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structures / Manifolds / Paraboloid (circular × pinched continuum)

Paraboloid (circular × pinched continuum)

OBJECTgeometric-objectembedded-setK:variableparametrizationmanifoldintermediatehow it's classified →

A surface of revolution combining a circular coordinate with a continuous 'axial' coordinate, where the circle's radius (spread) varies with — and can shrink toward zero at — position along the axis, unlike a flat cylinder (S¹ × ℝ) where the circle's radius stays constant.

Replicationcomputed from the corpus — never hand-assigned
1 paper1 architecture class1 domain1 model family
Filled = two or more values reported by papers that share no author — replication. Outlined = two or more values, but all from a single study — breadth, not replication. Grey = a single value. Derived from paper authorship and each model's architecture class, domain and family; it updates itself when a paper is added.

Definition

A paraboloid of revolution is generated by rotating a profile curve r=f(h)r = f(h) around an axis, giving a surface parametrised by an angular coordinate θS1\theta \in S^1 and an axial coordinate hh:

(f(h)cosθ, f(h)sinθ, h),θ[0,2π)\big(f(h)\cos\theta,\ f(h)\sin\theta,\ h\big), \qquad \theta \in [0, 2\pi)

The defining feature relative to a cylinder (f(h)constf(h) \equiv \text{const}, i.e. S1×RS^1 \times \mathbb{R}, where the circle’s radius never changes) is that ff is genuinely a function of hh — the circle’s radius grows or shrinks as hh varies, and can shrink to a single point (f(h)=0f(h)=0) at one or both ends of hh‘s range. The classical paraboloid z=x2+y2z = x^2+y^2 is the special case f(h)=hf(h) = \sqrt{h}, one-sided and unbounded; the map’s use of the term (per Bhalla et al., 2026, below) is the qualitative shape — radius as a non-constant function of the axial coordinate — rather than this specific quadratic profile, which the source paper does not state in closed form.

Why this structure / Why it matters

A cylinder (S1×RS^1 \times \mathbb{R}) is the “independent” combination of a circular and a linear factor — Circle and 1D continuum manifold/Affine Subspace simply stacked with no interaction between them. A paraboloid is the same two coordinates coupled: how much the circular factor can vary depends on where you are along the axial one. This matters representationally because it means the two “factors” of a concept (e.g. hue and lightness) are not cleanly separable/orthogonal in the way a Cartesian product would be — probing or steering along the circular coordinate has different effective range depending on the axial coordinate’s value.

Relative to manifolds-hyperboloid

Both are quadric-family local shapes under Manifolds, but arise from different detection methods and different domains: this node’s paraboloid shape is read directly off PCA loop-radius behavior (a parametrization-mode claim, circular hue × continuous lightness in color representations), whereas Local hyperboloid-type quadric patch‘s shape is established via implicit polynomial surface fitting and eigenvalue- based quadric-type classification on controlled sentence-paraphrase neighbourhoods. Neither generalizes the other; they are sibling instances of the same quadric-surface family found by different means in different representational domains.

Key evidence

Bhalla et al. (2026) report that color representations in Llama-3.1-8B (residual stream, layer 19) organize along “a paraboloid with circular hue and lightness dimensions” (their Sec. 3, Fig. 2 left) rather than a flat hue-circle × lightness-line cylinder — i.e. the spread of hue-distinct representations is not constant across the lightness coordinate. They validate this is not a merely visual/PCA artifact: activation-patching steering interventions along the manifold’s principal components (moving between hex-code prompts’ PCA centroids) produce smooth, monotonic changes in the model’s predicted color-name tokens (their App. B.1, Table 2), indicating the structure is causally load-bearing, not incidental. See color-paraboloid for the full Observation.

How to detect it

Fit PCA (or another dimensionality-reduction method) to activations varying a single concept with two known sub-factors (one cyclic, e.g. hue; one continuous, e.g. lightness). Check the resulting 2-3-component projection for (a) a closed loop at each fixed value of the continuous factor (confirming the circular part), and (b) whether that loop’s radius changes across the continuous factor’s range — constant radius indicates a cylinder, non-constant (especially radius 0\to 0 at an extreme) indicates a paraboloid-like coupling instead.

