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Paper Citation Record · LEDGER

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models

As of 19 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 3 inbound Pith citation observations for arXiv:2508.17734.

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

pith.paper-citation-record.v1
2508.17734 v1

Coverage vector

measured 33 of 33 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:47:21.132773Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T00:57:22.872902Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T12:59:53.047847Z

Reference resolution

33 of 33 outbound references displayed

  • verified exact0
  • verified fuzzy3
  • unresolved30
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 08415b34-c8ec-4d53-8fcd-4c0a8ff2fd0e · outbound

This paper cites Qwen Technical Report.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Qwen Technical Report

Reference 1

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source=arxiv_source observed=2026-08-05T16:47:18.532533Z digest=sha256:96b8df1fdbcd660d18d08a1fa35545d565092209cfefcbfabb71aa99156eacbc

Observation f06ad9c0-03ab-48b7-8f58-b6fe934f4b68 · outbound

This paper cites Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling

Reference 2

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source=arxiv_source observed=2026-08-05T16:47:18.639727Z digest=sha256:20b3648f5ce835b2b3891820f2fc4e6a50b22dbd687afa3824267344b9f105d9

Observation f3fe1b96-9e49-4917-963e-e5658d3fad08 · outbound

This paper cites Piqa: Reasoning about physical commonsense in natural language.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Piqa: Reasoning about physical commonsense in natural language

Reference 3

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source=arxiv_source observed=2026-08-05T16:47:18.746514Z digest=sha256:c0d448f4793d49cfa5add3dd40101563c2b96203e19a275c4defd5d7566bb0e7

Observation 7c705f94-9853-46a2-8b65-f37140178d97 · outbound

This paper cites Editing factual knowledge in language models.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Editing factual knowledge in language models

Reference 4

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source=arxiv_source observed=2026-08-05T16:47:18.828214Z digest=sha256:8c165469d6796383e595fd4409a124a66772e3b297920b61643044c4e21b4d04

Observation d571667b-3af2-490d-8fb6-e837630800d7 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 5

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source=arxiv_source observed=2026-08-05T16:47:18.893768Z digest=sha256:05481102e76e54bbe7724f0e91864716f35018cb9d70c08459b47894803418f2

Observation 0873c3d0-79dc-438e-b19d-dd25506c05c1 · outbound

This paper cites Knowledge neurons in pretrained transformers.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Knowledge neurons in pretrained transformers

Reference 6

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source=arxiv_source observed=2026-08-05T16:47:18.998554Z digest=sha256:36e3f87c8cfe9d6ba3e92632f525953278f1e7f3f3162ec3333ecad558910147

Observation 16a2b864-e812-49f6-8673-4dd97fe2f0a1 · outbound

This paper cites A framework for few-shot language model evaluation, 07 2024.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models A framework for few-shot language model evaluation, 07 2024

Reference 7

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source=arxiv_source observed=2026-08-05T16:47:19.075776Z digest=sha256:e54fdad45476612a404e1fb93d609a3cf591e50d3c8891fb7cf99237f33dbfec

Observation c35682f4-607b-4ee1-ac91-ebc8014652ff · outbound

This paper cites Transformer feed-forward layers are key-value memories.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Transformer feed-forward layers are key-value memories

Reference 8

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source=arxiv_source observed=2026-08-05T16:47:19.132132Z digest=sha256:dce1e1eaa9c62580a6dd394b2263e38a3e220fed1a97dbd72d265e36c4400fc9

Observation 1943563b-6490-44cf-853a-78278d546c1b · outbound

This paper cites The Llama 3 Herd of Models.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models The Llama 3 Herd of Models

Reference 9

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source=arxiv_source observed=2026-08-05T16:47:19.202280Z digest=sha256:175093ef812b1f0bc4046eb2ffa9409bb1c945684e31ff7988e310c0dc5bb0b1

Observation ff1a42a7-6e55-4e63-8f60-4fd3d64b5a1a · outbound

This paper cites Rae, and Laurent Sifre.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Rae, and Laurent Sifre

Reference 10

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-05T16:47:19.277780Z digest=sha256:be91dbe09217aa9846bf2d602cb6ab60bc7a002b9d29b2f845e22a3bc58569fe

Observation 06fc52a5-2750-4ee1-b643-03891995c460 · outbound

This paper cites Mini CPM : Unveiling the potential of small language models with scalable training strategies.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Mini CPM : Unveiling the potential of small language models with scalable training strategies

