Paper Citation Record · LEDGER

Irrational Complex Rotations Empower Low-bit Optimizers

As of 11 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2501.12896.

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

pith.paper-citation-record.v1
2501.12896 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:49:47.284760Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

41 of 41 outbound references displayed

verified exact1
verified fuzzy12
unresolved28
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c36c9da5-7541-4479-9056-a298e2acc9a4 · outbound

This paper cites \ TensorFlow \ : a system for \ Large-Scale \ machine learning.

Irrational Complex Rotations Empower Low-bit Optimizers \ TensorFlow \ : a system for \ Large-Scale \ machine learning

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:49:47.729395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 0ee1150d-5bee-4785-9ccd-761e30e86605 · outbound

This paper cites Skyformer: Remodel self-attention with gaussian kernel and nystr " om method.

Irrational Complex Rotations Empower Low-bit Optimizers Skyformer: Remodel self-attention with gaussian kernel and nystr " om method

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:49:47.719364Z

Source-reported events for the cited work

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Observation 50c0ea28-aee6-4b0e-ba33-7fbc71df44f7 · outbound

This paper cites 8-Bit Approximations for Parallelism in Deep Learning.

Irrational Complex Rotations Empower Low-bit Optimizers 8-Bit Approximations for Parallelism in Deep Learning

Reference 3

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Observation c80adc21-bcf1-4a73-b946-aa352a7a2fa0 · outbound

This paper cites 8-bit Optimizers via Block-wise Quantization.

Irrational Complex Rotations Empower Low-bit Optimizers 8-bit Optimizers via Block-wise Quantization

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T16:49:47.067643Z

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Observation 6c5f3d3e-e13b-4cf3-9c91-4fb2f8d94c58 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers Unresolved cited work

Reference 5

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Observation 17bf84dd-0099-4990-9f21-1eaf52a667ee · outbound

This paper cites SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression.

Irrational Complex Rotations Empower Low-bit Optimizers SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T16:49:47.074635Z

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Observation 4c964c70-5102-49fd-8d68-f0cc7ee50f91 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Irrational Complex Rotations Empower Low-bit Optimizers GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T16:49:47.078604Z

Source-reported events for the cited work

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Observation b95a7e3d-af0d-4388-8db2-c113c29f665f · outbound

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

Irrational Complex Rotations Empower Low-bit Optimizers A framework for few-shot language model evaluation, 07 2024

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-10T16:49:47.082184Z

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Observation a425909a-bbad-4936-b8b4-e556a54fa832 · outbound

This paper cites W., and Keutzer, K.

Irrational Complex Rotations Empower Low-bit Optimizers W., and Keutzer, K

Reference 9

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Observation 719612fc-d438-4703-8bfe-274152037d9e · outbound

This paper cites SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization.

Irrational Complex Rotations Empower Low-bit Optimizers SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T16:49:47.088799Z

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Observation 8dac304a-f5fb-4dc0-ac0d-6b9d6dcc3f6c · outbound

This paper cites Aptq: Attention-aware post-training mixed-precision quantization for large language models.

Irrational Complex Rotations Empower Low-bit Optimizers Aptq: Attention-aware post-training mixed-precision quantization for large language models

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:49:47.696087Z

Source-reported events for the cited work

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Observation 29c314d7-f3bb-4cde-bd5a-9d3ae089cded · outbound

This paper cites an unresolved cited work.

Irrational Complex Rotations Empower Low-bit Optimizers Unresolved cited work

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T16:49:47.095658Z

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Observation ad51d18d-0857-4ca0-8376-b7bc398b6e90 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Irrational Complex Rotations Empower Low-bit Optimizers Adam: A Method for Stochastic Optimization

Reference 13

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Observation c54901fc-6ad6-4c77-b7e9-d161fb90256e · outbound

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

Irrational Complex Rotations Empower Low-bit Optimizers Learning multiple layers of features from tiny images

Reference 14

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Observation a78c37ec-40b1-4477-81d8-c7f8cf41059f · outbound

This paper cites Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models.

Irrational Complex Rotations Empower Low-bit Optimizers Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models

Reference 15

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Source-reported events for the cited work

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Observation 267aeeef-c7c6-4695-845b-624372e9a66c · outbound

This paper cites Memory efficient optimizers with 4-bit states.

Irrational Complex Rotations Empower Low-bit Optimizers Memory efficient optimizers with 4-bit states

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:49:47.662152Z

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Observation dbfd8628-68a7-4813-8674-b088e00b987e · outbound

This paper cites AnglE-optimized Text Embeddings.

Irrational Complex Rotations Empower Low-bit Optimizers AnglE-optimized Text Embeddings

Reference 17

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Observation e04f3e49-8426-442c-b385-10a5816a25e0 · outbound

This paper cites Awq: Activation-aware weight quantization for on-device llm compression and acceleration.

