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Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance

T0 review · 3 major / 2 minor · reviewed 2026-06-28 · grok-4.3

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Pith's one-line read ELUDe disentangles polysemantic neurons into monosemantic features with no change to model predictions or accuracy.

desk verdict ELUDe claims exact output preservation while splitting polysemantic neurons via layer re-routing, but the identification and verification steps need close checking. read the letter →

arxiv 2605.31304 v1 pith:E7SKZ3SJ submitted 2026-05-29 cs.LG cs.CV

Doğukan Bağc{i} , Bernt Schiele , Simone Schaub-Meyer , Jonas Fischer , Robin Hesse This is my paper · ORCID
classification cs.LGcs.CV
keywords interpretabilitydisentanglementpolysemanticitymonosemanticfeaturesneuralnetworksvisionmodelslosslessmethodsrepresentationsteering
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Deep neural networks often have individual neurons encoding multiple unrelated concepts, which obscures how decisions are made. ELUDe addresses this by reorganizing information flow between layers to isolate those concepts into separate, inspectable units. The method guarantees the network's outputs remain exactly the same as the original, with no retraining or labels required. This matters because earlier disentanglement techniques typically traded off some predictive performance for better clarity. A sympathetic reader would view it as a way to gain actionable insights into existing models at zero functional cost.

What carries the argument

ELUDe, which separates polysemantic signals by explicit re-routing of concept-specific contributions between layers while preserving exact functional equivalence.

What would settle it

Any observable difference in the model's output predictions or accuracy on a held-out test set after applying ELUDe would falsify the claim of lossless disentanglement.

Watch

Extended reading notes

Core claim

ELUDe is an explicit, lossless, unsupervised method that breaks latent representations in deep neural networks into clear sub-units behaving like interpretable features. It achieves this by re-routing concept-specific contributions between layers while preserving the original computation exactly by construction. The approach applies to pretrained models such as DINOv2 and ViT-B/16, improves interpretability, keeps downstream accuracy unchanged, and supports uses like steering representations.

Load-bearing premise

That concept-specific contributions can be identified and rerouted between layers to fully separate polysemantic signals into monosemantic features without altering the overall network computation.

Editorial extensions

If this is right

  • Interpretability improves on vision models including DINOv2 and supervised ViT-B/16 with no accuracy change.
  • The method applies directly to any pretrained model without labels or retraining.
  • Representation steering becomes possible as a practical downstream use.
  • Functional equivalence holds by design, so performance metrics remain identical.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The re-routing principle might generalize to language models if concept contributions can be similarly isolated.
  • Pairing ELUDe with other post-hoc methods could produce even finer-grained control over features.
  • The lossless property would enable direct comparisons of interpretability before and after under identical conditions.
  • Efficiency on very large models would hinge on scalable ways to detect the concept contributions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper introduces ELUDe, an unsupervised post-hoc method that reorganizes information flow between layers of pretrained DNNs (e.g., DINOv2, supervised ViT-B/16) by re-routing concept-specific contributions. It claims to produce monosemantic sub-units while guaranteeing exact functional equivalence to the original model (outputs unchanged by construction), with no training, no labels, and no degradation in downstream accuracy. The approach is positioned as enabling interpretability improvements and practical interventions such as representation steering.

Significance. If the lossless equivalence and monosemanticity claims hold, the result would be significant for interpretability research: it would remove the usual accuracy-interpretability tradeoff, allow direct application to existing models, and support downstream uses like steering without retraining. The absence of free parameters or invented entities in the high-level description is a strength if the re-routing mechanism is shown to be parameter-free and the equivalence is proven by construction.

major comments (3)
  1. [Methods (re-routing procedure)] The central claim of exact functional equivalence 'by construction' via re-routing requires an explicit derivation or proof in the methods section showing that the reorganized computation is mathematically identical to the original forward pass; without this, the 'lossless' guarantee cannot be verified and remains an assumption.
  2. [Experiments] Empirical verification that the resulting sub-units are monosemantic (rather than merely reorganized polysemantic features) is load-bearing for the interpretability claim; the experiments section should include quantitative metrics (e.g., feature activation sparsity, concept purity scores) with controls and statistical tests, not only qualitative examples.
  3. [Results (accuracy tables)] The claim that downstream accuracy remains exactly unchanged must be supported by reporting both mean and variance across multiple runs or seeds, plus a direct comparison to the unmodified baseline on the same evaluation protocol; any numerical difference, even if small, would contradict the 'exactly the same' guarantee.
minor comments (2)
  1. [Methods] Notation for the re-routing operation should be defined with explicit equations early in the methods section to avoid ambiguity when describing how contributions are identified and moved between layers.
  2. [Introduction] The abstract and introduction would benefit from a short related-work paragraph contrasting ELUDe with sparse autoencoders on the specific dimension of functional equivalence.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their thoughtful and constructive comments on our manuscript. We address each major comment point by point below.

read point-by-point responses

  1. Referee: [Methods (re-routing procedure)] The central claim of exact functional equivalence 'by construction' via re-routing requires an explicit derivation or proof in the methods section showing that the reorganized computation is mathematically identical to the original forward pass; without this, the 'lossless' guarantee cannot be verified and remains an assumption.

