Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

InverseScope: Scalable Activation Inversion for Interpreting Large Language Models

T0 review · 4 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read InverseScope makes activation inversion practical for large LLMs by conditioning a decoder on the target activation through control layers, enabling quantitative feature tests.

desk verdict Solid architecture improvement, but a normalization error in Appendix B undermines the central FCR metric; the paper needs a substantive fix before its quantitative claims are credible. read the letter →

arxiv 2506.07406 v3 pith:EHW46C42 submitted 2025-06-09 cs.LG cs.AI

Yifan Luo , Zhennan Zhou , Bin Dong This is my paper
classification cs.LGcs.AI
keywords activationinversionmechanisticinterpretabilityconditionalgenerationfeatureconsistencyratein-contextlearningtaskvectorssparseautoencoderslargelanguagemodels
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

InverseScope claims that the information encoded in an LLM activation can be read out by sampling natural-language inputs whose activations fall near it, without assuming linearity or sparsity. To make this tractable in thousands of dimensions, it trains a decoder-only conditional generator whose hidden states receive the target activation through dedicated control layers, and reports that this lowers the average sample refusal rate on the indirect object identification (IOI) task from 10.2% to 3.5% versus the token-prepending baseline. The paper introduces the feature consistency rate, the probability that a sampled neighbor shares a hypothesized feature with the seed input, as a quantitative test of whether a feature is genuinely encoded at a given site. Using this protocol it recovers known IOI circuits, outperforms sparse autoencoders at attribute identification on RAVEL, and explains why in-context-learning task vectors appear only in specific layers: the task feature is encoded before the query feature, and later the query feature masks the task vector rather than destroying it.

What carries the argument

The load-bearing mechanism is the control-layer conditioning architecture. Each control layer computes a query from the generator's current hidden state and keys and values from the target activation $\hat{z}$, with an attention-like gate $\omega = \tanh(\langle q, k \rangle)$, and adds the resulting signal to the hidden state; a multi-head variant sums per-head outputs. Site-specific linear projections map activations from arbitrary layers and heads into a shared latent space, so one generator serves many sites. Training first fine-tunes the decoder backbone on the task prior, then freezes it and trains only the control and projection layers by maximizing conditional log-likelihood on noise-perturbed activation pairs. This construction is what makes sampling from a high-dimensional kernel-weighted neighborhood efficient enough for quantitative use; the refusal-rate drop from 10.2% to 3.5% on IOI is the evidence that it works.

What would settle it

Take a small task with a finite input space, such as the roughly three million IOI template combinations, enumerate the true kernel-weighted distribution by computing distances to target activations for all inputs, and compare InverseScope's empirical samples to it for a set of target activations; if the sampled distribution deviates substantially from the ground-truth neighborhood, for instance by yielding a higher feature consistency rate than the true neighborhood does, the framework's quantitative conclusions do not follow.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that activation inversion can be made assumption-light, quantitative, and scalable at once. Given a target activation $\hat{z}$, the object of study is the distribution $P(x; \hat{z}) \propto k(d(z(x), \hat{z})/\epsilon)$ over inputs; InverseScope approximates this distribution with a GPT-2-small decoder fine-tuned on task inputs and augmented with control layers that inject the conditioning activation into every layer. Training with norm-banded noise perturbation keeps the generator from memorizing input-activation pairs. The feature consistency rate $\mathrm{FCR}(f) = \mathbb{E}_{(\hat{x},\hat{z})\sim D}\,\mathbb{E}_{x\sim P(x;\hat{z})}\,\mathbb{I}\{f(x)=f(\hat{x})\}$ then tests whether a hypothesized feature is preserved across the neighborhood. The paper demonstrates the architecture's sample efficiency on IOI, strong attribute classification on RAVEL, and uses the resulting feature-consistency maps to give a mechanistic account of task vectors in in-context learning: in the layers where patching works, attention outputs encode the task but not the query; once the query feature appears, the residual stream encodes the specific output token, so task-vector patching fails even though the task representation is still present.

Load-bearing premise

The result stands only if the trained generator actually produces the same variety of inputs that the neighborhood of a target activation defines, rather than leaning on the task prior or on memorized training examples; the paper checks this only by refusal rates, not by comparing distributions.

