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Paper Citation Record · LEDGER

Expand Neurons, Not Parameters

As of 9 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2510.04500.

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

pith.paper-citation-record.v1
2510.04500 v3

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T11:33:18.712887Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved39
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1c4b9164-73e9-48f1-bb6f-e7de55801d1b · outbound

This paper cites On the complexity of neural computation in superposition.

Expand Neurons, Not Parameters On the complexity of neural computation in superposition

Reference 1

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source=arxiv_source observed=2026-08-04T11:33:13.557312Z digest=sha256:b46c7fbe631207dfe06a7921e3b1e8d8304ce8b61b2a5f5bd7c7544f88e2c903

Observation 4ecc8e7c-d5b2-40dd-9676-8c76e14d05a9 · outbound

This paper cites Towards Combinatorial Interpretability of Neural Computation.

Expand Neurons, Not Parameters Towards Combinatorial Interpretability of Neural Computation

Reference 2

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source=arxiv_source observed=2026-08-04T11:33:13.657166Z digest=sha256:4cc980834dea033188a29ac28d2a3ba464fec88a33f574dbd82a098827dbf245

Observation f1225378-5451-420a-8113-7d8fec206700 · outbound

This paper cites The lottery ticket hypothesis for pre-trained bert networks.

Expand Neurons, Not Parameters The lottery ticket hypothesis for pre-trained bert networks

Reference 3

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source=arxiv_source observed=2026-08-04T11:33:13.774805Z digest=sha256:d0a86df63f89d5bb22c33c7042b0f1c770e2172be3e73e42246ddc4f521e26b1

Observation 718f3f4c-8c46-4b19-92c9-40c5c9119760 · outbound

This paper cites The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models.

Expand Neurons, Not Parameters The lottery tickets hypothesis for supervised and self-supervised pre-training in computer vision models

Reference 4

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source=arxiv_source observed=2026-08-04T11:33:13.829371Z digest=sha256:13dba535e1c62552edcf1a0086d4cba9f42f7a43ad99fabd6bcc101664a64863

Observation 5086abc5-286b-4fa9-bde2-8334b87fcc53 · outbound

This paper cites Net2Net: Accelerating Learning via Knowledge Transfer.

Expand Neurons, Not Parameters Net2Net: Accelerating Learning via Knowledge Transfer

Reference 5

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source=arxiv_source observed=2026-08-04T11:33:13.904579Z digest=sha256:37f86382f9a653132636296871429f8ba0599a26c17c84dd37a9db00b124c2c9

Observation bef8eab3-90ef-4aa0-a094-efdd1e716db2 · outbound

This paper cites Imagenet: A large-scale hierarchical image database.

Expand Neurons, Not Parameters Imagenet: A large-scale hierarchical image database

Reference 6

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source=arxiv_source observed=2026-08-04T11:33:13.994761Z digest=sha256:221124cc57e002cb428931961a67c3c994bf64210668d98395d6a708fab678c1

Observation fd391cb1-75bc-4d5a-a0bc-60b0708f1bda · outbound

This paper cites Pure: Turning polysemantic neurons into pure features by identifying relevant circuits.

Expand Neurons, Not Parameters Pure: Turning polysemantic neurons into pure features by identifying relevant circuits

Reference 7

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source=arxiv_source observed=2026-08-04T11:33:14.087925Z digest=sha256:c488cf1da95eaf2a260016d94f137dc6b2b2c170c431ef84903b31ddf7a9caf2

Observation a8d849f9-fcd8-4e34-884f-4fea884d1c69 · outbound

This paper cites Toy Models of Superposition.

Expand Neurons, Not Parameters Toy Models of Superposition

Reference 8

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source=arxiv_source observed=2026-08-04T11:33:14.244738Z digest=sha256:10c3bca14e9c53e33a371942069caa06cdb87752a4cd2744886a20c3abf15c40

Observation 969fb15b-01f2-45e4-8094-8dbce678d410 · outbound

This paper cites Rigging the lottery: Making all tickets winners.

Expand Neurons, Not Parameters Rigging the lottery: Making all tickets winners

Reference 9

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source=arxiv_source observed=2026-08-04T11:33:14.382121Z digest=sha256:48bb93d3792f48c3897c9c4bcaff65f7b0d104e6b37f78119d37900fbed840b2

Observation 1d518cd7-da6e-44af-9ca1-a5f1dd62a5d1 · outbound

This paper cites MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models.

