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arxiv: 2601.05679 · v7 · pith:VEGH6MNYnew · submitted 2026-01-09 · 💻 cs.LG

Do Sparse Autoencoders Identify Reasoning Features in Language Models?

George Ma , Zhongyuan Liang , Irene Y. Chen , Somayeh Sojoudi This is my paper

Pith reviewed 2026-05-21 15:48 UTC · model grok-4.3

classification 💻 cs.LG
keywords sparse autoencodersreasoning featureslanguage modelsinterpretabilityfalsificationfeature attributiontoken interventions
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The pith

Sparse autoencoders often retain low-dimensional token patterns rather than genuine reasoning processes in language models.

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

The paper investigates how reliably sparse autoencoders can be used to claim the existence of internal features that drive reasoning behavior in large language models. A stylized analysis shows that sparsity regularization tends to keep stable, low-dimensional correlates while discarding higher-dimensional variation inside the same behavior. The authors introduce a falsification framework that injects specific tokens into non-reasoning contexts and uses language-model-generated counterexamples and paraphrases to test whether selected features truly encode reasoning. In experiments across multiple models, layers, and datasets, most candidate features activate after only a few token injections, and the rest fail when presented with non-reasoning inputs or paraphrased reasoning traces. Steering experiments produce only small benchmark changes, supporting the view that sparse decompositions favor co-occurring surface cues over the full reasoning computation.

Core claim

In the settings studied, sparse decompositions preferentially retain low-dimensional correlates that co-occur with reasoning traces; this is shown by 45-90 percent of contrastively selected features activating after injection of a few associated tokens into non-reasoning text, by the remaining candidates activating on LLM-constructed non-reasoning counterexamples or deactivating on meaning-preserving paraphrases, and by minimal effects when those features are used for steering.

What carries the argument

A falsification-based evaluation framework that combines causal token injection with LLM-guided construction of counterexamples and paraphrases to test whether an SAE feature encodes reasoning or surface cues.

If this is right

  • Many contrastively selected reasoning features activate after injection of only a few associated tokens into non-reasoning text.
  • Context-dependent candidates still activate on targeted non-reasoning inputs produced by the LLM and deactivate on paraphrases of reasoning traces.
  • Steering with the identified features yields only minimal performance changes on the evaluated reasoning benchmarks.
  • Attributing high-level behaviors such as reasoning to individual SAE features requires explicit falsification against simpler co-occurring explanations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The results suggest that similar low-dimensional bias may affect SAE interpretations of other high-level behaviors such as planning or arithmetic.
  • Improved methods would need to isolate higher-dimensional variation inside a behavior rather than relying on contrastive selection alone.
  • This pattern raises a general question about how to certify that any discovered feature participates in the full computation rather than a correlated shortcut.

Load-bearing premise

Token-level causal interventions together with LLM-generated counterexamples and paraphrases constitute valid tests that can distinguish reasoning encoding from surface cues.

What would settle it

Finding that the selected features remain inactive when their associated tokens are injected into non-reasoning text, or that they stay active after meaning-preserving paraphrases of top-activating reasoning traces, would falsify the claim that these features primarily track low-dimensional correlates.

Figures

Figures reproduced from arXiv: 2601.05679 by George Ma, Irene Y. Chen, Somayeh Sojoudi, Zhongyuan Liang.

