Score Hidden Objectives Before Compliant Tokens Appear
Score hidden objectives before a compliant token emits. J-Lens reads residual Jacobians as a complementary readout, not an alignment proof.
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.
Confirm Layer Selection With Patching Before You Trust Attention Maps
Attention weights leave the real selector hidden. State a claim, then patch activations to show which residual hypotheses a layer amplifies or suppresses.
What the J Lens Protocol Isolates That a Logit Lens Cannot
Isolate causal paths and confirm necessity on residual streams with the J Lens joint lens and first-order Jacobian stack.
Tracing Causal Paths in Claude’s Addition and Safety Circuits
Researchers map how features causally influence Claude outputs by reconstructing prompt specific graphs with sparse autoencoders and cross layer…
Sparse Autoencoders Use L1 Penalties to Isolate Distinct Model Features
Sparse autoencoders use L1 penalties on residual activations to generate sparse mostly zero latents that correspond to candidate features in language…
Safety Teams Decode AI Models Using Mechanistic Interpretability in 2026
Safety teams use mechanistic interpretability to reveal AI behaviors missed by tests, making safety operational in 2026.
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.
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…