Editorial extensions

If this is right

  • If the generator is faithful, activation inversion becomes a generic tool for testing feature hypotheses on real LLMs: no linearity or sparsity assumption, just geometric neighborhood sampling and a feature function.
  • The feature consistency rate gives a per-site, per-feature score that can identify circuits without human patch experiments; on IOI it flags Name Mover, Negative Name Mover, Backup Name Mover, and Subject Inhibition heads.
  • The ICL account predicts that task vectors are masked rather than erased in deeper layers, so task information should still be detectable by attention-output readouts after layer 16 even when residual-stream patching fails.
  • InverseScope's attribute classification on RAVEL outperforms both general and task-trained sparse autoencoders across all tested layers, suggesting inversion recovers attribute information that sparse decompositions miss.
  • The same pipeline transfers to a second 7B model, LLaMA-2-7B, with the same task-versus-query feature separation, indicating the mechanism is not specific to one model family.

Reading between the lines

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

  • A testable extension the paper does not run: patch attention outputs only at layers 13-15 versus only after layer 16 to confirm that the task feature alone drives correct translation and the query feature alone drives source-token output, which would isolate the proposed causal story.
  • If the generator's fidelity were validated distributionally, the feature consistency rate could be inverted into a search procedure: instead of pre-specifying a feature, cluster samples from the conditional neighborhood and propose features from the clusters, automating the hypothesis-generation step the paper leaves to humans.
  • The norm-band trick with $\delta=0.1$ and the kernel bandwidth $\epsilon$ are hyperparameters with no reported sensitivity analysis; their values plausibly trade off sample diversity against label noise, and the refusal-rate improvement may depend on this tuning.
  • Because the backbone is always GPT-2-small, InverseScope interprets the target model through a fixed text prior; how much of the recovered semantics reflects the target model versus the generator's own linguistic bias remains untested and could be probed by re-running with a different backbone.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper introduces InverseScope, a conditional generative architecture for interpreting LLM activations by sampling natural-language inputs that produce nearby activations. The method defines a target distribution P(x; \hat z) over inputs weighted by a kernel of the distance between their activation z(x) and a target activation \hat z, and trains a Transformer generator with additional control layers to sample from this distribution. The authors report improved sample efficiency over InversionView on the IOI task, higher attribute classification accuracy than sparse autoencoders on RAVEL, and a new explanation of task vectors in in-context learning based on the separation of task and query features across layers. The central claims are that the generator faithfully approximates P(x;\hat z) and that the resulting feature consistency rate (Eq. 1) provides a quantitative test of interpretability hypotheses.

Significance. If the approximation were correct, InverseScope would offer a genuinely assumption-light tool for feature interpretability, with a quantitative evaluation protocol and meaningful efficiency gains. The paper's validation strategy is appropriate in spirit: it benchmarks against InversionView and SAE baselines, and cross-checks against the known IOI circuit. However, the mathematical foundation of the approximation is flawed (Appendix B), so the quantitative results, including FCR values and the ICL mechanism, do not currently measure the stated distribution. The core idea remains promising, but the evidence as presented does not support the claims.