Irrational Complex Rotations Empower Low-bit Optimizers Awq: Activation-aware weight quantization for on-device llm compression and acceleration

Reference 18

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Observation ac3cf5fe-7be6-4d46-85b3-f59d572e307f · outbound

This paper cites Learning long-range spatial dependencies with horizontal gated-recurrent units.

Irrational Complex Rotations Empower Low-bit Optimizers Learning long-range spatial dependencies with horizontal gated-recurrent units

Reference 19

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Source-reported events for the cited work

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Observation 63bfe16e-de10-4561-b318-48c9c462e878 · outbound

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

Irrational Complex Rotations Empower Low-bit Optimizers LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 20

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Observation 96f420f0-3473-435b-b9b4-645fbe18f6d0 · outbound

This paper cites L., Daly, R.

Irrational Complex Rotations Empower Low-bit Optimizers L., Daly, R

Reference 21

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Source-reported events for the cited work

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Observation e45d53c5-99e0-48f3-a384-05033de4f70d · outbound

This paper cites ListOps: A Diagnostic Dataset for Latent Tree Learning.

Irrational Complex Rotations Empower Low-bit Optimizers ListOps: A Diagnostic Dataset for Latent Tree Learning

Reference 22

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Observation c1720b75-ddd7-4ad4-ae85-9b3cf1533efd · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library.

Irrational Complex Rotations Empower Low-bit Optimizers Pytorch: An imperative style, high-performance deep learning library

Reference 23

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Observation c04bae98-406c-4e2c-b66b-103283e9682e · outbound

This paper cites YaRN: Efficient Context Window Extension of Large Language Models.

Irrational Complex Rotations Empower Low-bit Optimizers YaRN: Efficient Context Window Extension of Large Language Models

Reference 24

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Observation 215d5fd6-d4c6-41b5-b14a-b5be6e52bc94 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers R., Muthukrishnan, P., and Qazvinian, V

Reference 25

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Source-reported events for the cited work

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Observation a62a06e7-7b32-4898-9e6f-8555730aaee2 · outbound

This paper cites W., Potapenko, A., Jayakumar, S.

Irrational Complex Rotations Empower Low-bit Optimizers W., Potapenko, A., Jayakumar, S

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:49:47.625259Z

Source-reported events for the cited work

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Observation d3cabd82-d94d-4719-9f0b-aa309a5a0252 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers PB-LLM: Partially Binarized Large Language Models

Reference 27

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Observation 691d5a50-b898-42ce-8d4c-fd9159fc278b · outbound

This paper cites Roformer: Enhanced transformer with rotary position embedding.

Irrational Complex Rotations Empower Low-bit Optimizers Roformer: Enhanced transformer with rotary position embedding

Reference 28

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Observation 76a82207-3832-4412-974e-002f1418bbe3 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers A Length-Extrapolatable Transformer

Reference 29

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Observation eb6db8a9-17aa-4585-820b-58becb9a90e1 · outbound

This paper cites An image patch is a wave: Phase-aware vision mlp.

Irrational Complex Rotations Empower Low-bit Optimizers An image patch is a wave: Phase-aware vision mlp

Reference 30

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Observation 08f83682-8b09-4981-9d4b-7a470dc55239 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers Long Range Arena: A Benchmark for Efficient Transformers

Reference 31

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Observation 28817403-4522-4d66-a105-0b57aa3c6aa4 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers X., Wen, J.-R., and Cao, Z

Reference 32

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Observation 2a9fc1e5-6329-45ee-866c-ac6b5b5dee70 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers X., and Wen, J.-R

Reference 33

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Observation e794efcf-1038-416f-9ccc-37fbe9f363f0 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers X., Zhang, C., Zhao, X., Ma, Z., and Wen, J.-R

Reference 34

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Observation 8af5a93f-b59d-42f7-a8db-d8eff37d95fa · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers Z., et al

Reference 35

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Observation 9801e0f9-f370-4ab9-8502-2ca936aed9eb · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

Irrational Complex Rotations Empower Low-bit Optimizers BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 36

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Observation 0796a154-294b-4085-a5c9-9cef67dab397 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 37

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Observation fffde624-2ae4-4c3c-914d-c9ee24045ee9 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers Zeroquant: Efficient and affordable post-training quantization for large-scale transformers

Reference 38

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Observation effcc37d-04b3-499c-8b52-5de0b3811170 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers TinyLlama: An Open-Source Small Language Model

Reference 39

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Irrational Complex Rotations Empower Low-bit Optimizers A Survey of Large Language Models

Reference 40

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Observation e7d19610-b522-4178-b012-6fcbdeea0179 · outbound

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Irrational Complex Rotations Empower Low-bit Optimizers write newline

Reference 41

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Pith citing papers

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