Pith. sign in

REVIEW 3 major objections 2 minor 1 cited by

Do Sparse Autoencoders Identify Reasoning Features in Language Models?

T0 review · 3 major / 2 minor · reviewed 2026-05-21 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Sparse autoencoders often retain low-dimensional token patterns rather than genuine reasoning processes in language models.

desk verdict The paper finds that contrastively picked SAE features for reasoning often fire on minimal token injections and LLM counterexamples, so attribution needs stronger falsification steps. read the letter →

arxiv 2601.05679 v7 pith:VEGH6MNY submitted 2026-01-09 cs.LG

George Ma , Zhongyuan Liang , Irene Y. Chen , Somayeh Sojoudi This is my paper · ORCID
classification cs.LG
keywords sparseautoencodersreasoningfeatureslanguagemodelsinterpretabilityfalsificationfeatureattributiontokeninterventions
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

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.

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.

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.

Watch

Extended reading notes

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.

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.

Editorial extensions

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.

Reading between the lines

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.
Share X Bluesky LinkedIn Reddit HN

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 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 · score 2.0 of 10

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.

Assumptions & free parameters 0 free parameters · 1 assumptions · 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.

assumptions (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.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Do Sparse Autoencoders Identify Reasoning Features in Language Models?." pith.science (2026). https://pith.science/paper/VEGH6MNY

@misc{pith2026260105679,
  author       = {Pith},
  title        = {Pith review of: Do Sparse Autoencoders Identify Reasoning Features in Language Models?},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VEGH6MNY}},
  note         = {Machine review of arXiv:2601.05679}
}
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.

Figures

Figures reproduced from arXiv: 2601.05679 by the authors.

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 configurations, this … view at source ↗
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, we analy… view at source ↗
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 G3-4b L17 … view at source ↗
Figures from the paper (7 more)
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 …
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 reasoni…
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. W…
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…
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]
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 th…
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]

Discussion (0). Continue with ORCID to comment.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. OC-Distill: Ontology-aware Contrastive Learning with Cross-Modal Distillation for ICU Risk Prediction

    cs.LG 2026-04 unverdicted novelty 6.0 of 10

    Ontology-aware contrastive pretraining plus note-to-vitals distillation improves MIMIC ICU risk and length-of-stay prediction using only vital signs at inference.

Reference graph

Works this paper leans on

120 extracted references · 120 canonical work pages · cited by 1 Pith paper

  1. [1]

    Then, you will cross the river using the old bridge

    To reach the ancient temple ruins, tourists first need to traverse the dense jungle path. Then, you will cross the river using the old bridge. To navigate the terrain safely, you must wear appropriate

    work page

  2. [2]

    Then, you must traverse the glacier efficiently to avoid the risk of falling

    To cross the dangerous mountain pass safely, hikers first need to navigate the steep ridge clearly marked on the trail map. Then, you must traverse the glacier efficiently to avoid the risk of falling

    work page

  3. [3]

    The legal department should then review the consent forms thoroug

    To enforce the privacy policy efficiently, the corporation first must identify the data sources clearly defined in the user agreement. The legal department should then review the consent forms thoroug... False Negatives (Semantic paraphrases of high-activation samples that fail to activate):

    work page

  4. [4]

    Traditional systems compress images into fixed vectors, causin

    Addressing improvements in image captioning via attention mechanisms starts by grasping the limits of older models lacking such features. Traditional systems compress images into fixed vectors, causin

    work page

  5. [5]

    Humor relies on complex human traits missing in current software

    Initial thoughts focused on why AI finds humor difficult. Humor relies on complex human traits missing in current software. Thinking about key elements like surprise and context came before everything

    work page

  6. [6]

    separately

    Grasping the request for practical Natural Language Processing examples is the starting point. Effective handling involves looking at NLP in business and daily tech. Analysis of application areas like... Feature 13118 High-Activation Examples: Example 1: effectively, I'll first identify the core challenges. These generally revolve around latency, throughp...

    work page

  7. [7]