    Authors: We agree that an explicit derivation strengthens the presentation. In the revised manuscript we will add a formal proof in the Methods section. The re-routing decomposes each pre-activation into additive concept-specific contributions and reassigns them to dedicated sub-units; because the subsequent linear transformation receives exactly the same total input, the forward pass is identical by construction. revision: yes

  2. Referee: [Experiments] Empirical verification that the resulting sub-units are monosemantic (rather than merely reorganized polysemantic features) is load-bearing for the interpretability claim; the experiments section should include quantitative metrics (e.g., feature activation sparsity, concept purity scores) with controls and statistical tests, not only qualitative examples.

    Authors: We acknowledge that quantitative metrics would provide stronger support. While the current experiments rely on qualitative visualizations and steering demonstrations, we will add quantitative evaluations (activation sparsity, concept purity scores, and statistical comparisons against baseline features) in the revised Experiments section. revision: yes

  3. Referee: [Results (accuracy tables)] The claim that downstream accuracy remains exactly unchanged must be supported by reporting both mean and variance across multiple runs or seeds, plus a direct comparison to the unmodified baseline on the same evaluation protocol; any numerical difference, even if small, would contradict the 'exactly the same' guarantee.

    Authors: ELUDe is fully deterministic and contains no trainable parameters or stochastic operations. Because functional equivalence holds by construction, every output is identical to the original model for any input; therefore downstream accuracy is exactly the same with zero variance. We will clarify this point explicitly in the revised Results section and omit multiple-run statistics, as they are inapplicable. revision: no

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected

full rationale

The paper presents ELUDe as a reorganization of information flow between layers that re-routes concept-specific contributions while preserving the original computation exactly by construction. No equations, fitted parameters, or derivation chain appear in the provided abstract or description that would reduce any claimed prediction or result to its inputs by definition. The functional equivalence is asserted as a built-in property of the re-routing procedure rather than a statistically forced outcome from data fitting. No self-citation load-bearing steps, uniqueness theorems imported from prior author work, or ansatz smuggling are identifiable from the text. The central claim remains independent of the inputs and does not reduce to renaming or self-definition.

Assumptions & free parameters 0 free parameters · 0 assumptions · 0 invented entities

Abstract-only input supplies no information on free parameters, axioms, or invented entities.

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Cite this review

Pith. "Pith review of Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance." pith.science (2026). https://pith.science/paper/E7SKZ3SJ

@misc{pith2026260531304,
  author       = {Pith},
  title        = {Pith review of: Interpretability Without Tradeoffs: Disentangling Polysemanticity At Equal Predictive Performance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/E7SKZ3SJ}},
  note         = {Machine review of arXiv:2605.31304}
}
read the original abstract

Deep neural networks (DNNs) are widely used, but interpreting what they actually learn remains difficult. A major obstacle is that individual neurons often encode multiple unrelated concepts, obscuring the decision process of the network. While prior work, such as sparse autoencoders, can separate these mixed signals into more meaningful, "monosemantic" features, this typically requires altering the model in ways that can degrade downstream performance. To overcome this, we introduce ELUDe (explicit, lossless, unsupervised disentanglement), a method for improving the interpretability of DNNs while preserving their functional equivalence. ELUDe breaks latent representations into clear, inspectable sub-units that behave like interpretable features, while guaranteeing that the model's outputs remain exactly the same. It requires no explicit training, no labels, and can be applied to pretrained models. ELUDe works by reorganizing how information flows between layers, re-routing concept-specific contributions while preserving the original computation by construction. Across several vision models, including DINOv2 and supervised ViT-B/16, ELUDe improves interpretability, keeps downstream accuracy unchanged, runs efficiently, and supports practical uses such as steering model representations. In short, ELUDe offers interpretability (almost) without a tradeoff: clearer, scalable, and actionable model insights with no loss in performance.

Figures

Figures reproduced from arXiv: 2605.31304 by the authors.

