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Paper Citation Record · LEDGER

Compress Any Segment Anything Model (SAM)

As of 8 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2507.08765.

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

pith.paper-citation-record.v1
2507.08765 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:20:01.135021Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

  • verified exact3
  • verified fuzzy15
  • unresolved19
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b38b576-c9f9-4232-b6ec-665a428b5c0a · outbound

This paper cites Segment anything,.

Compress Any Segment Anything Model (SAM) Segment anything,

Reference 1

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no resolver link, observed 2026-08-06T18:20:00.905628Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:00.905628Z digest=sha256:2aec4dcfc2c3ad5597a2ad9eabbb46decec351431fa4c55120f16138bbdfcb63

Observation 7290ac4e-7934-40f6-953a-9caf96713376 · outbound

This paper cites MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM.

Compress Any Segment Anything Model (SAM) MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM

Reference 2

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source=pdf_text observed=2026-08-06T18:20:00.911194Z digest=sha256:ee37e309fbaae8a2ca4b8a97fc943de03a0983b87d419b71a7cb6d57ef2e661c

Observation 088e3ba1-acae-4532-8fbb-3ec3b33a6170 · outbound

This paper cites Segment anything in high quality,.

Compress Any Segment Anything Model (SAM) Segment anything in high quality,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-06T18:20:01.820763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.916618Z digest=sha256:111233b19a6d1bc1a2d0d0ec5db97edc5bcffafd6fff9c27743ba4eeec86da61

Observation a058d4da-82a0-456f-a638-7b63f7dcdfd6 · outbound

This paper cites MobileSAMv2: Faster Segment Anything to Everything.

Compress Any Segment Anything Model (SAM) MobileSAMv2: Faster Segment Anything to Everything

Reference 4

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source=pdf_text observed=2026-08-06T18:20:00.924093Z digest=sha256:aab175d4f119383396b1cb15e1fde78c0ef10d1301bf2cd27577c8fd31c29a60

Observation 3b02a9c1-e73a-47b9-b9a8-2e4143658b47 · outbound

This paper cites SAM 2: Segment Anything in Images and Videos.

Compress Any Segment Anything Model (SAM) SAM 2: Segment Anything in Images and Videos

Reference 5

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:00.931060Z digest=sha256:d7c4b7056b76ed0d144973b66d1294ea2a4c5cf1aeda85fa235bb44502bdc9de

Observation 6a8b9a16-ab44-4497-9e82-d1d90006f5f2 · outbound

This paper cites EdgeSAM: Prompt-In-the-Loop Distillation for SAM.

Compress Any Segment Anything Model (SAM) EdgeSAM: Prompt-In-the-Loop Distillation for SAM

Reference 6

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source=pdf_text observed=2026-08-06T18:20:00.943870Z digest=sha256:a35641a8520ec2c6677f7ef278d04497e7282a3bf992837f49379da5793e3bab

Observation 8826e435-889a-4457-abe9-d7eb2310ae3b · outbound

This paper cites Efficientsam: Leveraged masked image pretraining for efficient segment anything,.

Compress Any Segment Anything Model (SAM) Efficientsam: Leveraged masked image pretraining for efficient segment anything,

Reference 7

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raw_fallback, observed 2026-08-06T18:20:01.804497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.951707Z digest=sha256:599909fd44132c49a5cf52a7109c3d9be9889e1991541d936de10bdaf9e500d9

Observation 1e7b2e09-99b3-45ef-ae5d-002e05d4f97c · outbound

This paper cites Tinysam: Pushing the envelope for efficient segment anything model,.

Compress Any Segment Anything Model (SAM) Tinysam: Pushing the envelope for efficient segment anything model,

Reference 8

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.962006Z digest=sha256:a344500b10433538f3f6469228fe3ef4605d85e19c07ce415c56f5ff2c37b6eb

Observation f86dbcae-1b4e-445f-8e6e-3f849018729c · outbound

This paper cites Segment anything in medical images,.

Compress Any Segment Anything Model (SAM) Segment anything in medical images,

Reference 9

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source=pdf_text observed=2026-08-06T18:20:00.968151Z digest=sha256:667b0b090dc9bf5d250c1484cd4f4d428bb1a876b8656e53ed469443c698cd2f

Observation 7cde8883-c7cc-4001-a2f8-af7f4ed3c8a4 · outbound

This paper cites An empir- ical study of catastrophic forgetting in large language models during continual fine-tuning,.

Compress Any Segment Anything Model (SAM) An empir- ical study of catastrophic forgetting in large language models during continual fine-tuning,

Reference 10

Resolution
verified exact
raw_fallback, observed 2026-08-06T18:20:01.468268Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.971632Z digest=sha256:a23150530a4521d7d853350b520bbd2666e2389f7a339ff10d99817e1d1872a1

Observation 68ee955b-6ebe-4121-bbe0-7fa8c7719933 · outbound

This paper cites A survey on model compression for large language models,.