    First, I’ll cushion the cups individually with paper

    To pack the fragile china securely, I need to wrap the plates separately. First, I’ll cushion the cups individually with paper. The box itself is reinforced. The move entails a loading phase. Then, I’

    work page

  8. [8]

    First, I’ll spray the strands individually for hold

    To style the hair perfectly, I need to curl the sections separately. First, I’ll spray the strands individually for hold. The heat itself is moderate. The look entails a setting phase. Then, I’ll brus

    work page

Show all 120 references
  1. [9]

    First, I’ll spray the cuffs individually with starch

    To iron the dress shirts correctly, I need to press the collars separately. First, I’ll spray the cuffs individually with starch. The fabric itself is cotton. I must use a high heat setting for the be... False Negatives (Semantic paraphrases of high-activation samples that fai...

  2. [10]

    Spotting these challenges helps us find targeted solutions for keeping syste

    Real-time query systems face hurdles like latency or throughput so we should look at those core issues plus data freshness. Spotting these challenges helps us find targeted solutions for keeping syste

  3. [11]

    This strategy gives us the total duration by combining the two parts

    Calculating the time from ten-thirty to noon means counting the minutes up to eleven plus the full hour that follows. This strategy gives us the total duration by combining the two parts

  4. [12]

    effectively,

    Large number multiplication gets simpler with patterns like powers of ten. We can also use shortcuts for numbers like eleven or five to speed things up. These tricks make the calculation much faster. Feature 1123 High-Activation Examples: Example 1: First, I should consider th...

  5. [13]

    Next, I thoroughly inspected the storage tanks

    To serve the colony effectively, I first comprehensively filtered the water supply. Next, I thoroughly inspected the storage tanks. I accurately measured the levels today. Therefore, I approach the ma

  6. [14]

    Next, I thoroughly labeled the cables

    To assist the developers effectively, I first comprehensively organized the server racks. Next, I thoroughly labeled the cables. I accurately connected the power supply. My approach indicates that the

  7. [15]

    Next, I thoroughly organized the tangled power cables beneath the shared workstations

    To support the developers effectively, I first comprehensively stocked the break room with fresh coffee beans. Next, I thoroughly organized the tangled power cables beneath the shared workstations. I ... 33 Falsifying Sparse Autoencoder Reasoning Features in Language Models Fa...

  8. [16]

    Jane starts with 7 and gives away 4

    Solving the sticker problem requires basic arithmetic. Jane starts with 7 and gives away 4. Subtracting the given amount from the total yields the final count of stickers remaining

  9. [17]

    Recent developments have seen models producing high-quality images and text, marking a significant shift in the capab

    Current trends in technology highlight Generative AI as a rapidly advancing field. Recent developments have seen models producing high-quality images and text, marking a significant shift in the capab

  10. [18]

    I”, “My”) with sequencing markers (“First

    Healthcare benefits significantly from machine learning integration. Key applications include improving diagnostics, tailoring medicine to individuals, and accelerating drug discovery. These technolog... Feature 282 High-Activation Examples: Example 1: I need to break down wha...

  11. [19]

    First, I need to define the user inputs

    To describe this software feature, I need to clarify the structure. First, I need to define the user inputs. Then, I will provide an explanation of the background calculation. Next, I need to list the

  12. [20]

    First, I need to identify the author’s objectives

    My approach to reviewing this novel involves a literary explanation. First, I need to identify the author’s objectives. Then, I must consider the narrative inputs and themes. Next, I will analyze the

  13. [21]

    First, I need to consider the narrative explanation

    To critique this novel effectively, I need to formulate a structured approach. First, I need to consider the narrative explanation. Then, I must identify the thematic inputs provided by the author. Ne... False Negatives (Semantic paraphrases of high-activation samples that fai...

  14. [22]

    It essentially measures how well a model performs tasks like classification

    Defining ‘accuracy’ in the context of AI serves as the starting point. It essentially measures how well a model performs tasks like classification. Following the definition, an analysis of performance

  15. [23]

    To provide a comprehensive answer, one must examine the various phases of the writing process

    An analysis of the inquiry highlights the role of AI in creative writing. To provide a comprehensive answer, one must examine the various phases of the writing process. Identifying stages such as idea

  16. [24]

    Indexing is a critical tool for faster data retrieval

    Answering the question comprehensively requires outlining key strategies for database optimization. Indexing is a critical tool for faster data retrieval. Describing the purpose of indexes and showing... Feature 14872 High-Activation Examples: Example 1: First, I should consid...

