Latent Correctness Directions Emerge from Residual Activations Without Probes
Latent correctness directions appear in residual activations of language models when using Jacobian lenses and sparse autoencoders instead of probes.
Editorial Team
The J-Space Editorial Team curates and synthesizes cutting-edge research in mechanistic interpretability, AI alignment, and cognitive science. With a commitment to precision, empirical rigor, and clear exposition, the team distills complex technical developments into accessible yet authoritative analyses for researchers, engineers, and policymakers working at the frontier of artificial intelligence.
How J-space Detects Model Misalignment
In brief J-space detects model misalignment by examining a compact internal workspace in large language models that handles deliberate reasoning, using the…
Jacobian Lens for AI Safety: Reading a Language Model’s Hidden Thoughts
In brief Mid-layer activations in language models are projected by the Jacobian lens through transport matrices derived from averaged input-output…
Anthropic Interpretability Research Explained: Inside the Push for Transparent AI
In brief Anthropic's interpretability research employs dictionary learning with sparse autoencoders to extract monosemantic features from the internal…
Global Workspace Theory and Transformer Attention for Modeling Conscious Information Processing
at a Glance Transformer Attention wins for AI researchers and engineers building systems at global scale because of its parallel processing and O(n²)…
What Does the Jacobian Lens Reveal About Claude’s Internal Representations?
The jacobian lens is a gradient-based method that identifies j space in Claude's internal representations, a sparse subspace accounting for less than 10%…
Emergent Workspace in LLMs (2026): J‑Space, Jacobian Lens, and the Future of Internal Reasoning
The emergent workspace in LLMs 2026 is a limited-capacity subspace inside the residual stream of large language models that holds roughly 25 verbalizable…
What Is the Jacobian Lens and How Does It Reveal J-Space?
The Jacobian lens uses input-output Jacobians to find j space, a low-dimensional workspace of verbalizable concepts. How to compute it and what it reveals.
J-Space as a Workspace in Language Models: Insights for Interpretability
How j-space works as a limited-capacity workspace in language models, and what circuit tracing and attribution graphs show about reportable reasoning.
What Is J-Space in AI Interpretability? A Deep Guide for Researchers
J-space is a small internal workspace in large language models, found with the Jacobian lens. What it is, how it was found, and what it means for AI safety.