major comments (4)
  1. [Appendix B, Eq. (1)] The derivation that the perturbation training approximates P(x;\hat z) is incorrect for the cosine distance with the norm-band modification. Because d(z,\hat z)=1 - (z·\hat z)/(||z|| ||\hat z||) is independent of the radial coordinate, the normalization constant of the noise distribution p(r)\propto k(\tilde d(r+\hat z,\hat z)) is Z(\hat z)=C_n ||\hat z||^n, where n is the activation dimension. Consequently, the posterior learned by maximum likelihood is proportional to P(X)(x) k(d(\hat z,z(x)))/||z(x)||^n, not the claimed P(x;\hat z). In dimensions n=768 (GPT-2-small) and n=2304 (Gemma-2-2B), a 10% norm difference changes the weight by (1.1)^n, which is astronomically large. This biases all FCR computations (Eq. 1, Figures 4, 6, 7) and the ICL explanation, and the refusal-rate check in Section 4.1 does not detect the bias because cosine distance is scale-invariant. The statement in Appendix B that the equality holds for sufficiently large \delta is false for any finite \delta: the normalizer remains proportional to the n-th power of the norm.
  2. [Section 4.1, Eq. (1)] The kernel bandwidth \epsilon is never reported for any experiment. The refusal rate and the FCR in Eq. (1) depend directly on \epsilon through the kernel, yet the paper does not state its value for the IOI, RAVEL, or ICL experiments, nor the kernel form (hard threshold vs. Gaussian) used in each. Without this value, the quantitative results cannot be reproduced or compared to InversionView, which also requires \epsilon for its neighborhood definition.
  3. [Section 4.3, Figure 7] The claim that the separation of task and query feature consistency 'underlies' the task-vector phenomenon is not supported by the experiments. The FCR curves (left panel) and the task-vector patching results (right panel) are only correlated across layers; no causal intervention on the task or query features is performed to establish that the emergence of the query feature is what masks the task vector. The patching experiment itself does not manipulate the identified features, so the explanation remains a hypothesis.
  4. [Abstract and Contributions] The abstract states that InverseScope scales to models of up to 14B parameters and reveals sentence-level linear analogies, but the body reports experiments only up to 7B (LLaMA-2-7B) and does not discuss any analogies. This overclaiming should be corrected to match the actual scope of the paper.
minor comments (5)
  1. [Appendix D.1.1] There is an incomplete sentence, 'The activation at', at the end of the dataset description; it should be removed or completed.
  2. [Section 4.1, Figure 5] Refusal rates are reported as averages without error bars or variance across the 144 attention heads or across training seeds; given the wide spread shown in the histogram, confidence intervals are needed to support the comparison.
  3. [Appendix B] The hyperparameter \delta=0.1 is introduced only in the appendix; it should be stated in the main text's method section, along with its role in the perturbation scheme.
  4. [Section 5] Typo: 'Nautral language interpretability' should be 'Natural language interpretability'.
  5. [Table 1] The caption refers to the target input \hat x but the displayed completions are partial; it would be clearer to show the full prompt or denote the generation prefix explicitly.

Circularity Check

1 steps flagged · score 4.0 of 10

FCR is measured under a norm-biased posterior because the Appendix B equality fails for cosine distance, so the quantitative conclusions partially reduce to the fitted generator rather than to Eq. 1.

  1. other [Appendix B, "Approximating P(x; zhat) via Activation Perturbation", paragraph after the modified distance definition and the delta=0.1 setting; load-bearing for Eq. (1) FCR and Figures 4, 6, 7.]
    "One can verify that for sufficiently large δ, the equality ~p(x, z = zhat) = P(x; zhat) still holds. However, increasing δ introduces greater variance into the training labels, making the conditional generator harder to train. In practice, we set δ = 0.1 as a trade-off between theoretical fidelity and empirical stability."

    The paper's bridge between training and Eq. (1) is the asserted equality ~p(x, z=zhat)=P(x; zhat). With cosine distance and the norm-band dtilde used in the experiments, the noise density p(r|a) ∝ k(dtilde(a+r,a)) has normalizer Z(a)=∫_{||z||∈[(1-δ)||a||,(1+δ)||a||]} k(1-u·a/||a||) dz = C_n ||a||^n. The MLE objective therefore converges to P_theta(x|z) ∝ P(X)(x) k(dtilde(z,z(x)))/Z(z(x)), and conditioning on z=zhat gives P(x; zhat)/||z(x)||^n restricted to the norm band, not P(x; zhat). For n=768 or 2304, this norm factor is astronomically biased, so the FCR values and ICL/FCR curves are expectations under the generator's own norm-biased training posterior rather than under the defined P of Eq. (1).

full rationale

There is no self-citation chain or imported uniqueness theorem: the method is benchmarked against InversionView, SAE baselines, the known IOI circuit of Wang et al. (2022), and independent task-vector patching, which provide external checks and keep the paper from being wholly circular. However, the Appendix B derivation that justifies InverseScope as a sampler for P(x; zhat) is load-bearing and, on inspection, fails for the cosine-distance norm-band scheme actually used (delta=0.1). The learned conditional carries an extra ||z(x)||^{-n} factor, so the reported FCR (Eq. 1) is not the quantity defined in Section 2. This makes the FCR-based layer curves and the ICL mechanism explanation partially self-referential: they measure a distribution constructed by the training procedure rather than the activation geometry specified by Eq. (1). The external IOI and task-vector checks mitigate but do not remove this circularity, hence a score of 4 rather than higher.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

