Paper Citation Record · LEDGER

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers

As of 21 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2502.01770.

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

pith.paper-citation-record.v1
2502.01770 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:38:49.937475Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+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

37 of 37 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b419006d-16a3-4c9b-afff-7893d0332736 · outbound

This paper cites The groq software-defined scale-out tensor streaming multipro- cessor: From chips-to-systems architectural overview.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers The groq software-defined scale-out tensor streaming multipro- cessor: From chips-to-systems architectural overview

Reference 1

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Observation ae8bf404-f7de-435d-8646-bb8240fda371 · outbound

This paper cites Vivit: A video vision transformer.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Vivit: A video vision transformer

Reference 2

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Observation acea6c6d-1f90-4dcb-a543-e1ea593118f8 · outbound

This paper cites Longformer: The Long-Document Transformer.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Longformer: The Long-Document Transformer

Reference 3

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This paper cites Prime: A novel processing-in-memory architecture for neural net- work computation in reram-based main memory.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Prime: A novel processing-in-memory architecture for neural net- work computation in reram-based main memory

Reference 4

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Observation e3025021-94c1-4ca1-a82f-0ae969d232f6 · outbound

This paper cites On a model of associative memory with huge storage capacity.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers On a model of associative memory with huge storage capacity

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:38:50.495954Z

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Observation fbbc6e7a-07a2-462a-b65e-7f01f8e25bc6 · outbound

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

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Imagenet: A large-scale hierarchical im- age database

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:38:50.485968Z

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Observation f8830fe7-2255-458e-b758-5955e30abd1d · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 7

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Observation 16af9084-2018-4824-9596-3b991fcba02b · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Observation 81bce930-33a2-4d9f-8423-e1af50bfcb83 · outbound

This paper cites Array multiplier using xnor.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Array multiplier using xnor

Reference 9

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Observation 4968c933-7ede-4ef1-aa50-12bb47aee446 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 10

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Observation 8717f85c-2bab-4f52-bd26-00d9b1b0779a · outbound

This paper cites Sparse co-attention visual question answering networks based on thresholds.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Sparse co-attention visual question answering networks based on thresholds

Reference 11

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Observation c03eb002-a4b6-480d-a948-1d151862ad9c · outbound

This paper cites Bivit: Ex- tremely compressed binary vision transformers.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Bivit: Ex- tremely compressed binary vision transformers

Reference 12

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Observation d2588fec-12a9-41f7-b23b-b3ccd450ae75 · outbound

This paper cites Binarized neural net- works.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Binarized neural net- works

Reference 13

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Observation c2da33d0-4378-41ee-9ef0-1a27d49c5da5 · outbound

This paper cites In-datacenter performance analysis of a tensor process- ing unit.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers In-datacenter performance analysis of a tensor process- ing unit

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:38:50.432867Z

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Observation 739ad5b8-32c2-4791-8ae2-1575d658db63 · outbound

This paper cites Scaling Laws for Neural Language Models.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Scaling Laws for Neural Language Models

Reference 15

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Observation 31f9d1e8-1f92-4f49-9c24-6adbec35840b · outbound

This paper cites Reformer: The Efficient Transformer.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Reformer: The Efficient Transformer

Reference 16

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Observation 01440fe2-66d2-4c8d-8b0b-8dd749c24c58 · outbound

This paper cites Self-Binarizing Networks.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Self-Binarizing Networks

Reference 17

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Observation d31963a7-fa17-4619-9d2e-b0ac05b836d2 · outbound

This paper cites RACE: Large-scale ReAding Comprehension Dataset From Examinations.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers RACE: Large-scale ReAding Comprehension Dataset From Examinations

Reference 18

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This paper cites Power and area-efficient xnor-and hybrid binary neural networks using tft-type synaptic devices.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Power and area-efficient xnor-and hybrid binary neural networks using tft-type synaptic devices

