Paper Citation Record · LEDGER

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective

As of 19 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2507.19131.

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

pith.paper-citation-record.v1
2507.19131 v1

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:05:12.229482Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

35 of 35 outbound references displayed

verified exact0
verified fuzzy25
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 53a0e674-e48f-464f-85ba-ab5d895832a4 · outbound

This paper cites Pymoo: Multi-objective optimization in python.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Pymoo: Multi-objective optimization in python

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.757153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5dd2641d-8ef6-4873-ace2-30c6e90835b8 · outbound

This paper cites Sparsevit: Revisiting activation spar- sity for efficient high-resolution vision transformer.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Sparsevit: Revisiting activation spar- sity for efficient high-resolution vision transformer

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.742170Z

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Observation 33fcd15b-1751-44e7-9b71-e05d0f1c65bd · outbound

This paper cites Masked-attention mask transformer for universal image segmentation.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Masked-attention mask transformer for universal image segmentation

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.728958Z

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Observation 2f42fc45-876b-442f-8cb6-49f3c2d5327e · outbound

This paper cites A fast and elitist multiobjective genetic algo- rithm: Nsga-ii.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective A fast and elitist multiobjective genetic algo- rithm: Nsga-ii

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.716267Z

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Observation e9aa6c58-0395-4eb7-9b3b-701675e54f4e · outbound

This paper cites Emq: Evolving training-free proxies for automated mixed precision quantization.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Emq: Evolving training-free proxies for automated mixed precision quantization

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.702962Z

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Observation 351b0877-19e4-4793-b8b3-def5c5185f76 · outbound

This paper cites Hawq: Hessian aware quantization of neural networks with mixed-precision.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Hawq: Hessian aware quantization of neural networks with mixed-precision

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.692631Z

Source-reported events for the cited work

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Observation d95107b5-1ff8-488c-8692-f0e816e0500f · outbound

This paper cites Hawq-v2: Hessian aware trace-weighted quantization of neural networks.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Hawq-v2: Hessian aware trace-weighted quantization of neural networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T18:05:12.115373Z

Source-reported events for the cited work

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Observation 3f487afb-47c1-4cae-b407-7cf38199c64a · outbound

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

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 8

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Source-reported events for the cited work

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Observation 2b283179-b413-4e97-8f47-990a5f4de5bf · outbound

This paper cites Adaptive token sampling for efficient vision transformers.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Adaptive token sampling for efficient vision transformers

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.674531Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6bf54d1f-9004-4af8-9756-fcf7c3e5eb20 · outbound

This paper cites Jumping through local minima: Quantization in the loss landscape of vision transformers.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Jumping through local minima: Quantization in the loss landscape of vision transformers

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.663255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9c701f70-c9b6-4eb9-803c-dda3f8daffee · outbound

This paper cites Mask r-cnn.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Mask r-cnn

Reference 11

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2fc22fd6-5a8f-44a3-b541-5aaceeb2cfad · outbound

This paper cites Metamix: Meta-state precision searcher for mixed-precision activation quantization.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Metamix: Meta-state precision searcher for mixed-precision activation quantization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.641484Z

Source-reported events for the cited work

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Observation 2101f4b1-13bd-4662-99cd-d4029b85aa73 · outbound

This paper cites Spvit: Enabling faster vision transformers via latency-aware soft token pruning.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Spvit: Enabling faster vision transformers via latency-aware soft token pruning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.626236Z

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Observation cbbe04d5-64af-48f9-997f-2903f5d9d7f0 · outbound

This paper cites Q-vit: Accurate and fully quantized low-bit vision transformer.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Q-vit: Accurate and fully quantized low-bit vision transformer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.610554Z

Source-reported events for the cited work

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Observation ca2fbc0f-e764-4341-badd-4c925d5e7090 · outbound

This paper cites Repq- vit: Scale reparameterization for post-training quantization of vision transformers.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Repq- vit: Scale reparameterization for post-training quantization of vision transformers

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.596770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9e74e16b-7f40-4407-bf36-e6fa0098a6fd · outbound

This paper cites Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T18:05:12.154637Z

Source-reported events for the cited work

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Observation f303cd9a-e01c-47a5-847f-0ca6f7b8023b · outbound

This paper cites Microsoft coco: Common objects in context.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Microsoft coco: Common objects in context

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.581349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 2aff3838-8103-4f99-9cb4-d3c10e39a50a · outbound