Figure 1
Figure 1. Figure 1: Overview of our falsification-based evaluation framework for reasoning features. We begin with SAE features identified by contrastive methods. We then test whether lexical cues are sufficient to induce activation via causal token injection. Remaining features are subjected to LLM-guided adversarial counterexample generation, which decouples reasoning behavior from feature activation. Across all configurati… view at source ↗
Figure 2
Figure 2. Figure 2: Token concentration ratio and normalized activation entropy for SAE features across all layers of Gemma-3-4B-Instruct on the s1K dataset. Middle layers exhibit lower concentration and higher entropy, indicating reduced reliance on specific tokens. dominated by lexical processing, while late layers are in￾creasingly specialized toward output token prediction (Ma et al., 2025). To further support this choice… view at source ↗
Figure 4
Figure 4. Figure 4: visualizes these results across all configurations. The overall pattern is consistent across models and lay￾ers. Gemma-3-12B-Instruct evaluated on the s1K dataset exhibits the largest fraction of context-dependent features at 55%, while several Gemma configurations on the same dataset exhibit fewer than 12%. G3-12b L17 s1K G3-12b L17 GenInq G3-12b L22 s1K G3-12b L22 GenInq G3-12b L27 s1K G3-12b L27 GenInq … view at source ↗
Figure 3
Figure 3. Figure 3: Distribution of Cohen’s d values across SAE features for Gemma-3-12B-Instruct at layer 22. 5.3. Token Injection Results We apply the token injection procedure described in Sec￾tion 4.3 to the top 100 candidate reasoning features per configuration. For each feature, we test whether inserting its most activating tokens into non-reasoning text is suffi￾cient to elicit strong activation, and assign the feature… view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of cue-driven separability without semantic reasoning representation. Top: Reasoning traces often contain stable cue tokens such as wait, while non-reasoning text does not. A contrastively selected SAE feature that activates on the cue can separate reasoning from non-reasoning despite encoding only a low-dimensional lexical pattern. Bottom: Steering such a feature can increase the probability … view at source ↗
Figure 6
Figure 6. Figure 6: Jaccard similarity between top-100 feature sets selected by different ranking metrics on Gemma-3-4B-Instruct at layer 22 using the s1K dataset. Taken together, these results demonstrate that our main conclusions do not depend on the specific choice of ranking metric. Whether features are selected by Cohen’s d, ROC-AUC, or frequency ratio, the vast majority are explained by token-level or short-range contex… view at source ↗
Figure 7
Figure 7. Figure 7: Token concentration distributions across layers for Gemma-3-12B-Instruct and Gemma-3-4B-Instruct on the s1K dataset. Prepend Intersperse Bigram Trigram Bigram+Ctx Trigram+Ctx Injection Strategy 0 1 2 3 4 5 6 Cohen’s d (Injection Effect) Large effect Medium effect Small effect [PITH_FULL_IMAGE:figures/full_fig_p026_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Cohen’s d distributions across token injection strategies for Gemma-3-12B-Instruct at layer 22 on s1K. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of iterations required for LLM-guided falsification to converge. E.6. Feature overlap across reasoning datasets Finally, we examine whether the same features are selected when ranking on different reasoning datasets. For each model and layer, we compute the Jaccard similarity between the top 100 features identified using s1K and General Inquiry CoT [PITH_FULL_IMAGE:figures/full_fig_p028_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Feature overlap between s1K and General Inquiry CoT across models and layers. 29 [PITH_FULL_IMAGE:figures/full_fig_p029_10.png] view at source ↗
read the original abstract

We study how reliably sparse autoencoders (SAEs) support claims about reasoning-related internal features in large language models. We first give a stylized analysis showing that sparsity-regularized decoding can preferentially retain stable low-dimensional correlates while suppressing high-dimensional within-behavior variation, motivating the possibility that contrastively selected "reasoning" features may concentrate on cue-like structure when such cues are coupled with reasoning traces. Building on this perspective, we propose a falsification-based evaluation framework that combines causal token injection with LLM-guided counterexample construction. Across 22 configurations spanning multiple model families, layers, and reasoning datasets, we find that many contrastively selected candidates are highly sensitive to token-level interventions, with 45%-90% activating after injecting only a few associated tokens into non-reasoning text. For the remaining context-dependent candidates, LLM-guided falsification produces targeted non-reasoning inputs that trigger activation and meaning-preserving paraphrases of top-activating reasoning traces that suppress it. A small steering study yields minimal changes on the evaluated benchmarks. Overall, our results suggest that, in the settings we study, sparse decompositions can favor low-dimensional correlates that co-occur with reasoning, underscoring the need for falsification when attributing high-level behaviors to individual SAE features. Code is available at https://github.com/GeorgeMLP/reasoning-probing.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper examines whether sparse autoencoders (SAEs) reliably recover reasoning-related features in LLMs. It presents a stylized analysis arguing that sparsity can favor stable low-dimensional correlates over high-dimensional reasoning variation, then introduces a falsification framework using token injection and LLM-generated counterexamples/paraphrases. Across 22 configurations (multiple models, layers, reasoning datasets), contrastively selected features show 45-90% activation after injecting a few associated tokens into non-reasoning text; remaining candidates activate on targeted non-reasoning counterexamples but suppress on meaning-preserving paraphrases. A small steering experiment produces minimal benchmark changes. The authors conclude that SAEs often capture co-occurring surface cues rather than reasoning per se and stress the need for falsification when attributing behaviors to individual features. Code is released.