Reference 11

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source=arxiv_source observed=2026-08-05T16:47:19.374322Z digest=sha256:a18a9aff9cdb9836c8f812f5105da9227bfe56164578b00f705c137bc3e9fefc

Observation 0977488b-6e2a-49b2-9b53-4e023aec2dc0 · outbound

This paper cites Analyzing feed-forward blocks in transformers through the lens of attention maps.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Analyzing feed-forward blocks in transformers through the lens of attention maps

Reference 12

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-05T16:47:19.452666Z digest=sha256:cd397732fd9a50c3e457922d46e8ee81e2c58cf4d0f9b3155114ad3602d17422

Observation 4a1ef59a-1d60-48bd-99f0-78470c794758 · outbound

This paper cites Zero-Shot Relation Extraction via Reading Comprehension.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Zero-Shot Relation Extraction via Reading Comprehension

Reference 13

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source=arxiv_source observed=2026-08-05T16:47:19.543263Z digest=sha256:e374cb92c20f4afc3891ce21ad50c8c677319d1e638000dc01ad515aa74b1326

Observation 40d18a0f-a0d7-4adb-b6c4-cd1bfafa7b65 · outbound

This paper cites Fineweb-edu: the finest collection of educational content, 2024.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Fineweb-edu: the finest collection of educational content, 2024

Reference 14

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source=arxiv_source observed=2026-08-05T16:47:19.611528Z digest=sha256:18e5c321ed3299bb405ad682e52e3285de0e7350cc6b95458285be03150a10ad

Observation c5aa325c-95cb-4542-a1f4-f4ba8891f18a · outbound

This paper cites Locating and editing factual associations in GPT.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Locating and editing factual associations in GPT

Reference 15

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source=arxiv_source observed=2026-08-05T16:47:19.716827Z digest=sha256:71cf409bf2c71a0cd13e19e5fc79ece299f11b6bbdf882b7e8e6573c3f747ebd

Observation e3c5222e-edc2-490d-a694-27470fee8c60 · outbound

This paper cites Pointer sentinel mixture models.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Pointer sentinel mixture models

Reference 16

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source=arxiv_source observed=2026-08-05T16:47:19.822444Z digest=sha256:761653e3270334e55edf42e5f3cb46ddbe60803a80c6ebebfde23e1ab1491ff7

Observation 05207cd3-ff60-4b7d-a1b6-338e815a7ba6 · outbound

This paper cites an unresolved cited work.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Unresolved cited work

Reference 17

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-05T16:47:19.931430Z digest=sha256:4d75e1ec40ac6f1e30a099eedf821bc5e67e9557266777023bc2a4b8928ea823

Observation 6e4971e3-565b-4c16-835c-d849e7d7ba9c · outbound

This paper cites 2 OLMo 2 Furious.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models 2 OLMo 2 Furious

Reference 18

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source=arxiv_source observed=2026-08-05T16:47:20.021791Z digest=sha256:d6dbe2c7360994a4f66b25a22bb851db77ed516204bf2c1fba787e659cefd48c

Observation 3926c5a6-10c4-445e-91bd-65fc5f3de5ea · outbound

This paper cites The lambada dataset, Aug 2016.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models The lambada dataset, Aug 2016

Reference 19

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source=arxiv_source observed=2026-08-05T16:47:20.060132Z digest=sha256:c14602aabd5b6d26fa1d973ba7cf6dd160390c7c74a2d899654e05d9000f8efa

Observation 20583272-b1b1-4f02-b235-7a75ce1fc72a · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Winogrande: An adversarial winograd schema challenge at scale

Reference 20

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source=arxiv_source observed=2026-08-05T16:47:20.112911Z digest=sha256:aba4a11225c97c76889876324a54facb99b7cd91b5d5927bfaca3a206ac930ff

Observation 05af4b76-b859-4bf1-badc-4b29850e3fab · outbound

This paper cites GLU Variants Improve Transformer.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models GLU Variants Improve Transformer

Reference 21

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source=arxiv_source observed=2026-08-05T16:47:20.203670Z digest=sha256:5a9a1665a06038bb8be3bddfd6a65f5753e8bea76832da79349c31bd22440191

