Mechanistic Interpretability with Sparse Autoencoder Neural Operators | Paper Citation Record

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This paper cites Brain-score: Which artificial neural network for object recognition is most brain-like?.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Brain-score: Which artificial neural network for object recognition is most brain-like?

Reference 1

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This paper cites The topology and geometry of neural representations.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators The topology and geometry of neural representations

Reference 2

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This paper cites High-level visual representations in the human brain are aligned with large language models.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators High-level visual representations in the human brain are aligned with large language models

Reference 3

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This paper cites Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Stabilization of a brain–computer interface via the alignment of low-dimensional spaces of neural activity

Reference 4

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This paper cites Universality and individuality in neural dynamics across large populations of recurrent networks.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Universality and individuality in neural dynamics across large populations of recurrent networks

Reference 5

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This paper cites Equivalence between representational similarity analysis, centered kernel alignment, and canonical correlations analysis.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Equivalence between representational similarity analysis, centered kernel alignment, and canonical correlations analysis

Reference 6

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This paper cites Soft matching distance: A metric on neural representations that captures single-neuron tuning.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Soft matching distance: A metric on neural representations that captures single-neuron tuning

Reference 7

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This paper cites Representation topology divergence: A method for comparing neural network representations.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Representation topology divergence: A method for comparing neural network representations

Reference 8

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This paper cites Representational similarity analysis–connecting the branches of systems neuroscience.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Representational similarity analysis–connecting the branches of systems neuroscience

Reference 9

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This paper cites Position: The platonic representation hypothesis.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Position: The platonic representation hypothesis

Reference 10

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This paper cites Proof of a perfect platonic representation hypothesis.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Proof of a perfect platonic representation hypothesis

Reference 11

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This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 12

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This paper cites Fourier neural operator for parametric partial differential equations.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Fourier neural operator for parametric partial differential equations

Reference 13

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This paper cites Neural operator: Learning maps between function spaces with applications to pdes.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Neural operator: Learning maps between function spaces with applications to pdes

Reference 14

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Observation cd2f55fa-dded-47e4-9409-d5b67af896f4 · outbound

This paper cites Neural operators for accelerating scientific simulations and design.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Neural operators for accelerating scientific simulations and design

Reference 15

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This paper cites Vars-fusi: Variable sampling for fast and efficient functional ultrasound imaging using neural operators.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Vars-fusi: Variable sampling for fast and efficient functional ultrasound imaging using neural operators

Reference 16

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This paper cites Noble–neural operator with biologically-informed latent embeddings to capture experimental variability in biological neuron models.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Noble–neural operator with biologically-informed latent embeddings to capture experimental variability in biological neuron models

Reference 17

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This paper cites FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators

Reference 18

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This paper cites Geometry-informed neural operator for large-scale 3d pdes.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Geometry-informed neural operator for large-scale 3d pdes

Reference 19

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This paper cites Unify- ing subsampling pattern variations for compressed sensing mri with neural operators.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Unify- ing subsampling pattern variations for compressed sensing mri with neural operators

Reference 20

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Observation b1f39c06-b087-4aa7-9cfa-8e00c29f2d37 · outbound

This paper cites Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav).

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)

Reference 21

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Observation 226ca26f-33b8-4257-978c-f948605fc288 · outbound

This paper cites Emergence of simple-cell receptive field properties by learning a sparse code for natural images.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Emergence of simple-cell receptive field properties by learning a sparse code for natural images

Reference 22

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This paper cites Sparse coding with an overcomplete basis set: A strategy employed by v1?.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Sparse coding with an overcomplete basis set: A strategy employed by v1?

Reference 23

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Observation b03c3bc0-cdb2-4a4a-b30e-706ca2489d12 · outbound

This paper cites Toy Models of Superposition.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Toy Models of Superposition

Reference 24

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This paper cites Sparse autoencoders find highly interpretable features in language models.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Sparse autoencoders find highly interpretable features in language models

Reference 25

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Observation f5ff2886-da2a-495e-be3a-e4263af7f94c · outbound

This paper cites Towards monosemanticity: Decomposing language models with dictionary learning.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Towards monosemanticity: Decomposing language models with dictionary learning

Reference 26

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Observation a74c71cc-6b9e-4dc3-823f-fc61655aa2a1 · outbound

This paper cites Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Jumping Ahead: Improving Reconstruction Fidelity with JumpReLU Sparse Autoencoders

Reference 27

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Observation 88201636-0630-4aeb-abba-55d34788a8c7 · outbound

This paper cites The linear representation hypothesis and the geometry of large language models.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators The linear representation hypothesis and the geometry of large language models

Reference 28

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This paper cites Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Scaling monosemanticity: Extracting interpretable features from claude 3 sonnet

Reference 29

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Observation 92b55590-ceb1-4681-a96c-f7fc74c89a4a · outbound

This paper cites Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2

Reference 30

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Observation 4fbdf2fb-08ba-4e0b-b284-5cbb72f1f261 · outbound