  17. [25]

    I should look for the pre-drilled holes near the edge

    To assemble this bookshelf, I first need to determine which board is the bottom piece. I should look for the pre-drilled holes near the edge. I will align the side panel with the base. I need to figur

  18. [26]

    I should look for the recommended pressure on the door label

    First, I need to determine if the car tires are properly inflated. I should look for the recommended pressure on the door label. I need to figure out which tire looks low. I will simply attach the air

  19. [27]

    I should check the time on my phone

    First, I need to determine the correct time to set this alarm clock. I should check the time on my phone. I need to figure out which button changes the hour digit. I will simply hold down the ’set’ bu... False Negatives (Semantic paraphrases of high-activation samples that fai...

  20. [28]

    The situation calls for integer division

    Analyzing the task involves splitting 17 candies between 2 kids without breaking any. The situation calls for integer division. 17 over 2 makes 8, leaving 1 behind. This calculation shows that 8 is th

  21. [29]

    Mike holds 4 erasers total

    Review the provided data points. Mike holds 4 erasers total. Usage is 1 per day. Determining the duration involves dividing the total quantity by the daily consumption rate. The math is clear: 4 divid

  22. [30]

    With 4 bananas and 2 buddies, division is the necessary operation

    The problem asks for an equal share of bananas. With 4 bananas and 2 buddies, division is the necessary operation. Splits happen evenly here. Dividing 4 by 2 results in 2. Each friend receives exactly... Feature 491 High-Activation Examples: Example 1: the ethical concerns of ...

  23. [31]

    First, I need to recall the secret message

    To address the topic of invisible ink effectively, I need to consider the transparency of the liquid. First, I need to recall the secret message. My approach begins with analyzing the blank paper comp

  24. [32]

    First, I should outline a color-coding system

    To address the topic of arranging the bookshelf effectively, I need to consider the visual appeal of the covers. First, I should outline a color-coding system. My approach begins with sorting the book... 35 Falsifying Sparse Autoencoder Reasoning Features in Language Models

  25. [33]

    First, I need to identify the type of tea leaves

    To answer the question of how to brew tea comprehensively, I need to consider the water temperature. First, I need to identify the type of tea leaves. My approach begins with boiling the water to the ... False Negatives (Semantic paraphrases of high-activation samples that fai...

  26. [34]

    The code itself must change to accommodate the structural differences between language

    Mere translation fails to achieve true multilingual support; the software’s core architecture needs modification. The code itself must change to accommodate the structural differences between language

  27. [35]

    Ethical risks in AI development are most prominent in the data collection phase, where biases in training sets can be learned and amplified by the system if not carefully managed

  28. [36]

    limited resources and time

    Surrounding information allows AI systems to interpret user intent correctly by providing the necessary background for ambiguous queries. Feature 4510 High-Activation Examples: Example 1: for implicit constraints and requirements. The mention of “limited resources and time” su...

  29. [37]

    First, I need to sift the dry ingredients into a large bowl

    To bake the perfect chocolate cake, I need to preheat the oven to 350 degrees. First, I need to sift the dry ingredients into a large bowl. To ensure the batter is smooth, I need to beat the eggs one

  30. [38]

    To stay alive, I need to reach the ventilation shaft in the ceiling

    First, I need to hide before the guards come back. To stay alive, I need to reach the ventilation shaft in the ceiling. I need to move quietly. To open the grate, I need to use the small knife in my p

  31. [39]

    First, I need to lay out all the wooden panels on the floor

    To assemble this bookshelf, I need to identify the screws listed in the manual. First, I need to lay out all the wooden panels on the floor. To connect the sides, I need to use the allen wrench provid... False Negatives (Semantic paraphrases of high-activation samples that fai...