Key papers

  • Bhalla, Fel, Rager, Feucht, Haklay, Wurgaft, Boppana, Kowal, Shyam, Lewis, McGrath, Merullo, Geiger & Lubana (2026). Do Sparse Autoencoders Capture Concept Manifolds? arXiv:2604.28119 — origin of this characterization in this map’s corpus, for color representations.

Exercises

Base

  1. Is a cylinder S1×RS^1 \times \mathbb{R} a special case of the paraboloid parametrisation above? If so, what is f(h)f(h)?
Solution

Yes: taking f(h)=cf(h) = c (a positive constant, independent of hh) recovers exactly S1×RS^1 \times \mathbb{R} — the circle’s radius never changes as hh varies. The paraboloid family is strictly more general, since it also allows non-constant ff.

  1. If f(h0)=0f(h_0) = 0 for some h0h_0, what happens to the surface at that value of hh, and is the result still a manifold everywhere?
Solution

At h=h0h=h_0 the entire circle of points collapses to the single point (0,0,h0)(0,0,h_0) — every value of θ\theta maps to the same point, so the parametrisation is not injective there. This is analogous to the apex of Cone: that single point need not have a Euclidean neighbourhood matching the rest of the surface, so the object is generally a manifold on hh0h \neq h_0 but may fail to be one exactly at h0h_0 depending on how the surface is completed there.

Middle

  1. A researcher measures the PCA radius of the hue loop at three lightness values and finds radii (0.9,0.9,0.9)(0.9, 0.9, 0.9) (within noise). Does this support a paraboloid or a cylinder account of the underlying geometry? What additional lightness values would make the test more conclusive?
Solution

Constant radius across the three sampled lightness values supports a cylinder, not a paraboloid — a paraboloid’s defining signature is radius varying with the axial coordinate. The test is more conclusive if it includes lightness values near the extremes of the range (very dark, very light), since that is where a paraboloid-like pinching (if present) would be most visible; three values clustered near the middle of the range could miss pinching that only occurs near the boundaries.

Found in (2 observations · 1 family)

Llama

Do Sparse Autoencoders Capture Concept Manifolds? (2026)measured

Color representations trace a paraboloid (circular hue and lightness)

Details

Bhalla et al. show color representations in Llama-3.1-8B (layer 19, hex-code prompts) organize along a paraboloid with a circular hue dimension and a lightness dimension, rather than a flat cylinder [bhalla-etal-2026] Fitting PCA and steering the activation along interpolated points between hue-bin centroids produces smooth, predictable shifts in the predicted color-name tokens, so the geometry is causally load-bearing [bhalla-etal-2026] A cross-lingual companion in NLLB-200 recovers the same hue-circle-plus-lightness-axis shape, though as a flat orthogonal decomposition rather than a fused paraboloid and without a causal test [bhalla-etal-2026]

models: Llama-3.1-8B · method: PCA, Activation Steering (Addition)
Do Sparse Autoencoders Capture Concept Manifolds? (2026)measured

SAEs dilute continuous manifolds rather than compactly capturing them

Details

Bhalla et al. formalize manifold capture (a small fixed group of decoder atoms spanning the manifold, consistently reselected by the encoder) and identify three regimes: compact capture, tiling/shattering, and intermediate dilution [bhalla-etal-2026] Training five SAE architectures on Llama-3.1-8B layer-19 activations, variance explained by a restricted atom group plateaus well beyond each manifold's ambient dimension, so none achieve compact capture [bhalla-etal-2026] Features behave like overlapping population-code tuning curves that redundantly tile the manifold, placing all tested SAEs in the dilution regime [bhalla-etal-2026] Ising-coactivation analysis on SAE codes recovers known manifolds (temperature, colors, political bias) unsupervised and surfaces a new epistemic-uncertainty manifold [bhalla-etal-2026]

models: Llama-3.1-8B · method: Sparse Autoencoders (SAE), Manifold capture theory (subspace capture / tiling / dilution), Ising coactivation analysis, PCA