This paper cites Scaling and evaluating sparse autoencoders.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Scaling and evaluating sparse autoencoders

Reference 31

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Observation 2b11c4a6-e61b-46d8-b002-d9d5b70451f3 · outbound

This paper cites Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Archetypal SAE: Adaptive and Stable Dictionary Learning for Concept Extraction in Large Vision Models

Reference 32

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Observation 3dd738ff-9c72-4855-aeee-2f5741092f89 · outbound

This paper cites Sparse feature circuits: Discovering and editing interpretable causal graphs in language models.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Sparse feature circuits: Discovering and editing interpretable causal graphs in language models

Reference 33

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Observation 9fa8ac97-0085-4e0f-ad78-e32e9e07f15f · outbound

This paper cites SAEBench: A comprehensive benchmark for sparse autoencoders in language model interpretability.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators SAEBench: A comprehensive benchmark for sparse autoencoders in language model interpretability

Reference 34

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This paper cites an unresolved cited work.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Unresolved cited work

Reference 35

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Observation 942bea03-0c7f-41c4-b3d2-00f8163420a0 · outbound

This paper cites Hastie, R.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Hastie, R

Reference 36

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Observation ae25c74a-319a-4636-8313-6af2b8c9da46 · outbound

This paper cites Compressed sensing.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Compressed sensing

Reference 37

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Observation adb7cd56-e93d-49b4-8d7b-cff3d8b69eda · outbound

This paper cites Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information

Reference 38

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Observation 6a4ef2d4-b3d9-4743-ab44-d8b2229d533a · outbound

This paper cites An introduction to compressive sampling.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators An introduction to compressive sampling

Reference 39

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Observation 91d777dd-294d-4fb9-8de6-f731ab05a340 · outbound

This paper cites Estimating unknown sparsity in compressed sensing.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Estimating unknown sparsity in compressed sensing

Reference 40

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Observation a428011d-3eed-406e-8f9a-ed4c00b99ec1 · outbound

This paper cites Online dictionary learning for sparse coding.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Online dictionary learning for sparse coding

Reference 41

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Observation cc5f6071-4332-4303-a611-4b2ff83a3fc0 · outbound

This paper cites Learning sparsely used overcomplete dictionaries via alternating minimization.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Learning sparsely used overcomplete dictionaries via alternating minimization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:46.996113Z

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Observation 65dfc4a4-05b6-470b-8ef7-3cc03a5b4275 · outbound

This paper cites Alternating minimization for dictionary learning: Local Convergence Guarantees.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Alternating minimization for dictionary learning: Local Convergence Guarantees

Reference 43

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Observation 5f304319-2e91-41a8-b182-27ecfc7d0c58 · outbound

This paper cites Tolooshams,Deep Learning for Inverse Problems in Engineering and Science.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Tolooshams,Deep Learning for Inverse Problems in Engineering and Science

Reference 44

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Observation 7c939d1a-8080-4486-b666-363cf2b2f927 · outbound

This paper cites Learning fast approximations of sparse coding.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Learning fast approximations of sparse coding

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.037973Z

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Observation 53089542-d5ee-4973-9372-a961e59c5392 · outbound

This paper cites Learning step sizes for unfolded sparse coding.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Learning step sizes for unfolded sparse coding

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.141953Z

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Observation 843ea1a1-5479-4b96-910f-22e5b010c69b · outbound

This paper cites Understanding approximate and unrolled dictio- nary learning for pattern recovery.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Understanding approximate and unrolled dictio- nary learning for pattern recovery

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.133655Z

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Observation 11e26938-5e76-40e1-859b-c58c454c24b7 · outbound

This paper cites Stable and interpretable unrolled dictionary learning.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Stable and interpretable unrolled dictionary learning

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.136235Z

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Observation e91e639e-5714-4452-b1c7-8baaea625f55 · outbound

This paper cites On the dynamics of gradient descent for autoen- coders.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators On the dynamics of gradient descent for autoen- coders

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.144871Z

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Observation 09e36e81-1dfe-4f36-9489-58d6b93bd3b7 · outbound

This paper cites Simple, efficient, and neural algorithms for sparse coding.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Simple, efficient, and neural algorithms for sparse coding

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.150289Z

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Observation 6b9dc672-2270-470e-b70e-2406a1354951 · outbound

This paper cites Theoretical linear convergence of unfolded ista and its practical weights and thresholds.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Theoretical linear convergence of unfolded ista and its practical weights and thresholds

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.158988Z

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Observation 17b86caa-5a93-4454-ab67-38a3f8a24e4b · outbound

This paper cites Sparse coding and autoencoders.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Sparse coding and autoencoders

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.138803Z

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Observation 374682a6-b6cd-4cba-ac68-c9499b7df102 · outbound

This paper cites Convolutional dictionary learning based auto-encoders for natural exponential-family distributions.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Convolutional dictionary learning based auto-encoders for natural exponential-family distributions