No new physical or conceptual entities are introduced; the free parameters and assumptions listed are the load-bearing choices that the FCR results depend on but that receive only partial validation.

free parameters (2)
  • kernel bandwidth epsilon
    Defines the neighborhood size in activation space for the kernel k(d/eps); refusal rates and FCR depend critically on it, but the paper does not report the values used in experiments.
  • norm-band delta = 0.1
    Chosen as a trade-off between theoretical fidelity and empirical stability for the modified cosine distance in Appendix B; directly affects the perturbation distribution and thus the generator training.
assumptions (4)
  • domain assumption Similar activations encode semantically similar information (continuity of neural networks).
    Section 2 grounds the definition of P(x; z_hat) via kernel proximity; no formal guarantee exists for discrete token spaces, so this is a heuristic premise.
  • domain assumption The conditional generator can approximate P(x; z_hat) sufficiently well for FCR to be meaningful.
    Section 3.2 trains by maximizing conditional log-likelihood with noisy activations; accuracy is only assessed by refusal rate, not by distributional distance, so the approximation quality is assumed.
  • ad hoc to paper The modified cosine distance with norm-band delta=0.1 yields a well-defined and accurate perturbation distribution.
    Appendix B states theoretical fidelity holds for sufficiently large delta; the finite value 0.1 is a practical compromise, and its impact on the approximated distribution is not quantified.
  • domain assumption Using GPT-2-small as the generator backbone is adequate regardless of the target model being interpreted.
    Section 3.2 always uses GPT-2-small as the backbone, even when interpreting Gemma-2-2B or LLaMA-2-7B; this assumes the small model is expressive enough to capture the inverse mapping for different target models.

how reviews work

0 comments
Cite this review

Pith. "Pith review of InverseScope: Scalable Activation Inversion for Interpreting Large Language Models." pith.science (2026). https://pith.science/paper/EHW46C42

@misc{pith2026250607406,
  author       = {Pith},
  title        = {Pith review of: InverseScope: Scalable Activation Inversion for Interpreting Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/EHW46C42}},
  note         = {Machine review of arXiv:2506.07406}
}
read the original abstract

Understanding the internal representations of large language models (LLMs) is a central challenge in interpretability research. Existing feature interpretability methods often rely on strong structural assumptions--such as linearity or sparsity--that may not hold in practice. In this work, we introduce InverseScope, an assumption-light and scalable framework for interpreting neural activations via input inversion. Given a target activation, InverseScope characterizes its encoded information by generating natural-language inputs that produce nearby activations, grounding abstract internal states in concrete language. To overcome the prohibitive cost of sampling in high-dimensional activation spaces, we propose a novel control-layer conditioning architecture that substantially improves sample efficiency compared to prior token-prepending approaches. We demonstrate that InverseScope reveals rich geometric structure in LLM representation spaces, including sentence-level linear analogies. The framework scales to state-of-the-art open-source models of up to 14B parameters and generalizes to out-of-distribution inputs, enabling systematic analysis of activation neighborhoods.

Figures

Figures reproduced from arXiv: 2506.07406 by the authors.

Figure 1
Figure 1. An toy example of samples in activation space and their corresponding inputs. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Example results from applying our method to the indirect object identification task on [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Network architecture of InverseScope. The decoder-only Transformer backbone is shown [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (5 more)
Figure 4
Figure 4. Figure 4: Feature consistency rate of the outputs of GPT-2-small’s attention heads in the IOI task. [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: Histogram of sample refusal rates across GPT-2-small’s 144 attention heads in the IOI task. The vertical dashed line indicates the average refusal rate across all heads. Lower refusal rates correspond to higher sample effi￾ciency. The y-axis is log-scaled for illustrat…
Figure 7
Figure 7. Figure 7: Left: Feature consistency rates of the outputs of Gemma-2-2B’s attention layers in the ICL task. Blue represents the task feature. Orange represents the query feature. Right: Results of task-vector patching experiments. Green represents the rate at which the patched in…
Figure 8
Figure 8. Figure 8: Detailed plot showing the relationship between [PITH_FULL_IMAGE:figures/full_fig_p014_8.png]
Figure 9
Figure 9. Figure 9: Left: Feature consistency rates of the outputs of LLaMA-2-7B’s attention layers in the ICL task. Blue represents the task feature. Orange represents the query feature. Right: Results of task-vector patching experiments. Green represents the rate at which the patched in…

Discussion (0). Sign in to comment.

