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Paper Citation Record · LEDGER

On the Expressive Power of Weight Quantization in Large Language Models

As of 22 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2606.22249.

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

pith.paper-citation-record.v1
2606.22249 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-26T11:53:45.787243Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

48 of 48 outbound references displayed

  • verified exact21
  • verified fuzzy0
  • unresolved27
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0e271f9e-50d0-4af5-a17b-57f97b8d4d37 · outbound

This paper cites Feed-Forward Neural Networks as a Mixed-Integer Program.

On the Expressive Power of Weight Quantization in Large Language Models Feed-Forward Neural Networks as a Mixed-Integer Program

Reference 1

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arxiv_id, observed 2026-07-04T08:19:44.263876Z

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Observation a54dd770-5431-473f-bbb8-3529846b265a · outbound

This paper cites Llama 3 model card.

On the Expressive Power of Weight Quantization in Large Language Models Llama 3 model card

Reference 2

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Observation 6d5cf53f-2d53-4d4f-8f3a-f257680c1f7b · outbound

This paper cites The High-Dimensional Geometry of Binary Neural Networks.

On the Expressive Power of Weight Quantization in Large Language Models The High-Dimensional Geometry of Binary Neural Networks

Reference 3

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local_arxiv, observed 2026-07-04T08:19:44.230870Z

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Observation d26275e2-b4eb-46ac-9aa8-74a6dabbb1e7 · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 4

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source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:34d1fd65935bc00d6b88e40cbe4afa6bdb5fe07e5987e2bb46de970eef0a303d

Observation 4d449f83-ee96-45a3-8a48-7395b4a43b84 · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 5

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source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:1e53c3e78767604b0509b2fb35c54c8d8be58dec7d08eca39133c3d105aa99b2

Observation 3749d258-689c-4f8c-8404-86bd06619706 · outbound

This paper cites Chatterjee and L.

On the Expressive Power of Weight Quantization in Large Language Models Chatterjee and L

Reference 6

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Observation 899af368-c482-4e2e-b0e4-7bdf79aa79e7 · outbound

This paper cites Efficient Ternary Weight Embedding Model: Bridging Scalability and Performance.

On the Expressive Power of Weight Quantization in Large Language Models Efficient Ternary Weight Embedding Model: Bridging Scalability and Performance

Reference 7

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arxiv_id, observed 2026-07-04T08:19:44.255237Z

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Observation 16567312-93c0-4f17-beac-fde61d522096 · outbound

This paper cites Cheng, T.

On the Expressive Power of Weight Quantization in Large Language Models Cheng, T

Reference 8

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Observation 5ecce3d5-e3bf-4e00-bdf3-dbf5e95e6620 · outbound

This paper cites BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions.

On the Expressive Power of Weight Quantization in Large Language Models BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 9

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Observation 9fe9faea-fc0d-4ae9-b494-a391343da4f1 · outbound

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

On the Expressive Power of Weight Quantization in Large Language Models Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 10

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local_arxiv, observed 2026-07-04T08:19:44.274922Z

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Observation 7fef6751-6aca-4b96-a3c1-ac3431472dfb · outbound

This paper cites Courbariaux, Y.

On the Expressive Power of Weight Quantization in Large Language Models Courbariaux, Y

Reference 11

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Observation 36f18ed3-7b6f-4c26-bc45-77d66d6b45f1 · outbound

This paper cites Courbariaux, Y.

On the Expressive Power of Weight Quantization in Large Language Models Courbariaux, Y

Reference 12

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Observation 80ed45bb-e4f9-46c0-a58b-5b04196f6926 · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 13

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Observation ed4e206b-1e2c-413a-8994-63f955198ac4 · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 14

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Observation 77947d34-ba98-4087-93dd-ceac11290e4e · outbound

This paper cites A Survey of Quantization Methods for Efficient Neural Network Inference.

On the Expressive Power of Weight Quantization in Large Language Models A Survey of Quantization Methods for Efficient Neural Network Inference

Reference 15

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arxiv_id, observed 2026-07-04T08:19:44.272097Z

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Observation 02588e55-9fd7-4b5f-91a8-9098f509d5d1 · outbound

This paper cites Gonon, N.

On the Expressive Power of Weight Quantization in Large Language Models Gonon, N

Reference 16

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Observation f004310e-226c-47f8-b03f-d9cf0f7764bf · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 17

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Observation 050503bd-13f0-4104-bfac-2d5ee821281e · outbound

This paper cites A Survey on Methods and Theories of Quantized Neural Networks.