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.009284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 6028c485-34de-4ec3-99f9-2eb5ab93e7b4 · outbound

This paper cites Noodl: Provable online dictionary learning and sparse coding.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Noodl: Provable online dictionary learning and sparse coding

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.014334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 046054d5-ba97-448b-8df6-eb70d66c874a · outbound

This paper cites Projecting assumptions: The duality between sparse autoencoders and concept geometry.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Projecting assumptions: The duality between sparse autoencoders and concept geometry

Reference 55

Resolution: verified exact; arxiv_id, observed 2026-05-18T19:01:45.797623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation a0a14a14-c863-42f7-b9ef-0f971c36b420 · outbound

This paper cites Elad,Sparse and redundant representations: from theory to applications in signal and image processing.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Elad,Sparse and redundant representations: from theory to applications in signal and image processing

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.041403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 7cf248c1-224d-4f6f-8b01-d202746f5b6b · outbound

This paper cites K-svd: An algorithm for designing overcomplete dictionaries for sparse representation.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators K-svd: An algorithm for designing overcomplete dictionaries for sparse representation

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.147614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 8f47fd64-2717-4723-91cd-8123329d5a70 · outbound

This paper cites Efficient generation of transcrip- tomic profiles by random composite measurements.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Efficient generation of transcrip- tomic profiles by random composite measurements

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.156069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 9c00df6f-e861-4f7c-9d57-a53b47ac0db9 · outbound

This paper cites Compressed sensing for highly efficient imaging transcriptomics.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Compressed sensing for highly efficient imaging transcriptomics

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.161447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 9715b89c-92f9-40a3-95b8-79ef74c4417f · outbound

This paper cites Regression shrinkage and selection via the lasso.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Regression shrinkage and selection via the lasso

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.126328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 5cf4d8c6-885b-4dfd-882a-6108c655a155 · outbound

This paper cites Atomic decomposition by basis pursuit.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Atomic decomposition by basis pursuit

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.173033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 2b4af22e-c625-44a6-981c-9a261d04fc59 · outbound

This paper cites Proximal algorithms.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Proximal algorithms

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.169748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation cb943835-ff9f-4f1a-8519-5b404bf94b4d · outbound

This paper cites An iterative thresholding algorithm for linear inverse problems with a sparsity constraint.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators An iterative thresholding algorithm for linear inverse problems with a sparsity constraint

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.119417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation d4041c8b-b494-45d8-b5c3-565b0c682cc8 · outbound

This paper cites A fast iterative shrinkage-thresholding algorithm for linear inverse problems.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators A fast iterative shrinkage-thresholding algorithm for linear inverse problems

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.122854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 3b21f6bb-e06d-4975-88d5-0c4a57410bc6 · outbound

This paper cites Efficient learning of sparse representations with an energy-based model.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Efficient learning of sparse representations with an energy-based model

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.129250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation a1edfa21-419f-408a-b562-73453575e015 · outbound

This paper cites Sparse feature learning for deep belief networks.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Sparse feature learning for deep belief networks

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.176080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 4963712b-829f-4cba-bd82-e527567e020d · outbound

This paper cites Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-05-18T19:01:45.823687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 6b909e7e-c414-4cc4-9faf-71d5c29196ca · outbound

This paper cites Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Algorithm unrolling: Interpretable, efficient deep learning for signal and image processing

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.113287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation a6038926-cbd6-41b8-a15a-c315b877dc0d · outbound

This paper cites Convolutional neural networks analyzed via convolutional sparse coding.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Convolutional neural networks analyzed via convolutional sparse coding

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.164295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 1e915e33-6d29-4366-b23c-88a9aab79771 · outbound

This paper cites Working locally thinking globally: Theoretical guarantees for convolutional sparse coding.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Working locally thinking globally: Theoretical guarantees for convolutional sparse coding

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.083522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 28e8d3e5-35d5-4395-8e4d-60e959df2a01 · outbound

This paper cites Deeply-sparse signal representations (ds2p).

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Deeply-sparse signal representations (ds2p)

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.086357Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 6434c794-abd4-472f-9552-6b4795f59be1 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Towards A Rigorous Science of Interpretable Machine Learning

Reference 72

Resolution: verified exact; local_arxiv, observed 2026-05-18T19:01:45.801934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 99ad64ff-9630-4841-b4a2-70d13a24130c · outbound

This paper cites an unresolved cited work.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Unresolved cited work

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.077786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation 6df7d218-c83e-48e8-ac94-068159427a79 · outbound

This paper cites an unresolved cited work.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Unresolved cited work

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.069354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

Observation eb0a8d57-e6c8-45ad-803c-3a30c2222998 · outbound

This paper cites an unresolved cited work.

Mechanistic Interpretability with Sparse Autoencoder Neural Operators Unresolved cited work

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-18T19:01:47.080496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-07-22T06:31:00.163083+00:00.

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Reference resolution

Outbound references

Pith citing papers