Anthropic researcher liv Gorton rebuts paper claiming superposition is undermined

On October 16, Anthropic researcher liv Gorton posted a lengthy thread rebutting a recent interpretability (interp) paper's conclusions. That paper argues that "neurons are underrated" and uses this to challenge the superposition hypothesis; Gorton contends the evidence is insufficient to support such a strong claim as "a core idea of interp is built on an error and has now been overturned." Her response is the most systematic statement in the current debate and worth close reading.

Confirmed

• Gorton stresses that superposition is first and foremost a claim about geometry: a network layer represents more features than its dimensionality via "almost orthogonal" directions, and features interfere with each other, but this interference is tolerable given feature sparsity; this has been the definition since 2020.
• She notes superposition is not a "radical" phenomenon: a d-dimensional space has only d orthogonal directions but exponentially many near-orthogonal ones; given the sparsity of neural network representations, it would be surprising if networks didn't exploit this capacity.
• Against the paper's rebuttal that "a fully monosemantic network can also have more features than dimensions because neurons compose," she counters: composition yields more "ways of combining," not more "features"—the two are not the same thing.
• She argues composition and superposition are not mutually exclusive; it is fully expected that neural networks use both simultaneously. Depending on how often features co-occur and their sparsity, the two involve different interference cost trade-offs, and in some regimes composition offers better or worse interference costs.
• She criticizes the paper's vague framing of the residual stream: "you can fit far more than the dimension count just by putting things in the right directions, so that alone doesn't count as packing"—she says she can only read this as a claim about composition, which makes the disagreement especially murky.
• At the end of the thread (tweet 14), she acknowledges that "neurons are more interpretable-friendly than commonly believed" has already been argued by others (e.g., Arora et al., 2025) and is an important direction; she also raises the prospect of leveraging network developmental structure to make interpretability easier.

Background

• Nick Cammarata, an early mechanistic interpretability researcher formerly at OpenAI, wrote a long piece arguing interpretability may be the most important scientific question today—alignment cannot be solved without deconstructing models. His early OpenAI work on mechanistic interpretability left him pessimistic (the original tools could then read out only 2% of neurons); this time he believes most neurons are now readable, and Gorton wrote her piece to rebut his misreading of the paper on the superposition hypothesis.

Why it matters

• Superposition is one of the core concepts in current mechanistic interpretability research; if it were "overturned," the premises of much subsequent work would be shaken. Gorton's response clarifies key notions such as the geometric definition and the distinction between composition and superposition, preventing readers from drawing overly strong conclusions from the paper, while affirming the value of "neurons may be underrated" and the developmental-structure perspective as research directions.

2026-10-16 ~ 2026-10-16 · 11 related posts

• Superposition is a geometric claim: near-orthogonal directions pack more features per layer — livgorton · 2026-10-16
• [source] Researcher rebuts superposition skeptics: near-orthogonal capacity, interference and recursive generalization — livgorton · 2026-10-16
• d dimensions, exponentially many near-orthogonal directions: why superposition is inevitable in neural nets — livgorton · 2026-10-16
• Composition gives more combinations, not more features: rebutting an anti-superposition essay — livgorton · 2026-10-16
• Composition and superposition aren't mutually exclusive — and each has worse interference regimes — livgorton · 2026-10-16
• The residual stream muddies the debate: is placing things in the right directions really packing? — livgorton · 2026-10-16
• Neurons may be underrated for interpretability — and developmental structure could make it easier — livgorton · 2026-10-16
• [source] Anthropic researcher pushes back on viral interp paper: evidence doesn't overturn the field — livgorton · 2026-10-16
• Interpretability debate: Nick Cammarata says most neurons are legible, Liv Gorton pushes back — sethlazar · 2026-10-16
• [source] Interp researchers push back: new findings don't overturn core interpretability ideas — menhguin · 2026-10-16
• Do polysemantic features hide useful structure? An interpretability debate — menhguin · 2026-10-16