On the Expressive Power of Weight Quantization in Large Language Models A Survey on Methods and Theories of Quantized Neural Networks

Reference 18

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local_arxiv, observed 2026-07-04T08:19:44.223609Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation c6e8a90c-ce4d-4e7f-8451-5cf7b360558a · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 19

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Observation 1ff95029-73d6-4d5c-a4e3-283fc0e896b1 · outbound

This paper cites Hornik, M.

On the Expressive Power of Weight Quantization in Large Language Models Hornik, M

Reference 20

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Observation f02bf457-647d-4dd9-af20-23e871867cbb · outbound

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On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 21

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Observation 3e204325-71e2-4c23-b4ad-8f4ce42b9563 · outbound

This paper cites SqueezeNext: Hardware-Aware Neural Network Design.

On the Expressive Power of Weight Quantization in Large Language Models SqueezeNext: Hardware-Aware Neural Network Design

Reference 22

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arxiv_id, observed 2026-07-04T08:19:44.240814Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation e36ae29e-df9b-45d4-a06e-b512fbcb3a9c · outbound

This paper cites Kidger and T.

On the Expressive Power of Weight Quantization in Large Language Models Kidger and T

Reference 23

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Observation fe4ff7cd-ee66-4af0-a606-50be94046415 · outbound

This paper cites Ternary Weight Networks.

On the Expressive Power of Weight Quantization in Large Language Models Ternary Weight Networks

Reference 24

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arxiv_id, observed 2026-07-04T08:19:44.233326Z

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Observation 187a58d0-fae1-44f3-b0ab-f5fad76ae35f · outbound

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On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 25

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Observation 7c9714d8-5af6-46b3-8cb6-4e0d3cca6ce8 · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

On the Expressive Power of Weight Quantization in Large Language Models LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 26

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arxiv_id, observed 2026-07-04T08:19:44.249942Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation 7fe7958a-4095-445b-8d0d-335f29c4f7db · outbound

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On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 27

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Observation 90b80846-5eed-4b96-b114-e8fd575cd095 · outbound

This paper cites Decoupled Weight Decay Regularization.

On the Expressive Power of Weight Quantization in Large Language Models Decoupled Weight Decay Regularization

Reference 28

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local_arxiv, observed 2026-07-04T08:19:44.238240Z

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Observation 1cd36aae-01a8-494d-892b-2e82291d14f6 · outbound

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On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 29

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Observation 47772374-55fd-4580-80a3-3d1f7446500b · outbound

This paper cites BitNet b1.58 2B4T Technical Report.

On the Expressive Power of Weight Quantization in Large Language Models BitNet b1.58 2B4T Technical Report

Reference 30

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arxiv_id, observed 2026-07-04T08:19:44.226259Z

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Observation f57dbb86-96c3-4656-81a9-26739e71a56d · outbound

This paper cites Pointer Sentinel Mixture Models.

On the Expressive Power of Weight Quantization in Large Language Models Pointer Sentinel Mixture Models

Reference 31

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local_arxiv, observed 2026-07-04T08:19:44.266628Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation fbbbe300-1f5c-4683-ac00-fd80974959d8 · outbound

This paper cites Mertens and A.

On the Expressive Power of Weight Quantization in Large Language Models Mertens and A

Reference 32

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Observation ff6e8f0d-b195-4b1e-b832-a157c11abba1 · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

On the Expressive Power of Weight Quantization in Large Language Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 33

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local_arxiv, observed 2026-07-04T08:19:44.221076Z

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Observation 15fb61d1-308b-4cc1-aa01-cebb11a8ea88 · outbound

This paper cites Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens.

On the Expressive Power of Weight Quantization in Large Language Models Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens

Reference 34

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arxiv_id, observed 2026-07-04T08:19:44.243529Z

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Observation 8a03c482-e816-4bd3-aa73-a4147383255f · outbound

This paper cites Sakaguchi, R.

On the Expressive Power of Weight Quantization in Large Language Models Sakaguchi, R

Reference 35

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Observation 45e5dd1b-4e88-4052-8ce8-0dd9f066bc6f · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

On the Expressive Power of Weight Quantization in Large Language Models SocialIQA: Commonsense Reasoning about Social Interactions

Reference 36

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local_arxiv, observed 2026-07-04T08:19:44.228528Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation ef68e3e1-26fc-4a76-955e-4508fd7b56b8 · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 37

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source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:950e0f8239c716548e1e76d707fcc10585a4e98c589a4b82381b14e59ea7766a

Observation 5ff50adf-eba7-4f6e-8dbe-1516f0eefa0d · outbound

This paper cites Szegedy, V.