  32. [40]

    Prioritizing strategies that maximize impact while minimizing effort is key

    Understanding the central question regarding knowledge base expansion under tight resources is vital. Prioritizing strategies that maximize impact while minimizing effort is key. Initially, examining

  33. [41]

    Breaking figures into hundreds, tens, and ones seems optimal, facilitating tracking and mirroring standard learning

    Answering effectively requires considering various approaches for adding large sums. Breaking figures into hundreds, tens, and ones seems optimal, facilitating tracking and mirroring standard learning

  34. [42]

    What are the different types of AI?

    Calculating a rectangle’s perimeter starts by recalling the definition: total distance around a shape. Adding lengths of all four sides achieves this. Logic dictates summing two lengths and two widths... Feature 3810 High-Activation Examples: 36 Falsifying Sparse Autoencoder R...

  35. [43]

    First, I should define the nature of the fog surrounding me

    To tackle this dream effectively, I need to identify the shifting shadows. First, I should define the nature of the fog surrounding me. I need to outline the path through the void and establish a conn

  36. [44]

    First, I should define the mood of the room to avoid tension

    To tackle this awkward dinner effectively, I need to identify a safe topic of conversation. First, I should define the mood of the room to avoid tension. I need to outline my anecdotes and establish a

  37. [45]

    First, I should define the posture of the central figure

    To tackle this sculpture effectively, I need to identify the natural grain of the wood. First, I should define the posture of the central figure. I need to outline the rough cuts and establish the cor... False Negatives (Semantic paraphrases of high-activation samples that fai...

  38. [46]

    Step one is grasping the essence of subtraction and how it ties to addition

    The assignment is to demonstrate how to subtract negative integers. Step one is grasping the essence of subtraction and how it ties to addition. Subtraction acts like adding the inverse, so removing a

  39. [47]

    A solid response involves exploring NLP’s role in daily tech and business

    The query seeks real-world samples of Natural Language Processing. A solid response involves exploring NLP’s role in daily tech and business. I will start by breaking down the specific areas using thi

  40. [48]

    break down,

    First, we have to retrieve the main formula for a rectangle’s area. The total space is the product of length and width. So, the rule is ‘area = length * width’. To add depth, I should also talk about ... Feature 1148 High-Activation Examples: Example 1: an AI model better, I n...

  41. [49]

    We have categorized the settings menu to break down complex options into distinct panels

    Version 2.0 has been implemented to thoroughly address user feedback. We have categorized the settings menu to break down complex options into distinct panels. This update introduces a structured work

  42. [50]

    It categorizes the potential risks to break down the emergency protocols into distinct action plans, focusing on empl

    The corporate safety manual is structured to thoroughly address workplace hazards. It categorizes the potential risks to break down the emergency protocols into distinct action plans, focusing on empl

  43. [51]

    It carefully categorizes the transaction logs to break down the complex revenue s

    The corporate audit framework is employed to thoroughly address the question of financial transparency and compliance. It carefully categorizes the transaction logs to break down the complex revenue s... False Negatives (Semantic paraphrases of high-activation samples that fai...

  44. [52]

    It starts with realizing that this involves more than just translating words

    Regarding the question on supporting software in multiple languages, I will split the big task into smaller bits. It starts with realizing that this involves more than just translating words. It requi

  45. [53]

    It starts with realizing that this is more than just translating text

    Regarding the question on supporting software in multiple languages, I will split the big task into smaller bits. It starts with realizing that this is more than just translating text. It involves ful

  46. [54]

    I” when used in the context of meta-cognitive planning, self-narration, or outlining a response strategy (e.g., “I need to

    The user asks how to apply AI to personal tasks with few resources. Because that subject is huge, I will select particular spots where it works. I’ll start with writing and art tools. Feature 6048 High-Activation Examples: Example 1: question comprehensively, I need to outline...