Observation 8fc0d314-0d4d-48bc-9497-53fbcc73747a · outbound

This paper cites Layer by Layer: Uncovering Hidden Representations in Language Models.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Layer by Layer: Uncovering Hidden Representations in Language Models

Reference 22

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source=arxiv_source observed=2026-08-05T16:47:20.290385Z digest=sha256:6c98df37e9927987e3e8993e00d8331c56eb3ab7ea581b75ba760d87264d4f53

Observation 0427c39a-9e8e-48e5-9ab5-c81d5cafdc23 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models LLaMA: Open and Efficient Foundation Language Models

Reference 23

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Observation bf935a5b-09dd-41f9-9ff1-31dabd492cda · outbound

This paper cites Attention is all you need.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Attention is all you need

Reference 24

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source=arxiv_source observed=2026-08-05T16:47:20.464055Z digest=sha256:0845cc2800c61227dc79bc466945f12907c6bc62bf58345789d668bd3579842e

Observation 91187e76-5f2e-4c55-a58e-51996b39d663 · outbound

This paper cites On layer normalization in the transformer architecture.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models On layer normalization in the transformer architecture

Reference 25

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-05T16:47:20.500653Z digest=sha256:400289b3558992e9b8d25a78b7c5375438c30e6c9627087d11220cc464bad1bc

Observation 81a50e45-4a39-4ba9-87fb-33e3dcd1e5fe · outbound

This paper cites On Layer Normalization in the Transformer Architecture.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models On Layer Normalization in the Transformer Architecture

Reference 26

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source=arxiv_source observed=2026-08-05T16:47:20.582931Z digest=sha256:1d35373e5a432b477202a2f84bf3b6f07416733f86568d6a00d8317e417246bf

Observation e724b23d-d7f9-4eca-8764-cb461c7d0263 · outbound

This paper cites Qwen3 Technical Report.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Qwen3 Technical Report

Reference 27

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source=arxiv_source observed=2026-08-05T16:47:20.671374Z digest=sha256:09c86a3ac6509dd01fae3531da117c6f4235f8be9ccf44aab68e357280e2868d

Observation 2c6f9f48-cc68-4a7b-9b14-9c9ffda33e2c · outbound

This paper cites Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Hellaswag: Can a machine really finish your sentence? In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019

Reference 28

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Observation 86d6fcbe-443f-43da-b1e6-34642db7612c · outbound

This paper cites OPT: Open Pre-trained Transformer Language Models.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models OPT: Open Pre-trained Transformer Language Models

Reference 29

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Observation 40cc5619-41ce-4f33-9366-c17a852c7de3 · outbound

This paper cites write newline.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models write newline

Reference 30

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source=arxiv_source observed=2026-08-05T16:47:20.928740Z digest=sha256:4e19e8cf1ae6cfe1bc2aa716a66a074880b8530ea3326c43da9f905b176b58c1

Observation 3eb665a7-c9b6-4dd9-95e6-303ef934be68 · outbound

This paper cites @esa (Ref.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models @esa (Ref

Reference 31

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Observation bb942742-8394-458a-b939-737359663b95 · outbound

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Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Unresolved cited work

Reference 32

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Observation 76c01695-2020-4358-96ec-c7659765bb96 · outbound

This paper cites an unresolved cited work.

Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models Unresolved cited work

Reference 33

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Pith citing papers

Observation 75d245b3-9045-4ee3-85c4-756a16af6286 · inbound

Variable-Width Transformers cites this paper.

Variable-Width Transformers Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models

Reference 17

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arxiv_id, observed 2026-07-03T20:58:58.615011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ee9ca529-2ee5-49dd-8422-2fe3466f51a8 · inbound

Tapered Language Models cites this paper.

Tapered Language Models Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models

Reference 16

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arxiv_id, observed 2026-07-04T10:09:44.020128Z

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source=pdf_text observed=2026-06-26T09:11:20.341634Z digest=sha256:fbad9ab4cd953cf774a7f0c140e48e8f51d549391bc8aca6b96a7d3ef0582203

Observation 0f563cd1-4b6c-4763-8845-3367d29c930b · inbound

Discovering Millions of Interpretable Features with Sparse Autoencoders cites this paper.

Discovering Millions of Interpretable Features with Sparse Autoencoders Layerwise Importance Analysis of Feed-Forward Networks in Transformer-based Language Models

Reference 26

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arxiv_id, observed 2026-07-04T12:59:53.049302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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