On the Expressive Power of Weight Quantization in Large Language Models Szegedy, V

Reference 38

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Unavailable: canonical work link unavailable.

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Observation 8eb5187d-17e9-4869-ac5c-0c22bb79402d · outbound

This paper cites Vaswani, N.

On the Expressive Power of Weight Quantization in Large Language Models Vaswani, N

Reference 39

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source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:c050072ba4708c5e39426fe77811ff0b7414fa5fa9e58f384889ea890b7ebe9e

Observation f5e44dd5-6223-47a1-b24e-ac5322fa7334 · outbound

This paper cites Voigtlaender.

On the Expressive Power of Weight Quantization in Large Language Models Voigtlaender

Reference 40

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Observation cc6fd2e5-59e8-46a6-82b8-e9fe53d76d4e · outbound

This paper cites Bitnet distillation.arXiv preprint arXiv:2510.13998, 2025.

On the Expressive Power of Weight Quantization in Large Language Models Bitnet distillation.arXiv preprint arXiv:2510.13998, 2025

Reference 41

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verified exact
arxiv_id, observed 2026-07-04T08:19:44.252565Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Observation dbb6e93e-96d8-41b0-b2c4-f3ca46222aa9 · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 42

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unresolved
no resolver link, observed 2026-06-26T11:53:45.787243Z

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source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:56854595a33ff9f69e1adfee9797528f07a8ca803773ed4588e9c24b2931ba07

Observation a06f545f-b4e5-46bb-8aed-33ee6f4a5e25 · outbound

This paper cites Universal Approximation Theorems of Fully Connected Binarized Neural Networks.

On the Expressive Power of Weight Quantization in Large Language Models Universal Approximation Theorems of Fully Connected Binarized Neural Networks

Reference 43

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verified exact
arxiv_id, observed 2026-07-04T08:19:44.258016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:0a8b97bb6cb5b5ce0ebc6c0c5f646649523e8222f4061318850bfef8f584c388

Observation f5e06067-afb9-4417-9337-93718717745d · outbound

This paper cites an unresolved cited work.

On the Expressive Power of Weight Quantization in Large Language Models Unresolved cited work

Reference 44

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no resolver link, observed 2026-06-26T11:53:45.787243Z

Source-reported events for the cited work

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source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:e1e72136a4418110e8186e26dec5ef7183b3dc046ff1f7f7572cb90151863d33

Observation 68a3ace8-852b-408e-9561-6cc0635c6b1b · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

On the Expressive Power of Weight Quantization in Large Language Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 45

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verified exact
local_arxiv, observed 2026-07-04T08:19:44.235777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:9915d2f71e53c1efbf7f88feceacbd0df4d08fdc20cf05fe4c6c5560de019313

Observation 5700b27d-1c9d-4259-9c09-2ceb0d9b45c8 · outbound

This paper cites TernaryCLIP: Efficiently compressing vision-language models with ternary weights and distilled knowledge.

On the Expressive Power of Weight Quantization in Large Language Models TernaryCLIP: Efficiently compressing vision-language models with ternary weights and distilled knowledge

Reference 46

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verified exact
arxiv_id, observed 2026-07-04T08:19:44.260813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:79e0386e072777588b59acbb2364627035079ef8b12df7861080a4a67b9d7fae

Observation e4e65ffa-f404-4534-95a7-bbf458bb2681 · outbound

This paper cites Zhang and Z.-H.

On the Expressive Power of Weight Quantization in Large Language Models Zhang and Z.-H

Reference 47

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unresolved
no resolver link, observed 2026-06-26T11:53:45.787243Z

Source-reported events for the cited work

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source=pdf_text observed=2026-06-26T11:53:45.787243Z digest=sha256:56bd53d74353040b778ace16b3765eb17102a6ad7531575089917834ff0172bd

Observation 6f78f110-8059-4e9b-aeea-4979af43776c · outbound

This paper cites Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights.

On the Expressive Power of Weight Quantization in Large Language Models Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights

Reference 48

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verified exact
local_arxiv, observed 2026-07-04T08:19:44.269324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

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Pith citing papers

No inbound Pith citation observations are available.