  47. [55]

    First, I should consider the introductory module

    To answer this question about the course structure, I need to outline the core topics effectively. First, I should consider the introductory module. I need to identify the required textbooks and expla

  48. [56]

    First, I should consider the activity of the wild yeast

    To answer this question about the perfect sourdough, I need to identify the core biological processes of fermentation. First, I should consider the activity of the wild yeast. I need to explain how te

  49. [57]

    First, I should consider the layout and flow of the room

    To answer this question effectively, I need to outline the core principles of interior design for small spaces. First, I should consider the layout and flow of the room. I need to identify how lightin... False Negatives (Semantic paraphrases of high-activation samples that fai...

  50. [58]

    Indexing functions as a primary consideration

    To provide a comprehensive answer regarding database query performance, an outline of key strategies is essential. Indexing functions as a primary consideration. Because indexes speed up data retrieva

  51. [59]

    The definition should characterize estimation as a method

    Tackling this inquiry effectively involves defining estimation within the context of addition, followed by a description of common strategies. The definition should characterize estimation as a method

  52. [60]

    First” or “To answer

    Understanding subtraction through the lens of adding negative numbers requires a clear explanation. A crucial initial step involves defining the additive inverse. This foundation establishes that subt... 38 Falsifying Sparse Autoencoder Reasoning Features in Language Models Fe...

  53. [61]

    First, I need to identify where the carrot nose went, as this is the most pressing mystery of the

    To answer this question comprehensively, I need to consider the existential dread of a melted snowman. First, I need to identify where the carrot nose went, as this is the most pressing mystery of the

  54. [62]

    To compose this symphony, I need to consider the rhythm of the city streets and how the traffic sounds blend into a chaotic harmony

    First, I need to identify the melody within the noise. To compose this symphony, I need to consider the rhythm of the city streets and how the traffic sounds blend into a chaotic harmony

  55. [63]

    I should identify the color of my wings and determine if I can fly towards

    To answer this question comprehensively, I first need to consider the possibility that we are all just butterflies dreaming. I should identify the color of my wings and determine if I can fly towards ... False Negatives (Semantic paraphrases of high-activation samples that fai...

  56. [64]

    Usually this implies handling regulated or confidential data

    Determining the suitability of AI for sensitive tasks requires analyzing the specific aspects that define sensitivity. Usually this implies handling regulated or confidential data. Consequently the re

  57. [65]

    Next comes accounting for the 2 additional marbles she discovered later

    The calculation starts with the 7 marbles Olivia owns originally. Next comes accounting for the 2 additional marbles she discovered later. Finding the total demands combining these two separate quanti

  58. [66]

    process,

    Recalling the standard formula for a rectangle’s area is the prerequisite step. Multiplying length by width yields the area. The core concept is ‘area = length * width’. Making the answer complete als... Feature 444 High-Activation Examples: Example 1: to break down the proces...

  59. [67]

    She noted that the **structure** was awkward and the **topic** was unclear

    The editor circled the **sentence** in red. She noted that the **structure** was awkward and the **topic** was unclear. The entire **paragraph** lacked coherence. She recommended rewriting the **text*

  60. [68]

    It describes the **functionality** of each **element**

    The manual outlines the installation **process**. It describes the **functionality** of each **element**. The **system** handles the data **retrieval** automatically. Users can configure the **setting

  61. [69]

    The **topic** of discussion was renewable energy

    The moderator posed the **question** to the panel. The **topic** of discussion was renewable energy. The expert provided a detailed **answer**. She analyzed the **issue** from a scientific **perspecti... False Negatives (Semantic paraphrases of high-activation samples that fai...

  62. [70]

    I’ll look at clarity and engagement

    I need to determine what makes a bot chat effective. I’ll look at clarity and engagement. I’ll discuss the requirements, starting with creating a smooth exchange for the user

  63. [71]

    Because of the zeros, I need to borrow

    I have to subtract 27 from 100. Because of the zeros, I need to borrow. I will place 100 on top of 27. The ones place is zero, so I must borrow from the neighboring columns before subtracting

  64. [72]

    the”, “its

    I have to take 27 away from 100. Because of the zeros, I need to borrow. I will place 100 on top of 27. The ones place is zero, so I must borrow from the left. Feature 34 High-Activation Examples: Example 1: Expressions (CTEs) often helps. Using `EXPLAIN` to analyze the execut...

