Jacobian Lens (J-Lens) and J-Space Toolkit for transformer mechanistic interpretability. Train linear lenses, decompose hidden states, and run causal interventions on decoder-only models.
-
Updated
Sep 16, 2026 - Python
Jacobian Lens (J-Lens) and J-Space Toolkit for transformer mechanistic interpretability. Train linear lenses, decompose hidden states, and run causal interventions on decoder-only models.
Mechanistic interpretability of code models through probing, perturbations, and causal interventions
An independent, from-scratch reproduction of the mechanistic-interpretability findings in Anthropic's When Models Manipulate Manifolds: The Geometry of a Counting Task
Mechanistic interpretability of multilingual reasoning in transformers. 170+ causal intervention experiments across 4 model families.
Evaluate mechanistic estimates through the interventions, control loops, and safety decisions they guide.
Code for SCIT: cache-level causal diagnostics for latent chain-of-thought models (EMNLP 2026 Findings)
Ouro-1.4B SAE-derived loop-boundary causal pilot: matched-control interventions, FP16/FP32 robustness analysis, reproducible figures, and scientific audit.
Knows ≠ uses: measure when a language model has a feature but does not use it, and observe it
Experiments and analysis for studying overtopping in LLMs: high-leverage activation channels, intervention composition, dose and decode-time event localization, and learning-time causal reorganization.
Mechanistic study of contextual-integrity post-training in Qwen2.5-7B, testing whether improved privacy behavior comes from new mechanisms or better use of machinery already present in the base model.
Code, notebooks, and archived results for reproducing the analyses in “Context Sufficiency in Transformers” (Computers, 2026).
Research-engineering framework for Transformer mechanistic interpretability, causal interventions, activation patching, and reproducible experiments on GPT-2 and SmolLM3-3B.
Behavioral auditing of finetuned language models using blinded policy recovery, model diffing, and causal activation interventions.
A controlled mechanistic interpretability study testing whether representation–self-report alignment survives a targeted modification to a language model.
To associate your repository with the causal-interventions topic, visit your repo's landing page and select "manage topics."