper layers—we identify distinct differences between compliant and non-compliant responses to uncover a jailbreak "direction." Using this insight, we develop intervention strategies that modify

Normative, machine-first definition of Interpretive SEO (SEO interprétatif), aligned with the Interpretive Governance standard.

Cross-patching diagnostic showing instruction-tuned late effects depend on upstream state.

Fine-tuned DistilBERT for binary sentiment analysis on IMDB movie reviews with token-level interpretability using LayerIntegratedGradients

Real-time 3D visualization of LLM cognitive states — explore transformer geometry during inference

Studying LLM latent space topology using persistent homology and Hodge decomposition. Discovers that Hodge gradient potential recovers the animacy hierarchy — a linguistic universal — without supervision.

Layerwise diagnostic showing instruction-tuned LLMs settle on next-token predictions later.