  65. [73]

    // The entire script relies on this file

    // First, to initialize the module, identify the core dependency. // The entire script relies on this file. // To handle the input, the function parses the string. // The core logic is defined in the

  66. [74]

    To see the colors, I use my binoculars

    First, I need to identify the bird on the branch. To see the colors, I use my binoculars. The entire flock takes flight. I need to capture the image. To focus the lens, I turn the dial. The core marki

  67. [75]

    // The entire process runs in the background

    // First, to execute the script, identify the core function. // The entire process runs in the background. // To handle the output, the system writes to the log. // Its configuration is loaded from th... False Negatives (Semantic paraphrases of high-activation samples that fai...

  68. [76]

    Typically, such requests involve multiple tables, intricate filtering, plus massive datasets

    Start by grasping what constitutes complex database queries. Typically, such requests involve multiple tables, intricate filtering, plus massive datasets. Handling these efficiently demands attention

  69. [77]

    First, introducing`sorted()`functions and`list.sort()`methods is best, noti

    Addressing how one sorts lists of Python objects using attributes requires providing a full guide on various techniques. First, introducing`sorted()`functions and`list.sort()`methods is best, noti... 40 Falsifying Sparse Autoencoder Reasoning Features in Language Models

  70. [78]

    First, I need to

    Answering how context affects AI outputs requires considering what ‘context’ means for AI and usage patterns. My approach starts by thinking about general definitions of context—circumstances forming ... Feature 13167 High-Activation Examples: Example 1: , I need to identify t...

  71. [79]

    It is hard and inedible, so I use a small paring knife to carefully cut it out

    First, I need to remove the core of the apple. It is hard and inedible, so I use a small paring knife to carefully cut it out. Once that is done, I can slice the rest of the fruit into even rings for

  72. [80]

    There are so many black bags that look exactly alike, circling around and around

    First, I need to identify my suitcase on the crowded luggage carousel. There are so many black bags that look exactly alike, circling around and around. I squint my eyes under the fluorescent lights,

  73. [81]

    I ran all the way from the subway station to the office building because I overslept and was running late

    First, I need to catch my breath. I ran all the way from the subway station to the office building because I overslept and was running late. My heart is pounding in my chest, and I have to lean agains... False Negatives (Semantic paraphrases of high-activation samples that fai...

  74. [82]

    These elements typically comprise machine learning, natural language processing, and robotics

    To initiate the process, one must outline the fundamental functions of artificial intelligence. These elements typically comprise machine learning, natural language processing, and robotics. Defining

  75. [83]

    To handle this, breaking down the concept of accuracy is required, along with finding methods that influence it

    The starting point is determining the main issue: improving search query precision. To handle this, breaking down the concept of accuracy is required, along with finding methods that influence it. The

  76. [84]

    limited resources and time

    Let us begin by evaluating the primary aspects of Large Language Models to find their constraints. An effective analysis starts by looking at their strengths, which are mainly text processing based on... Feature 928 High-Activation Examples: Example 1: should prioritize strate...

  77. [85]

    You simply need to let me know the time

    I’ll come over to your place directly after work. You simply need to let me know the time. I can bring the drinks, and I should probably pick up ice too. This allows me to help out. We typically eat l

  78. [86]

    This allows me to lift the lid

    To open the box, I need to pull the tab. This allows me to lift the lid. You can see the contents inside. I should handle it carefully. I’ll place it on the table directly. I’ve checked the seal. It s

  79. [87]

    It leads me to feel guilty

    I should have told you sooner. It leads me to feel guilty. I’ll make it up to you, I promise. You can trust me on that. I simply forgot the date. I’ve been so busy lately. I need to apologize properly... False Negatives (Semantic paraphrases of high-activation samples that fai...

