What Claude Thinks Before It Speaks
Anthropic has given researchers the clearest window yet into the inner workings of a large language model during conversation, revealing a hidden computational region where individual words flicker into existence before the model decides what to say. The company calls this the J-space, and it was discovered using a new probing technique dubbed the Jacobian lens, or J-lens. In experiments on Claude Opus 4.6—a version of the firm’s flagship model released this February—researchers found that the J-space consistently contains words semantically related to the eventual output, even when the model ultimately chooses a different phrasing or withholds a response entirely. The findings were posted to Anthropic’s website this week and accompanied by an interactive demo built with the open‑source platform Neuronpedia, allowing anyone to explore the phenomenon directly.
The discovery represents a significant step forward in mechanistic interpretability, a field that tries to decode how AI models think by examining their internal activations. MIT Technology Review named mechanistic interpretability one of this year’s 10 Breakthrough Technologies, and Anthropic has been at the forefront—publishing studies on individual neurons, feature visualization, and monosemanticity. The J‑lens goes deeper than previous methods by exploiting the Jacobian matrix, a mathematical construct that captures how small changes in intermediate layers influence the final output probabilities. “It’s very good and interesting work,” said Tom McGrath, chief scientist and cofounder at Goodfire, a startup also building interpretability tools. According to the paper, the technique reveals not just abstract concepts but individual tokens that the model is actively weighing, often several layers below the output head.
How the Jacobian Lens Exposes Hidden Tokens
Large language models like Claude are typically described as a stack of transformer layers, each containing thousands of neurons. Information flows upward: early layers process raw input text, and late layers generate the probability distribution for the next token. In between, activations were thought to encode increasingly abstract concepts. Anthropic’s J‑lens challenges that picture. By computing the Jacobian of the model’s logit outputs with respect to activations at an intermediate layer, researchers can identify a “max‑activating token”—the single word most sensitive to that activation pattern. When they applied this across many positions, they uncovered a dedicated subspace where these tokens cluster, which they termed J‑space.
In one illustrative example, when Claude was asked “What is the capital of France?”, the J‑space in early layers contained not only “Paris” but also “city”, “Europe”, and “France” itself—words that did not appear in the final answer but that the model considered conceptually. In more unsettling cases, when prompted with a misleading or manipulative question, the J‑space sometimes contained tokens that reflected a different line of reasoning than what the model ultimately said aloud. This mismatch suggests that a model’s stated chain‑of‑thought may not always align with the computations happening under the hood, a finding with profound implications for trust and alignment.
From Monosemanticity to J‑Space: A Brief History
Anthropic’s latest work builds on a series of interpretability milestones. In 2023, the company demonstrated that individual neurons in a toy language model could be linked to specific concepts, a phenomenon called monosemanticity. Later that year, they scaled the approach to Claude, finding millions of features corresponding to everything from DNA sequences to legal jargon. But those techniques required painstaking analysis of each feature in isolation. The J‑lens offers a different approach: instead of cataloguing features one by one, it monitors the model’s shifting token preferences in real time as it processes a prompt. This allows researchers to watch how competing tokens rise and fall across layers, essentially tracing the model’s decision pathway.
The tool’s name comes from the Jacobian matrix, a cornerstone of multivariable calculus. In the context of neural networks, the Jacobian captures how a perturbation in an intermediate layer ripples through the rest of the computation. By peering through the lens at a specific layer, researchers can approximate what the model is “thinking” at that moment. The team found that the J‑space is remarkably stable across different runs and that its contents can be decoded without any specialized training—the max‑activating tokens are simply the ones with the highest Jacobian scores from the model’s own unembedding matrix. This means the technique could potentially be applied to other transformer‑based models, though Anthropic has only tested it on Claude Opus 4.6 so far.
Why Hidden States Matter for AI Safety
Monitoring J‑space could become a practical tool for detecting undesirable model behaviors before they manifest in output. If a model begins computing tokens related to deception, bias, or harmful instructions, those tokens would appear in J‑space even if the model later suppresses them. Anthropic’s paper describes early experiments where the J‑lens flagged tokens indicative of sycophancy or misaligned reasoning, suggesting it could serve as an early warning system. The demo on Neuronpedia lets users input their own prompts and see a live visualization of which tokens light up in the model’s hidden layers, making the abstract concepts of mechanistic interpretability tangible for developers and researchers.
However, the approach is not without limitations. The J‑space is not a complete representation of the model’s internal state; it captures only the most strongly projected tokens. Context that is distributed across many neurons without a clear lexical correlate may be invisible to the lens. Furthermore, the technique relies on having access to the model’s full weights and activations—something only available for open or in‑house models. Despite these caveats, the ability to watch tokens form before they are spoken could be a game‑changer for red‑teaming and alignment audits. “What an LLM is actually doing can often be different from what it says it is doing,” the Anthropic researchers note, and the J‑lens provides a direct way to check that discrepancy.
What Comes Next
Anthropic plans to extend the J‑lens method to more models and layers, and to investigate whether the hidden tokens can be steered to influence model behavior in beneficial ways. The collaboration with Neuronpedia points toward a future where interpretability tools are not just research artifacts but interactive aids used in model development. For the broader AI community, the discovery of J‑space reinforces the lesson that even the most advanced models are still largely black boxes. As policymakers increasingly demand transparency from AI systems, techniques like the Jacobian lens may become as important as accuracy benchmarks. Developers working with large language models—whether they’re building enterprise chatbots or autonomous agents—should watch this space closely. If J‑lens proves reliable across architectures, it could fundamentally change how we validate, debug, and trust the next generation of AI systems.
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