Expand Neurons, Not Parameters MaskLLM: Learnable Semi-Structured Sparsity for Large Language Models

Reference 10

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source=arxiv_source observed=2026-08-04T11:33:14.484741Z digest=sha256:2e0f10d3a8a3f6110a512ca098a5a68cbd668ae74007152b2a67a5092728e94c

Observation 5d03d10e-14a7-460b-a94f-2499c6fd9014 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

Expand Neurons, Not Parameters The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 11

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source=arxiv_source observed=2026-08-04T11:33:14.588254Z digest=sha256:47a0e129016d86180a676d26efc4127c202b27c1f6c57dad11512025c773683e

Observation f14b2c87-ade7-416b-9e06-52439f227372 · outbound

This paper cites Sparsegpt: Massive language models can be accurately pruned in one-shot.

Expand Neurons, Not Parameters Sparsegpt: Massive language models can be accurately pruned in one-shot

Reference 12

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source=arxiv_source observed=2026-08-04T11:33:14.669407Z digest=sha256:8c447146ded4914fa3aeaf4375ff9e377901b7c1f2eb04a1e93e9f192e412821

Observation 6fd936e0-445f-4980-8a79-c059e27133b6 · outbound

This paper cites Multimodal neurons in artificial neural networks.

Expand Neurons, Not Parameters Multimodal neurons in artificial neural networks

Reference 13

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source=arxiv_source observed=2026-08-04T11:33:14.791682Z digest=sha256:d2019a4ea76f8b99fbb922f8a924460bfe2f071cd0d5f034a6cbf204e6168869

Observation 5ab5f514-f0e2-4fd7-a162-f630313c056d · outbound

This paper cites Are wider nets better given the same number of parameters?.

Expand Neurons, Not Parameters Are wider nets better given the same number of parameters?

Reference 14

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source=arxiv_source observed=2026-08-04T11:33:14.924940Z digest=sha256:317ae539f95f5686748bff63273d20381d301dc3cb888431ec02185f8f011215

Observation 54340b46-e7cd-436c-8ace-784b398c07b5 · outbound

This paper cites Universal Neurons in GPT2 Language Models.

Expand Neurons, Not Parameters Universal Neurons in GPT2 Language Models

Reference 15

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source=arxiv_source observed=2026-08-04T11:33:15.014741Z digest=sha256:a90f00ad1a64fe29fbb950d2ddc0d0ee945ee8ce5b4cda5013e05f322bc4384d

Observation b04190b8-67f7-40e5-852f-a6614060038e · outbound

This paper cites Learning both weights and connections for efficient neural network.

Expand Neurons, Not Parameters Learning both weights and connections for efficient neural network

Reference 16

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source=arxiv_source observed=2026-08-04T11:33:15.124801Z digest=sha256:be3ab0d035797afcc6b778af2c01e17c9b00c734f5ffd87e3e0b1ce3944a7627

Observation 1696d495-f987-4c85-86c1-f35dfdc58ce5 · outbound

This paper cites Dynamic neural networks: A survey.

Expand Neurons, Not Parameters Dynamic neural networks: A survey

Reference 17

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source=arxiv_source observed=2026-08-04T11:33:15.177038Z digest=sha256:3ef73b70edda8788c603fbda0b15f67e72419d4497e968a8fc03532f9b3022ff

Observation 40ea786c-a2c5-4d8c-81d1-c329910fcffb · outbound

This paper cites Mathematical Models of Computation in Superposition.

Expand Neurons, Not Parameters Mathematical Models of Computation in Superposition

Reference 18

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source=arxiv_source observed=2026-08-04T11:33:15.465355Z digest=sha256:55079d7a8b88669d0f007481ca3f6848e24eb4c4e5df8120ac969f656b8f8bc1

Observation eb4b2656-3cd9-446f-8c90-fc98628569a1 · outbound

This paper cites Engineering Monosemanticity in Toy Models.

Expand Neurons, Not Parameters Engineering Monosemanticity in Toy Models

Reference 19

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source=arxiv_source observed=2026-08-04T11:33:15.807379Z digest=sha256:ffe07bf31dd0de34b96c00602afda3c5d0e172155a46bdafe5746fcd9f948861

Observation f40b7283-0cfc-4739-a6f0-26d697aae671 · outbound

This paper cites Learning multiple layers of features from tiny images.

Expand Neurons, Not Parameters Learning multiple layers of features from tiny images

Reference 20

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no resolver link, observed 2026-08-04T11:33:15.905776Z

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source=arxiv_source observed=2026-08-04T11:33:15.905776Z digest=sha256:aca4c2e678068f45e67955668072df10d52ec57584f365386550e9f1036b3343

Observation 16866f60-31c4-42d0-9da2-875eb330aa20 · outbound

This paper cites What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes.

