Paper Citation Record · LEDGER

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data

As of 14 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 1 inbound Pith citation observation for arXiv:2506.14014.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.14014 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:29:54.913346Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-26T18:52:22.357765Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T02:49:25.403135Z

Reference resolution

31 of 31 outbound references displayed

verified exact0
verified fuzzy20
unresolved11
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External citation measurements

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Outbound references

Observation 18deb3c4-577c-49c1-8515-8abc199b977e · outbound

This paper cites and Polosukhin, I.Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data and Polosukhin, I.Attention is all you need.Advances in Neural Information Processing Systems, 30, 2017

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:59.568108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 147035ec-8df8-4ec6-88b2-44045063d2f9 · outbound

This paper cites A unified, scalable framework for neural population decoding.Advances in Neural Information Processing Systems, 36:44937–44956, 2023.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data A unified, scalable framework for neural population decoding.Advances in Neural Information Processing Systems, 36:44937–44956, 2023

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:59.373019Z

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Observation 9d2a4193-1fa3-4ac4-ac54-7ccc582a6881 · outbound

This paper cites The mythos of model interpretability.Communications of the ACM, 61(10), pp.36–43.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data The mythos of model interpretability.Communications of the ACM, 61(10), pp.36–43

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:59.170207Z

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Observation 6db34450-1cb8-4619-a449-a291a8bd86ad · outbound

This paper cites Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1(5), pp.206–215.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Nature Machine Intelligence, 1(5), pp.206–215

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:58.901057Z

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Observation ccb63b0e-043c-42e5-9b8f-cc7575d3746e · outbound

This paper cites Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T00:29:53.264934Z

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Observation e2bbd3e9-330f-4df7-83fe-ee36c6b3718a · outbound

This paper cites and Elhadad, N., 2015.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data and Elhadad, N., 2015

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:58.744209Z

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Observation b25dd971-39fa-4cb9-aed2-c6271fa3096a · outbound

This paper cites Mohsenzadeh Khaligh-Razavi and Nikolaus Kriegeskorte.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Mohsenzadeh Khaligh-Razavi and Nikolaus Kriegeskorte

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:58.567510Z

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Observation bfc1255c-286a-4650-9431-05bf494f9632 · outbound

This paper cites and Bengio, Y .Understanding intermediate layers using linear classifier probes.2017.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data and Bengio, Y .Understanding intermediate layers using linear classifier probes.2017

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:58.416046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0b5b5fb3-55df-4e84-b922-5c1475408d2c · outbound

This paper cites Sparse Autoencoders Find Highly Interpretable Features in Language Models.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Sparse Autoencoders Find Highly Interpretable Features in Language Models

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T00:29:53.516807Z

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Observation 6df0744c-a9b5-46e7-85cd-166cba15537c · outbound

This paper cites L., Anil, C., Denison, C., Askell, A., Lasenby, R., Wu, Y ., Kravec, S., Schiefer, N., Maxwell, T., Joseph, N., Tamkin, A., Nguyen, K., McLean, B., Burke, J.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data L., Anil, C., Denison, C., Askell, A., Lasenby, R., Wu, Y ., Kravec, S., Schiefer, N., Maxwell, T., Joseph, N., Tamkin, A., Nguyen, K., McLean, B., Burke, J

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:58.258586Z

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Observation da0c16e6-ccc8-416e-81f3-5a11ceef6395 · outbound

This paper cites SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data SAeUron: Interpretable Concept Unlearning in Diffusion Models with Sparse Autoencoders

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T00:29:53.662752Z

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Observation 7cb7263a-6e3a-4c78-8bb5-248480229f5e · outbound

This paper cites Olshausen and David J.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Olshausen and David J

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:58.129262Z

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Observation 7b467b81-5ba2-4035-9f47-c653ef8b8433 · outbound

This paper cites k-Sparse Autoencoders.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data k-Sparse Autoencoders

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T00:29:53.807034Z

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Observation 4d1d7073-279b-4672-ae30-d6a3194cb975 · outbound

This paper cites an unresolved cited work.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:29:57.950449Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ce70e389-11e1-4e9a-ac42-329ffb915eac · outbound

