jacobian-lens

This article tests the transferability of a Jacobian interpretability lens from Qwen3.6-27B to Qwen3.8-27B, finding that it can read and steer the newer model with zero refitting for specific tasks.

This paper presents evidence that language models maintain a privileged set of internal representations (J-space) that are verbally reportable and function like a global workspace, introducing the Jacobian lens interpretability technique. The findings offer a window into model cognition and have implications for alignment and safety.

This blog post extends Anthropic's verbalizable workspace paper by measuring how far steering directions from middle layers reach, when the structure forms during training, whether it transfers between models, and how it scales, all on open models.

J-Wash is a tool that uses Anthropic's Jacobian lens to let users edit the behavior and identity of large language models by modifying token directions, then export the edited model as a standalone checkpoint without training or fine-tuning.

This study evaluates whether J-space entropy (inspired by Anthropic's Jacobian Lens) can serve as an error predictor across seven datasets on Qwen3-4B. Results show it can complement output confidence for factual retrieval but is not a general hallucination detector, with strong task dependence.

An interactive Jacobian-lens visualizer and live steerer for GGUF models running on llama.cpp, enabling real-time model interpretability and control.

The author created a tool based on Anthropic's Jacobian-Lens to manually tweak a model's Jacobian Space, producing an uncensored model called Nikusui-v1, released with GGUF quantizations.

Anthropic developed the Jacobian lens (J-lens) to reveal a hidden 'J-space' inside Claude Opus 4.6, offering unprecedented insight into an LLM's internal reasoning process before it outputs tokens. The technique allows monitoring and control of model behavior by surfacing the words the model is about to produce.

An article discussing a new interpretability technique called the Jacobian Lens and the discovery of J-space, a region in LLMs where verbalizable representations form a global workspace, marking a significant advance in understanding LLM reasoning.

A public demo of a Jacobian-lens view of language model representations, inspired by Anthropic's JSpace paper, allowing users to explore model internals across layers and tokens.

The author tested Anthropic's Jacobian Lens on open models, then it evolved into a local-model hallucination router for detecting AI hallucinations.

A developer built Subtext, a tool that visualizes the internal 'silent words' of language models using Anthropic's Jacobian lens, allowing real-time observation of the model's reasoning before it outputs tokens. The tool runs on a single 12GB GPU and streams at full generation speed.