This paper cites QLLM: Accurate and efficient low-bitwidth quantization for large language models.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective QLLM: Accurate and efficient low-bitwidth quantization for large language models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.567431Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 01ee41b5-2085-4e52-a885-db0098391753 · outbound

This paper cites Oscillation-free quantization for low-bit vision transform- ers.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Oscillation-free quantization for low-bit vision transform- ers

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.548901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 742422cb-5aa3-41eb-a828-99a4d631f9a0 · outbound

This paper cites Pq-sam: Post-training quantization for segment any- thing model.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Pq-sam: Post-training quantization for segment any- thing model

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T18:05:12.169913Z

Source-reported events for the cited work

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Observation cfaf9e5b-5e19-497b-a1c0-75e6b3eb73bc · outbound

This paper cites Revisiting token pruning for object detection and instance segmentation.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Revisiting token pruning for object detection and instance segmentation

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.526077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5e48c72e-5013-43ae-b34e-1e0022519a08 · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Swin transformer: Hierarchical vision transformer using shifted windows

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.512001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e09ec43c-ebf2-4203-94fe-06f294ead93b · outbound

This paper cites Ompq: Orthogonal mixed precision quantization.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Ompq: Orthogonal mixed precision quantization

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.497016Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c31e0ad0-cd30-40cf-a8ad-09e4a49a33fb · outbound

This paper cites Coco-o: A benchmark for object detectors under natural distribution shifts.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Coco-o: A benchmark for object detectors under natural distribution shifts

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.482762Z

Source-reported events for the cited work

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Observation 8b683eb6-567b-4f78-8f31-1655749b0a4d · outbound

This paper cites Data-free quantization through weight equal- ization and bias correction.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Data-free quantization through weight equal- ization and bias correction

Reference 25

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Source-reported events for the cited work

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Observation b855335c-13d2-4c43-8c5c-eb4aa616a30e · outbound

This paper cites Up or down? adap- tive rounding for post-training quantization.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Up or down? adap- tive rounding for post-training quantization

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-15T18:05:12.190882Z

Source-reported events for the cited work

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Observation 20409b6b-13bf-44b1-af8b-44c9956e21bb · outbound

This paper cites Dynamicvit: Efficient vision transformers with dynamic token sparsification.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Dynamicvit: Efficient vision transformers with dynamic token sparsification

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T18:05:12.194893Z

Source-reported events for the cited work

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Observation b539d472-512a-4c01-ab90-f53df70e8546 · outbound

This paper cites Entropy-driven mixed- precision quantization for deep network design.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Entropy-driven mixed- precision quantization for deep network design

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.442772Z

Source-reported events for the cited work

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Observation aa97e8ab-69c0-4986-8288-6f7cf8551861 · outbound

This paper cites Attention is all you need.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Attention is all you need

Reference 29

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Source-reported events for the cited work

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Observation f148c64b-e43d-44bb-8110-74dbb0124ee2 · outbound

This paper cites Thinking in granularity: Dynamic quantization for image super-resolution by intriguing multi- granularity clues, 2024.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Thinking in granularity: Dynamic quantization for image super-resolution by intriguing multi- granularity clues, 2024

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.409433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 52e04d21-4052-4647-8988-3e950c0bb35e · outbound

This paper cites Apq: Joint search for network architecture, pruning and quantization policy.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Apq: Joint search for network architecture, pruning and quantization policy

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.379435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation cd07a841-db50-4ba1-a1d6-19d0ab79e89f · outbound

This paper cites Patch- wise mixed-precision quantization of vision transformer,.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Patch- wise mixed-precision quantization of vision transformer,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.356363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5b760c8b-a5e1-4a5c-9813-c9c769095e9d · outbound

This paper cites K-net: Towards unified image seg- mentation.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective K-net: Towards unified image seg- mentation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.335845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ff16dac1-72b6-4a98-a3d4-537cb9b94800 · outbound

This paper cites Towards accurate post-training quantization of vision transformers via error reduction.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Towards accurate post-training quantization of vision transformers via error reduction

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T18:05:12.321476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a26b49e5-c43a-4f1a-8bdf-6d6dff8d7374 · outbound

This paper cites an unresolved cited work.

MixA-Q: Revisiting Activation Sparsity for Vision Transformers from a Mixed-Precision Quantization Perspective Unresolved cited work

Reference 35

Resolution: unresolved; raw_fallback, observed 2026-08-15T18:05:12.301186Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

No inbound Pith citation observations are available.