This paper cites X., Arora, V ., Knight, I.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data X., Arora, V ., Knight, I

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:57.774557Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation fd999811-9c21-4172-95bd-cdae8cab55f9 · outbound

This paper cites E., Lecoq, J.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data E., Lecoq, J

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:57.579076Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ea83707d-4e55-4fb5-8843-d32b8dd835e8 · outbound

This paper cites an unresolved cited work.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:29:57.406600Z

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Observation 17d2b270-8f7f-479e-b207-bb2b6b55585f · outbound

This paper cites L.Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex.Journal of Neurophysiology, 88(1):455–463, 2002.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data L.Spatial structure and symmetry of simple-cell receptive fields in macaque primary visual cortex.Journal of Neurophysiology, 88(1):455–463, 2002

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:57.184817Z

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Observation b74dee79-6f89-4170-ae1e-12878bb6f882 · outbound

This paper cites J., and Movshon, J.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data J., and Movshon, J

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:57.005321Z

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Observation dd335bc4-5912-4667-b0c8-79334da14c80 · outbound

This paper cites Perceiver IO: A General Architecture for Structured Inputs & Outputs.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Perceiver IO: A General Architecture for Structured Inputs & Outputs

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T00:29:54.233127Z

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Observation eacbed49-46dd-4a1d-ac44-c35aa329b6e1 · outbound

This paper cites J., Ocker, G.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data J., Ocker, G

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:56.842133Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4645a6f4-ba03-4250-a7ea-88dd8609c3b8 · outbound

This paper cites ,D(B) by sampling N datapoints with replacement fromD.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data ,D(B) by sampling N datapoints with replacement fromD

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:56.605867Z

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Observation 17bc5617-4219-4ed9-b018-129dc2a4925e · outbound

This paper cites an unresolved cited work.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Unresolved cited work

Reference 23

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:29:56.381752Z

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Observation 1cfc44be-ac87-4334-8650-69bc5d62db6a · outbound

This paper cites an unresolved cited work.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:29:56.174777Z

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Observation 49554273-e8a0-4c42-84ba-baefc526a3f5 · outbound

This paper cites an unresolved cited work.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:29:55.993353Z

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Observation 7abcd161-93fa-403f-9403-b732bf11da7c · outbound

This paper cites an unresolved cited work.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Unresolved cited work

Reference 26

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:29:55.808865Z

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Observation d8236344-d3b4-4624-aaf7-e66e6a76bd43 · outbound

This paper cites receptive fields.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data receptive fields

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:55.621322Z

Source-reported events for the cited work

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Observation 4d60fdad-4503-4ad7-9b55-157d3616a8bb · outbound

This paper cites Each Ai,j matrix was flattened into a 40-dimensional vector and standardized via z-scoring.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data Each Ai,j matrix was flattened into a 40-dimensional vector and standardized via z-scoring

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:55.456484Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation c66a0266-cc1f-407e-8e6f-fe8a5f005cc7 · outbound

This paper cites UMAP parameters were set ton neighbors = 15, min_dist= 0.1, using correlation distance as the metric.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data UMAP parameters were set ton neighbors = 15, min_dist= 0.1, using correlation distance as the metric

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:55.295608Z

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Observation 0472effa-b389-4fd2-8672-a55a5577d103 · outbound

This paper cites HDBSCAN was configured with a minimum cluster size of 5 and minimum samples of 1, using Euclidean distance.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data HDBSCAN was configured with a minimum cluster size of 5 and minimum samples of 1, using Euclidean distance

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:55.238400Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 6910c523-2dad-42e0-ba22-731b4c439801 · outbound

This paper cites We visualized both the UMAP embeddings colored by cluster identity and summarized the average activation patterns within each cluster.

Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data We visualized both the UMAP embeddings colored by cluster identity and summarized the average activation patterns within each cluster

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:29:55.147586Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation de501a82-c3aa-4ca8-8bf1-cdd6339a90fb · inbound

Can neurons speak? Semantic narration of vision at single-cell resolution cites this paper.

Can neurons speak? Semantic narration of vision at single-cell resolution Beyond Black Boxes: Enhancing Interpretability of Transformers Trained on Neural Data

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-04T02:49:25.404779Z

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