Expand Neurons, Not Parameters What Causes Polysemanticity? An Alternative Origin Story of Mixed Selectivity from Incidental Causes

Reference 21

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source=arxiv_source observed=2026-08-04T11:33:15.991947Z digest=sha256:f7057ce6e9e8c07f1778b3094a9b1f2d5ea8fce0bb8c7b27778d55e9dc95093d

Observation 13952b60-c9bd-40a8-aef1-1107bdc77dd9 · outbound

This paper cites A Survey of Lottery Ticket Hypothesis.

Expand Neurons, Not Parameters A Survey of Lottery Ticket Hypothesis

Reference 22

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source=arxiv_source observed=2026-08-04T11:33:16.090153Z digest=sha256:9059a4a9f6fcc323ca9ddf8f98e6f6858fbc3d3e8edf53ffaf6f528e3eec1537

Observation 1edc2362-95b0-4b44-bf94-b6241aeff5bf · outbound

This paper cites Superposition Yields Robust Neural Scaling.

Expand Neurons, Not Parameters Superposition Yields Robust Neural Scaling

Reference 23

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Observation 61cb87d9-b096-4f8f-a757-3996f5cf17d6 · outbound

This paper cites Proving the lottery ticket hypothesis: Pruning is all you need.

Expand Neurons, Not Parameters Proving the lottery ticket hypothesis: Pruning is all you need

Reference 24

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source=arxiv_source observed=2026-08-04T11:33:16.354740Z digest=sha256:d8ac7cfcf03527dc226a4191d243fcb44da7103f2038eadd80a1c6acdaadc3c6

Observation 3b6204f2-53a5-478b-baa9-c1f98cd05ad9 · outbound

This paper cites Analysis of boolean functions.

Expand Neurons, Not Parameters Analysis of boolean functions

Reference 25

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source=arxiv_source observed=2026-08-04T11:33:16.454736Z digest=sha256:20ed026c1f1e14973287c829069f6b1f9f1ac88fd29925fea7ad048547e1602f

Observation fc42be5c-6667-4136-8bd4-4e920162d553 · outbound

This paper cites Zoom in: An introduction to circuits.

Expand Neurons, Not Parameters Zoom in: An introduction to circuits

Reference 26

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source=arxiv_source observed=2026-08-04T11:33:16.535988Z digest=sha256:0c654faba38888903a34301da481f171d4c391c729056347f9199ec4aeacec44

Observation 3fb9ff1c-6154-4124-acf8-ad77d0ea299f · outbound

This paper cites Mixturegrowth: Growing neural networks by recombining learned parameters.

Expand Neurons, Not Parameters Mixturegrowth: Growing neural networks by recombining learned parameters

Reference 27

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no resolver link, observed 2026-08-04T11:33:16.634903Z

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source=arxiv_source observed=2026-08-04T11:33:16.634903Z digest=sha256:d047eed2f1d85bfc318066fb1ca77f179b2328a67daac82ac8cb26c52159d4b4

Observation 27f6956f-4b6b-4ac4-a88b-96f48c798b55 · outbound

This paper cites Accelerating inference with sparsity using the nvidia ampere architecture and nvidia tensorrt.

Expand Neurons, Not Parameters Accelerating inference with sparsity using the nvidia ampere architecture and nvidia tensorrt

Reference 28

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source=arxiv_source observed=2026-08-04T11:33:16.744903Z digest=sha256:5f156d363ad2d90cd4164355c42cf9a52145a3147c0a4dbe7668d53cd8ece006

Observation 9c3a1ff8-ae13-4c44-a857-61eea2e9d919 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Expand Neurons, Not Parameters Learning transferable visual models from natural language supervision

Reference 29

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source=arxiv_source observed=2026-08-04T11:33:16.864999Z digest=sha256:fda6c718dfd2ce319df5efc7debf04f38ffde6b3b08eb9ff814ee32a71c2ae7c

Observation dd19aae6-2d35-4514-88d1-a725bd6b083a · outbound

This paper cites Polysemanticity and Capacity in Neural Networks.

Expand Neurons, Not Parameters Polysemanticity and Capacity in Neural Networks

Reference 30

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source=arxiv_source observed=2026-08-04T11:33:16.977876Z digest=sha256:c00ee313886f94a970af5c9608227674d15a5350efeb8c0e410c49a193c68b15

Observation ca2e2831-8dae-4016-b213-9f1d45f4f04e · outbound

This paper cites A Simple and Effective Pruning Approach for Large Language Models.