Reference 19

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Observation a344935e-b50f-4b82-815b-c1b2ec237ce4 · outbound

This paper cites Fp-bnn: Binarized neural network on fpga.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Fp-bnn: Binarized neural network on fpga

Reference 20

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Observation f0c4a837-bee6-40f8-926e-ec8c506c48f9 · outbound

This paper cites Cerebras architecture deep dive: First look inside the hw/sw co-design for deep learning: Cerebras systems.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Cerebras architecture deep dive: First look inside the hw/sw co-design for deep learning: Cerebras systems

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:38:50.398540Z

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Observation b689a940-afd1-4966-977d-1132fb5afa68 · outbound

This paper cites Transformer Acceleration with Dynamic Sparse Attention.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Transformer Acceleration with Dynamic Sparse Attention

Reference 22

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Observation 5f80d868-7fa4-4504-885d-44af9c5f09f6 · outbound

This paper cites Bit: Robustly binarized multi-distilled transformer.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Bit: Robustly binarized multi-distilled transformer

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:38:50.365203Z

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Observation b1c5a520-2fca-40e2-a5d4-c1cb7f057de6 · outbound

This paper cites QuALITY: Question Answering with Long Input Texts, Yes!.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers QuALITY: Question Answering with Long Input Texts, Yes!

Reference 24

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Observation 41e878c0-ae5d-433c-b1fd-01237ade3d11 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 25

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Observation 0bc6496e-fd33-4345-bfa3-2af72836a718 · outbound

This paper cites Hopfield Networks is All You Need.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Hopfield Networks is All You Need

Reference 26

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Observation 8abd4172-985e-40b9-ad56-dde7503936e2 · outbound

This paper cites Xnor-net: Imagenet classifica- tion using binary convolutional neural networks.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Xnor-net: Imagenet classifica- tion using binary convolutional neural networks

Reference 27

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Observation df7b73f3-5567-4649-832f-f625e79e74de · outbound

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Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers XNOR-Net: Imagenet classifica- tion using binary convolutional neural networks

Reference 28

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Observation 345b58c1-29c7-4575-bdcd-2b25e26a056e · outbound

This paper cites Efficient sparse-dense matrix-matrix multiplication on gpus us- ing the customized sparse storage format.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Efficient sparse-dense matrix-matrix multiplication on gpus us- ing the customized sparse storage format

Reference 29

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Observation e358aeb3-d645-4693-bc82-1871e48d7010 · outbound

This paper cites Sextans: A streaming accelerator for general-purpose sparse-matrix dense-matrix multiplication.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Sextans: A streaming accelerator for general-purpose sparse-matrix dense-matrix multiplication

Reference 30

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Observation 4eaa9b5f-5e40-4dee-940b-b7f313ec6eaa · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Training data-efficient image transformers & distillation through attention

Reference 31

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation db93b213-b915-442a-8390-616bee382c13 · outbound

This paper cites Attention is all you need.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Attention is all you need

Reference 32

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Observation c3ad2eac-9e10-4e3f-a17e-7b7b4312b389 · outbound

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Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Audio Transformers

Reference 33

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Observation 688012e6-1b89-461a-b44b-fd1ec63f74e3 · outbound

This paper cites Linformer: Self-Attention with Linear Complexity.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Linformer: Self-Attention with Linear Complexity

Reference 34

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This paper cites OneBit: Towards Extremely Low-bit Large Language Models.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers OneBit: Towards Extremely Low-bit Large Language Models

Reference 35

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Observation 8ca2a554-0c2e-40b9-bcd7-7142221b8e17 · outbound

This paper cites Pb- llm: Partially binarized large language models.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Pb- llm: Partially binarized large language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:38:50.139820Z

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Observation 45272873-b124-4ba1-9150-da2ebbe02e7c · outbound

This paper cites Big bird: Transformers for longer se- quences.

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers Big bird: Transformers for longer se- quences

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T14:38:50.127043Z

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Pith citing papers

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