J++ Lens: Jacobian Filtering Enables More Faithful Workspace Lenses
Kola Ayonrinde
Abstract
Kola Ayonrinde, Anthropic Fellows, koayon@gmail.com ; Jack Lindsey, Anthropic TL;DR : The J-Lens was proposed to read verbalisable representations from language model activations. We introduce the J++ Lens: an improvement to the J-Lens that enables more faithful readouts across 9–284B models. The J++ Lens filters noisy gradients before they enter the averaged Jacobian. Using the J++ Lens as a drop-in replacement for the J-Lens, we read workspace representations with 55% success on latent variable extraction tasks (J-Lens: 36%, R-Lens: 38%) with especially strong performance at early layers. We believe that more faithful lenses can be useful in monitoring for eval awareness and unverbalised scheming. We release code , an interactive demo and open-source lenses . Figure 1. By filtering out noisy gradients, the J++ Lens reads intermediate variables more reliably (a) and at earlier layers (b) than the J-Lens and R-Lens. The J++ Lens improves on the J-Lens by adding: (1) mjx-container[jax="CHTML"] { line-height: 0; } mjx-container [space="1"] { margin-left: .111em; } mjx-container [space="2"] { margin-left: .167em; } mjx-container [space="3"] { margin-left: .222em; } mjx-container [spac