Expand Neurons, Not Parameters A Simple and Effective Pruning Approach for Large Language Models

Reference 31

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no resolver link, observed 2026-08-04T11:33:17.067257Z

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source=arxiv_source observed=2026-08-04T11:33:17.067257Z digest=sha256:d1e5ea5b3ae8559c9f5e64a20ddd0395047229eec0a332db6c382ee9aa3a2789

Observation 38d47441-2ea0-460d-b855-7fd2b2c92273 · outbound

This paper cites Firefly neural architecture descent: a general approach for growing neural networks.

Expand Neurons, Not Parameters Firefly neural architecture descent: a general approach for growing neural networks

Reference 32

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source=arxiv_source observed=2026-08-04T11:33:17.164936Z digest=sha256:023eb0ccb70a1dfe593e653ec8efec5a2fd7424696c0802dc850fbf8208d271f

Observation 2a288065-efe8-4b02-ad47-d4d8a6a54bbb · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Expand Neurons, Not Parameters Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 33

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source=arxiv_source observed=2026-08-04T11:33:17.270238Z digest=sha256:017546184075e4ecbb18677a8296e45e41f0a503460032e18c97b00b44338c44

Observation 1db67381-35de-408a-8c80-805b3d2c7aa8 · outbound

This paper cites An effective gram matrix characterizes generalization in deep networks.

Expand Neurons, Not Parameters An effective gram matrix characterizes generalization in deep networks

Reference 34

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source=arxiv_source observed=2026-08-04T11:33:17.374816Z digest=sha256:720e07ad721f47850aacec928feb04e6422ce64214a633e783960eb851d659da

Observation 0bff169f-713c-4fec-b2ce-cf1dd358f694 · outbound

This paper cites Growing Efficient Deep Networks by Structured Continuous Sparsification.

Expand Neurons, Not Parameters Growing Efficient Deep Networks by Structured Continuous Sparsification

Reference 35

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source=arxiv_source observed=2026-08-04T11:33:17.474748Z digest=sha256:ec502f0fc6ad796cf7b6317419f52a28a8445ea864d9cf7cd83a96c70d5d5d05

Observation e92a1b55-92f4-4007-8b67-faf1b59065f2 · outbound

This paper cites Deconstructing lottery tickets: Zeros, signs, and the supermask.

Expand Neurons, Not Parameters Deconstructing lottery tickets: Zeros, signs, and the supermask

Reference 36

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source=arxiv_source observed=2026-08-04T11:33:17.574889Z digest=sha256:0847b637b3f2689170ee75eeb8b23f598124d9bf98fe748fb63eceb038c6accd

Observation b6d2bd59-26b7-464b-88ac-838a4cd903ac · outbound

This paper cites write newline.

Expand Neurons, Not Parameters write newline

Reference 37

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source=arxiv_source observed=2026-08-04T11:33:18.177280Z digest=sha256:f8cd8169ccc3ebbe70a70558caa379a0f935dc795a0ca2a72c560597fb2cc8c6

Observation e40ee65a-09fd-490d-8334-2c982606a9f9 · outbound

This paper cites @esa (Ref.

Expand Neurons, Not Parameters @esa (Ref

Reference 38

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source=arxiv_source observed=2026-08-04T11:33:18.334748Z digest=sha256:fc03ebc672311066b6d9204f8125d2676eba6db9cfb9f311f3170b56105dfce0

Observation 1452af98-d1e0-4ed1-8583-7018278cbc3e · outbound

This paper cites an unresolved cited work.

Expand Neurons, Not Parameters Unresolved cited work

Reference 39

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source=arxiv_source observed=2026-08-04T11:33:18.537816Z digest=sha256:99028ebcdbf471417a201046cb1d0ac7dc11dd0f680d9ab4e32be05d83ee12b3

Observation b671efc4-b48c-4af8-a11c-b06ce82ffb6a · outbound

This paper cites I D ocٙ\^9;g=;;3g DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD suDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD䘘 TDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD &&.

Expand Neurons, Not Parameters I D ocٙ\^9;g=;;3g DDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD suDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD䘘 TDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDDD &&

Reference 40

Resolution
malformed identifier
no resolver link, observed 2026-08-04T11:33:18.712887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T11:33:18.712887Z digest=sha256:7ac0f484ed327107948e6f38a1b524870c90afc5283503ea5001543a03ace16c

Pith citing papers

No inbound Pith citation observations are available.