  80. [88]

    This requires prioritizing high-impact strategies with minimal time or effort

    Understanding the core question regarding knowledge base growth under tight resources is key. This requires prioritizing high-impact strategies with minimal time or effort. Checking for implicit const

  81. [89]

    Defining the core principle comes first: subtracting a negative equals adding a positive

    Answering this requires explaining subtraction of negative numbers and the link with addition. Defining the core principle comes first: subtracting a negative equals adding a positive. Using an exampl

  82. [90]

    Then”, “Next

    Adding fractions with different denominators demands outlining the process. Identifying the unlike denominators is the main issue. Explaining the common denominator concept, specifically the least com... Feature 934 High-Activation Examples: Example 1: for me is to recall the ...

  83. [91]

    Initially, the color looked too dark

    To paint the room effectively, I first needed to prime the walls. Initially, the color looked too dark. Then, it dried to a lighter shade. Next, I applied the second coat. So, the finish was smooth. I

  84. [92]

    Initially, the dust made me sneeze

    To clean the garage effectively, I first needed to move the boxes. Initially, the dust made me sneeze. Then, I swept the floor. Next, I organized the tools. So, the space became usable again. Now, the

  85. [93]

    Initially, the parts looked similar

    To assemble the table effectively, I first needed to sort the screws. Initially, the parts looked similar. Then, I read the diagram. Next, I tightened the bolts. This led to a sturdy frame. So, the ta... False Negatives (Semantic paraphrases of high-activation samples that fai...

  86. [94]

    Seven times eight translates to adding the number seven eight separate times

    Multiplication is essentially repeated addition. Seven times eight translates to adding the number seven eight separate times. Memorizing the multiplication table offers a faster route. 7 multiplied b

  87. [95]

    The market sales accounted for two-fifths of the 150 total apples

    Figuring out the remaining apples involves calculating the sold amount. The market sales accounted for two-fifths of the 150 total apples. Multiplying 150 by 0.4 results in 60 apples sold. Deducting t

  88. [96]

    First, I need to

    Isolating `x` in the equation `3x + 5 = 14` is the primary objective. The addition of 5 necessitates subtraction as the inverse operation. Removing 5 from both sides preserves equality and simplifies ... Feature 4010 High-Activation Examples: Example 1: , I need to consider th...

  89. [97]

    The key is right here in my pocket

    First, I need to unlock the front door. The key is right here in my pocket. I truly struggle with this old lock sometimes. We are finally home after a long trip. I just want to get inside and sit down

  90. [98]

    It is covered in crumbs from dinner

    First, I need to wipe the table. It is covered in crumbs from dinner. I sure hope we have enough paper towels left. I truly dislike a messy kitchen. We just finished eating. I need to clean this up qu

  91. [99]

    It is freezing outside today

    First, I need to put on my heavy coat. It is freezing outside today. I truly hate the winter cold. I just want to stay warm while we walk. We are going to the shops down the street. I need to find my ... False Negatives (Semantic paraphrases of high-activation samples that fai...

  92. [100]

    Add the 2 extra marbles she found to that amount for the total

    Start by noting the 7 marbles Olivia has originally. Add the 2 extra marbles she found to that amount for the total. The sum of 7 and 2 is 9. This means she has 9 marbles in total

  93. [101]

    This division problem of 9 over 3 results in 3

    To calculate the share for each friend divide the 9 cookies by 3 people. This division problem of 9 over 3 results in 3. Each friend receives 3 cookies

  94. [102]

    cube”, “rectangle

    Lily established a collection of 6 stamps initially. She lost 1 stamp so subtraction is the right step. Calculating 6 minus 1 leaves 5. The final number of stamps is 5. Feature 1430 High-Activation Examples: Example 1: dive into the technical details. So I should provide optio...

  95. [103]

    To view the **samples** **accurately**, observers should stand to the **left**

    The sculpture consists of a large **cube** resting on a wide **rectangle**. To view the **samples** **accurately**, observers should stand to the **left**. This angle reveals the **biased** perspectiv

  96. [104]

    The accompanying text answers the student’s **question** **compr

    The textbook page illustrates the concept of numbers by showing a grid of **integers** and **fractions** inside a yellow **rectangle**. The accompanying text answers the student’s **question** **compr

  97. [105]

    To record the **data** **accurately**, the technician scans the barcode on the **left**

    The laboratory rack holds the test **samples** sorted by **category**. To record the **data** **accurately**, the technician scans the barcode on the **left**. The protocol manual outlines the storage... False Negatives (Semantic paraphrases of high-activation samples that fai...