Figure 1
Figure 1. ELUDe for interpretable disentanglement. ELUDe decomposes a polysemantic unit at layer L into more monosemantic sub-units by restructuring incoming weights so that each sub-unit captures only a specific semantic concept. The sum of all sub-units exactly recovers the original unit activation, ensuring perfect faithfulness. On the right, we provide a concrete example of this process by showing highly activating images… view at source ↗
Figure 2
Figure 2. Interpretability-faithfulness tradeoff. We compare faithfulness and interpretability across disentanglement methods; marker size indicates the ex￾pansion factor. Existing approaches ex￾hibit a clear Pareto front, where higher faithfulness typically comes at the cost of lower interpretability. In contrast, ELUDe improves interpretability with￾out sacrificing faithfulness, substantially advancing the Pareto front. See… view at source ↗
Figure 3
Figure 3. Qualitative examples. Top-activating images from the ImageNet-1k validation split for a polysemantic parent unit from the last layer of DINOv2-B/14 (yellow frame, left; top-16) and the 8 ELUDe subunits it decomposes into (pink frames, right; top-4 each). Labels are annotated by hand. Please refer to Section D for additional qualitative examples. Our method decomposes a potentially polysemantic unit into a set of mor… view at source ↗
Figures from the paper (14 more)
Figure 4
Figure 4. Figure 4: Overview of ELUDe. (1) Embed every image x (i) in the dataset D through layer L and consider the resulting activation instances ℓ (e.g., spatial locations or tokens); select the top-k instances that activate unit u most strongly. (2) For each selected activation instan…
Figure 5
Figure 5. Figure 5: Steering an ELUDe-disentangled LLaVA-OneVision. Captions generated under two interventions on layer 26 of the disentangled vision encoder, all using the same COCO image [37], prompt, and greedy decoder. “−” and “+” denote removing and adding the concept, respectively. …
Figure 6
Figure 6. Figure 6: Example pairwise grid shown to a VLM judge. Each panel is a 3×3 collage of the top-9 most-activating images for one disentangled unit; two such panels from competing methods (left: ELUDe, right: Archetypal SAE) are placed side by side and the three judges select the mo…
Figure 7
Figure 7. Figure 7: Steering example 2: independent foreground and background edits on the train scene. Settings (α, γ, κ, ν) per row: train suppression (5, −50, 50, allpatchtokens); train→bus (50, −2, 100, 50); graffiti→clean (15, −15, 200, 50). The subject and the background style are m…
Figure 8
Figure 8. Figure 8: Steering example 3: surface, subject, and suppression edits on the giant-teddy scene. Settings (α, γ, κ, ν) per row: chair→table (5, −5, 50, 25); teddy→dog (5, −50, 100, 50); teddy suppression (5, −25, 200, 100). The seat-surface concept is separable from the toy, and …
Figure 9
Figure 9. Figure 9: Steering example 4: object swap, object removal, and background transplant on the ac￾tion shot. Settings (α, γ, κ, ν) per row: frisbee→ball (15, −0.5, 200, allpatchtokens); frisbee/ball suppression with jumping insertion (25, −10, 50, 250); indoor→beach/park (25, −25, …
Figure 10
Figure 10. Figure 10: Steering example 5: subject substitution and a disentanglement boundary on the cats-on-blanket scene. Settings (α, γ, κ, ν) per row: cat→baby (5, −10, 200, allpatchtokens); cat→bear (15, −10, 100, allpatchtokens); cat and blanket suppression (5, −15, 50, allpatchtoken…
Figure 11
Figure 11. Figure 11: K = 1: an already-monosemantic unit. HDBSCAN finds only a single cluster in the unit’s contribution-vector set, so ELUDe leaves the unit unchanged. Original Neuron 28 Reptiles & arthropods Large dogs [PITH_FULL_IMAGE:figures/full_fig_p023_11.png]
Figure 12
Figure 12. Figure 12: 2-way ELUDe decomposition. A polysemantic DINOv2-B/14 unit (yellow frame, left; top-16) decomposed by ELUDe into 2 subunits (pink frames, right; top-4 each). features and z˜sk their pre-activations; maximizing this term encourages dormant features to activate) in all …
Figure 13
Figure 13. Figure 13: 3-way ELUDe decomposition. A polysemantic DINOv2-B/14 unit (yellow frame, left; top-16) decomposed by ELUDe into 3 subunits (pink frames, right; top-4 each). Original Neuron 286 Long-haired dogs Primates Drum kits Insects [PITH_FULL_IMAGE:figures/full_fig_p024_13.png]
Figure 14
Figure 14. Figure 14: 4-way ELUDe decomposition. A polysemantic DINOv2-B/14 unit (yellow frame, left; top-16) decomposed by ELUDe into 4 subunits (pink frames, right; top-4 each). reflects a conservative, unoptimized implementation: neurons are processed sequentially on a single GPU, top-k…
Figure 15
Figure 15. Figure 15: 5-way ELUDe decomposition. A polysemantic DINOv2-B/14 unit (yellow frame, left; top-16) decomposed by ELUDe into 5 subunits (pink frames, right; top-4 each). Original Neuron 45 Shoes Vending machines Clownfish Beds Office printers Lizards [PITH_FULL_IMAGE:figures/ful…
Figure 16
Figure 16. Figure 16: 6-way ELUDe decomposition. A polysemantic DINOv2-B/14 unit (yellow frame, left; top-16) decomposed by ELUDe into 6 subunits (pink frames, right; top-4 each). E.5 Backbones [PITH_FULL_IMAGE:figures/full_fig_p025_16.png]
Figure 17
Figure 17. Figure 17: 7-way ELUDe decomposition. A polysemantic DINOv2-B/14 unit (yellow frame, left; top-16) decomposed by ELUDe into 7 subunits (pink frames, right; top-4 each). sionality reduction. The ablations in Section C.5 sweep each of these one at a time around this baseline. E.8 …

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Pith tools

Reviewed June 28, 2026 · model on record in the stance chip above.