Significance. If the central empirical pattern holds, the work supplies a useful cautionary result and concrete falsification protocol for the growing literature on SAE-based mechanistic interpretability of high-level capabilities. The multi-configuration scope and public code are strengths. The stylized analysis usefully motivates why contrastive selection may latch onto correlates. However, the absence of baseline activation rates, statistical controls, and explicit comparison to known abstract-reasoning features limits how strongly the results can be read as evidence against reasoning encoding rather than against the particular intervention suite.

major comments (3)
  1. [Abstract / Evaluation Framework] Abstract and evaluation framework: the reported 45%-90% activation rates after token injection are presented without baseline activation statistics on random or unrelated tokens, nor any mention of how many candidate features were screened before the contrastive selection step. This leaves open the possibility that the sensitivity figures are inflated by post-hoc choice of the most cue-like features.
  2. [Evaluation Framework] LLM-guided counterexample construction: the description does not specify quantitative constraints ensuring that generated counterexamples differ from reasoning traces only in the reasoning component while preserving all other semantics and surface statistics. Without such controls or inter-annotator agreement metrics, it is unclear whether activation on counterexamples falsifies reasoning encoding or merely detects residual lexical cues.
  3. [Steering Study] Steering study: the claim of 'minimal changes on the evaluated benchmarks' is given without reporting effect sizes, variance across runs, or comparison to random steering vectors of matched sparsity. This weakens the inference that the features do not participate in reasoning computations.
minor comments (2)
  1. [Stylized Analysis] The stylized analysis would benefit from a short formal statement (e.g., a proposition or inequality) showing the precise condition under which sparsity prefers low-dimensional correlates.
  2. [Experimental Setup] Table or figure summarizing the 22 configurations (model, layer, dataset, number of features tested) is missing; adding one would improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive comments, which identify opportunities to strengthen the transparency of our evaluation framework and steering results. We respond point-by-point below and indicate where revisions will be made.

read point-by-point responses

  1. Referee: [Abstract / Evaluation Framework] Abstract and evaluation framework: the reported 45%-90% activation rates after token injection are presented without baseline activation statistics on random or unrelated tokens, nor any mention of how many candidate features were screened before the contrastive selection step. This leaves open the possibility that the sensitivity figures are inflated by post-hoc choice of the most cue-like features.

    Authors: We agree that baseline activation rates and details on the screening process would improve interpretability. In the revised manuscript we will report activation rates on random and unrelated tokens as explicit baselines. We will also state the total number of SAE features per configuration and describe the systematic contrastive selection procedure applied to all features, clarifying that selection was not post-hoc. revision: yes

  2. Referee: [Evaluation Framework] LLM-guided counterexample construction: the description does not specify quantitative constraints ensuring that generated counterexamples differ from reasoning traces only in the reasoning component while preserving all other semantics and surface statistics. Without such controls or inter-annotator agreement metrics, it is unclear whether activation on counterexamples falsifies reasoning encoding or merely detects residual lexical cues.

    Authors: We will expand the methods to include the exact prompts used for counterexample and paraphrase generation, along with quantitative checks (e.g., token overlap and embedding similarity) that were applied to preserve surface statistics. Because the generation is fully automated, inter-annotator agreement metrics were not collected; we will note this limitation explicitly and argue that the consistent suppression on meaning-preserving paraphrases still provides evidence against purely lexical explanations. revision: partial

  3. Referee: [Steering Study] Steering study: the claim of 'minimal changes on the evaluated benchmarks' is given without reporting effect sizes, variance across runs, or comparison to random steering vectors of matched sparsity. This weakens the inference that the features do not participate in reasoning computations.

    Authors: We accept that additional statistical reporting is warranted. The revised version will include effect sizes, standard deviations across repeated runs, and direct comparisons against random steering vectors of matched sparsity. These additions will provide a clearer quantitative basis for the observed minimal benchmark changes. revision: yes

Circularity Check

0 steps flagged

No load-bearing circularity; evaluations rely on independent interventions

full rationale

The paper motivates its perspective with a stylized analysis of sparsity-regularized decoding and then evaluates via external causal token injections and LLM-generated counterexamples/paraphrases. These tests measure activation rates on newly constructed inputs rather than fitting parameters to the same data used for feature selection or claiming predictions that reduce tautologically to inputs. No equations, self-citations, or ansatzes are shown to force the central falsification results by construction. This is a standard empirical setup with independent evidence.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the validity of the token-injection and counterexample tests as proxies for distinguishing cue-like from reasoning structure; no new free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Sparsity-regularized decoding preferentially retains stable low-dimensional correlates while suppressing high-dimensional within-behavior variation.
    Stylized analysis presented in the abstract to motivate the possibility that contrastive features concentrate on cue-like structure.

pith-pipeline@v0.9.0 · 5775 in / 1344 out tokens · 38548 ms · 2026-05-21T15:48:53.677053+00:00 · methodology

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