Audio note: this post contains a lot of mathematical notation. We narrate the simpler formulas and flag the complex ones, so you may want the original text to hand.

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) : down-weighting the Jacobians from activations that result in poor readouts; (2) : applying Layer-wise Relevance Propagation to the [...]