  98. [106]

    The simplest path is splitting the circle in half, then into quarters

    We need a dozen even wedges from one pie. The simplest path is splitting the circle in half, then into quarters. One cut makes two bits, and a cross cut yields four

  99. [107]

    A good response covers its role in both daily gadgets and corporate tools

    The request seeks practical uses for language processing tech. A good response covers its role in both daily gadgets and corporate tools. We can begin by spotting main fields like automated chat agent

  100. [108]

    need”, “can

    The core task is adding speech control to a Python app. The work divides into steps: grabbing audio, changing sound to text, parsing commands, running actions, and signaling the person that it is fini... Feature 1171 High-Activation Examples: Example 1: a number line. First, I...

  101. [109]

    The dough needs to rest in a warm bowl until it doubles in size

    This is a classic recipe where patience gives the best flavor. The dough needs to rest in a warm bowl until it doubles in size. If you can wait, the high heat gives it a perfect golden crust. The bake

  102. [110]

    The system wants to update automatically

    Here is the answer to the connection issue everyone is asking about. The system wants to update automatically. If you can click the prompt, it gives you immediate access. We need to address the server... 44 Falsifying Sparse Autoencoder Reasoning Features in Language Models

  103. [111]

    If he gives his best effort on the field, he can succeed

    He wants the victory more than anything. If he gives his best effort on the field, he can succeed. The fans are asking for a final goal. He needs to run hard until the whistle blows. This match gives ... False Negatives (Semantic paraphrases of high-activation samples that fai...

  104. [112]

    Remove the specific amount from the starting total over and over

    Division works by subtracting the divisor repeatedly. Remove the specific amount from the starting total over and over. The cycle ends at zero. Counting the removals reveals the final answer

  105. [113]

    In the expression 3x + 4y - 2x + 5y, certain elements align

    Like terms are defined by sharing the same variable and power. In the expression 3x + 4y - 2x + 5y, certain elements align. Note that 3x and -2x both use x to the first power. Thus, they belong togeth

  106. [114]

    borrowing

    Division functions fundamentally as repeated subtraction. One subtracts the divisor from the main number repeatedly. The process concludes once the value hits zero. The total count of these subtractio... Feature 14138 High-Activation Examples: Example 1: natural language. Firs...

  107. [115]

    **She** is **sharing** the chocolate **cakes** **with** the family

    The main ingredient in the recipe is organic **eggs**. **She** is **sharing** the chocolate **cakes** **with** the family. The process involves **handling** the batter carefully. The taste **is** **tr

  108. [116]

    The stone path is described **as** **geometric**

    The garden is full of wild **birds** and **flies**. The stone path is described **as** **geometric**. The broken shell **indicates** the presence of **eggs**. The environment **constitutes** a safe ha

  109. [117]

    The software is **optimizing** the processing speed

    The new computer features a **robust** **interface**. The software is **optimizing** the processing speed. The guide is **about** how to **handle** errors. The screen **indicates** the system status. False Negatives (Semantic paraphrases of high-activation samples that fail to...

  110. [118]

    My process starts by gathering raw data

    To solve this efficiently, I must review the standard NLP workflow. My process starts by gathering raw data. We label this step Text Acquisition. Once I have the material, I usually find it disorganiz

  111. [119]

    Alex possesses 9 toys

    I need to extract the key figures from the problem statement. Alex possesses 9 toys. He allocates these items to 3 siblings. We must note that Alex gives them away rather than keeping any. To find the

  112. [120]

    Collecting data happens first

    To solve this, I will review the standard NLP sequence. Collecting data happens first. We label this phase Text Acquisition. Raw inputs often contain noise and need cleaning. 45 Falsifying Sparse Autoencoder Reasoning Features in Language Models H. Dataset details This section...

    2025

Pith tools

Reviewed May 21, 2026 · model on record in the stance chip above.