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. Mechanistic Interpretability of Cognitive Complexity in LLMs via Linear Probing using Bloom's Taxonomy

    cs.AI 2026-02 conditional novelty 6.0 of 10

    Linear probes on LLM residual streams classify Bloom's Taxonomy levels with high accuracy, but the result may reflect prompt lexico-semantic cues rather than a general cognitive-complexity representation.

Reference graph

Works this paper leans on

24 extracted references · 6 canonical work pages · cited by 1 Pith paper

  1. [1]

    Understanding intermediate layers using linear classifier probes.ArXiv, abs/1610.01644,

    Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes.ArXiv, abs/1610.01644,

    arXiv

  2. [5]

    Sparse autoen- coders find highly interpretable features in language models.arXiv preprint arXiv:2309.08600,

    Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. Sparse autoen- coders find highly interpretable features in language models.arXiv preprint arXiv:2309.08600,

    arXiv

  3. [9]

    Roee Hendel, Mor Geva, and Amir Globerson

    URLhttps://api.semanticscholar.org/ CorpusID:266933130. Roee Hendel, Mor Geva, and Amir Globerson. In-context learning creates task vectors.ArXiv, abs/2310.15916,

    arXiv

  4. [10]

    Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva, and Atticus Geiger

    URLhttps://api.semanticscholar.org/CorpusID: 264439386. Jing Huang, Zhengxuan Wu, Christopher Potts, Mor Geva, and Atticus Geiger. Ravel: Evaluat- ing interpretability methods on disentangling language model representations.arXiv preprint arXiv:2402.17700, 2024a. Xinting Huang, Madhur Panwar, Navin Goyal, and Michael Hahn. Inversionview: A general-purpose...

    arXiv

  5. [11]

    Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2.arXiv preprint arXiv:2408.05147,

    Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, J´anos Kram´ar, Anca Dragan, Rohin Shah, and Neel Nanda. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2.arXiv preprint arXiv:2408.05147,

    arXiv

  6. [12]

    Understanding deep image representations by inverting them.2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp

    Aravindh Mahendran and Andrea Vedaldi. Understanding deep image representations by inverting them.2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5188– 5196,

    work page 2015

  7. [14]

    Multifaceted feature visualization: Uncov- ering the different types of features learned by each neuron in deep neural networks.ArXiv, abs/1602.03616,

    Anh Totti Nguyen, Jason Yosinski, and Jeff Clune. Multifaceted feature visualization: Uncov- ering the different types of features learned by each neuron in deep neural networks.ArXiv, abs/1602.03616,

    arXiv

  8. [16]

    The linear representation hypothesis and the geometry of large language models.ArXiv, abs/2311.03658,

    Kiho Park, Yo Joong Choe, and Victor Veitch. The linear representation hypothesis and the geometry of large language models.ArXiv, abs/2311.03658,

    arXiv

Show all 24 references
  1. [18]

    Accessed: 2025-05-15

    URLhttps://www.lesswrong.com/posts/tojtPCCRpKLSHBdpn/ the-strong-feature-hypothesis-could-be-wrong. Accessed: 2025-05-15. Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhu- patiraju, L´eonard Hussenot, Thomas Mesnard, Bobak Shahriari, A...

    2025 arXiv

  2. [19]

    Llama 2: Open founda- tion and fine-tuned chat models.arXiv preprint arXiv:2307.09288,

    11 Under review as a conference paper at ICLR 2026 Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Niko- lay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. Llama 2: Open founda- tion and fine-tuned chat models.arXiv p...

    2026 arXiv

  3. [20]

    In- terpretability in the wild: a circuit for indirect object identification in gpt-2 small.ArXiv, abs/2211.00593,

    Kevin Wang, Alexandre Variengien, Arthur Conmy, Buck Shlegeris, and Jacob Steinhardt. In- terpretability in the wild: a circuit for indirect object identification in gpt-2 small.ArXiv, abs/2211.00593,

    arXiv

  4. [21]

    the indirect object name in the prompt

    A DEFINING FEATURE FUNCTIONS The term “feature” can have different meanings depending on the context, so we clarify our usage here. In this paper, we define a feature as a function over inputs. For example, a binary classification function that returns 1 if an inputxis a harmf...

    2026

  5. [23]

    Training is performed for 100,000 steps with a batch size of 2048 using the AdamW optimizer

    as baselines, we train a series of JumpReLU SAEs with feature width 4096 specifically on the RA VEL dataset. Training is performed for 100,000 steps with a batch size of 2048 using the AdamW optimizer. We apply anL 0 sparsity penalty with regularization parameterρ= 5×10 −5. Al...

    2024

  6. [24]

    [City] is a city in the country of

    in the original model. D.2.3 REPRESENTATIVEFEATURE FORSAE BASELINES To perform attribute classification using SAE, we assign a representative feature to each attribute in RA VEL. For this, we record which SAE features are activated for 100,000 input activations. For each attri...

    2023

  7. [64]

    Then, [B] and [A] went to the [PLACE]. [B] gave a [OBJECT] to

    The site-specific transformations consist of linear layers with input and output dimensions that are equal to the target model’s hidden dimension. All parameters in the additional layers are initialized using Kaiming initialization, except for the value projection matrices in ...

    2023

  8. [2009]

    Scaling and evaluating sparse autoencoders.arXiv preprint arXiv:2406.04093,

    Leo Gao, Tom Dupr ´e la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders.arXiv preprint arXiv:2406.04093,

    arXiv

  9. [2013]

    Mechanistic interpretability for ai safety–a review.arXiv preprint arXiv:2404.14082,

    Leonard Bereska and Efstratios Gavves. Mechanistic interpretability for ai safety–a review.arXiv preprint arXiv:2404.14082,

    arXiv

  10. [2014]

    Aleksandar Makelov, George Lange, and Neel Nanda

    URLhttps://api.semanticscholar.org/CorpusID:206593185. Aleksandar Makelov, George Lange, and Neel Nanda. Towards principled evaluations of sparse autoencoders for interpretability and control.arXiv preprint arXiv:2405.08366,

    arXiv

  11. [2016]

    Alexander Pan, Lijie Chen, and Jacob Steinhardt

    URLhttps://api.semanticscholar.org/CorpusID: 5970910. Alexander Pan, Lijie Chen, and Jacob Steinhardt. Latentqa: Teaching llms to decode activations into natural language.arXiv preprint arXiv:2412.08686,

  12. [2019]

    Open problems in mechanistic interpretability.arXiv preprint arXiv:2501.16496,

    Lee Sharkey, Bilal Chughtai, Joshua Batson, Jack Lindsey, Jeff Wu, Lucius Bushnaq, Nicholas Goldowsky-Dill, Stefan Heimersheim, Alejandro Ortega, Joseph Bloom, et al. Open problems in mechanistic interpretability.arXiv preprint arXiv:2501.16496,

    arXiv

  13. [2020]

    Selfie: Self-interpretation of large language model embeddings.arXiv preprint arXiv:2403.10949,

    Haozhe Chen, Carl V ondrick, and Chengzhi Mao. Selfie: Self-interpretation of large language model embeddings.arXiv preprint arXiv:2403.10949,

    arXiv

  14. [2022]

    Patch- scopes: A unifying framework for inspecting hidden representations of language mod- els.ArXiv, abs/2401.06102,

    Asma Ghandeharioun, Avi Caciularu, Adam Pearce, Lucas Dixon, and Mor Geva. Patch- scopes: A unifying framework for inspecting hidden representations of language mod- els.ArXiv, abs/2401.06102,

    arXiv

  15. [2023]

    Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al

    https://transformer- circuits.pub/2023/monosemantic-features/index.html. Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners.Advan...

    2023

  16. [2024]

    Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space.arXiv preprint arXiv:2203.14680,

    10 Under review as a conference paper at ICLR 2026 Mor Geva, Avi Caciularu, Kevin Ro Wang, and Yoav Goldberg. Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space.arXiv preprint arXiv:2203.14680,

    2026 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.