Compress Any Segment Anything Model (SAM) A survey on model compression for large language models,

Reference 11

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.979029Z digest=sha256:7980dc9c24546210054848ee482571441dfae78645abc3904a4bd35969671a66

Observation c9768f39-bc30-47dd-b71b-a79f94097d03 · outbound

This paper cites Model compression for deep neural networks: A survey,.

Compress Any Segment Anything Model (SAM) Model compression for deep neural networks: A survey,

Reference 12

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.983591Z digest=sha256:dc8de69ded13c5ee1147bbd09fcd905efd6928e0713c9f6a33d09adbf1179bc8

Observation 4709eb13-f5af-453c-8fdc-8bf401c368ad · outbound

This paper cites Hyper-compression: Model compression via hyperfunction,.

Compress Any Segment Anything Model (SAM) Hyper-compression: Model compression via hyperfunction,

Reference 13

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verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.987310Z digest=sha256:28f07906efd74084b1a799a702fa48e915ce2b5b43ff38e6a96a758fc6bd39c2

Observation 392d0f39-2fac-473a-9dd0-9ae6b222bdc0 · outbound

This paper cites Microsoft coco: Common objects in context,.

Compress Any Segment Anything Model (SAM) Microsoft coco: Common objects in context,

Reference 14

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:00.991117Z digest=sha256:4750bf64a9f2404d21ff52259884a092129c3de53fa390787a7badc8359a857f

Observation 55d66ed7-262d-466d-a5ce-c21a25d94be6 · outbound

This paper cites Lvis: A dataset for large vocabulary instance segmentation,.

Compress Any Segment Anything Model (SAM) Lvis: A dataset for large vocabulary instance segmentation,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:01.731708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.994753Z digest=sha256:c104983c25fa040f70b8676228e935037c02f35cfa1314129811b8544e09c29b

Observation a7e2e2c9-2db7-45f1-a162-24fa290efbfb · outbound

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

Compress Any Segment Anything Model (SAM) Sparsegpt: Massive language models can be accurately pruned in one-shot,

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:01.717545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:00.998426Z digest=sha256:7a75a79cd719b890a9479a47aab78d70d175006425fc4cb97fbd55f50c7f08bc

Observation 2206c33f-bf82-46a3-8000-9f14f329172d · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow,.

Compress Any Segment Anything Model (SAM) Pruning neural networks without any data by iteratively conserving synaptic flow,

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:01.008284Z digest=sha256:aad2c819ef92b52052266b2aeb1242b1c9f41b75317a655a03b1ceaf95af0d28

Observation 02d2a591-d8d3-4235-8fe6-95a5e41e45c2 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks,.

Compress Any Segment Anything Model (SAM) The lottery ticket hypothesis: Finding sparse, trainable neural networks,

Reference 18

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source=pdf_text observed=2026-08-06T18:20:01.014383Z digest=sha256:072931791c6d22e51326055f9d66697a8da4e6d7d7a93eb685a767efe4927cde

Observation 9d2a95b6-d6ef-4d7c-be95-f122268d9f79 · outbound

This paper cites AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction.

Compress Any Segment Anything Model (SAM) AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction

Reference 19

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:20:01.287341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.020855Z digest=sha256:6482b26e5cef1ab8fd63137355edf954d416eda853e4ca69bdc2d939190886a9

Observation 5876c83d-ada5-4daa-9847-d99eefed92db · outbound

This paper cites BiLLM: Pushing the Limit of Post-Training Quantization for LLMs.

Compress Any Segment Anything Model (SAM) BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 20

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source=pdf_text observed=2026-08-06T18:20:01.035710Z digest=sha256:97e2f4b6b8212b7a4bec5d605b0dfc6e0b7aa91f8ef7b2342b68ccfd412e51eb

Observation 1f6326bb-1c54-4468-a449-e8334b8b23fc · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference,.

Compress Any Segment Anything Model (SAM) Quantization and training of neural networks for efficient integer-arithmetic-only inference,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:01.676153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.043310Z digest=sha256:5468e707169a966c2c789b73b9fece0794cd90d6220e6c9046c5c7a64f48875f

Observation b3b3f99f-06db-4ab7-a298-b7d592d8ec7a · outbound

This paper cites Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation.

Compress Any Segment Anything Model (SAM) Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation

Reference 22

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T18:20:01.054268Z digest=sha256:7d2024fd8670b35fe2a1e89441e64a35f5dda3cac230815e311504266d4d22b6

Observation 5eed493c-504c-45ad-ad9a-c7e3c29c74e6 · outbound

This paper cites Low-bit quantiza- tion of neural networks for efficient inference,.

Compress Any Segment Anything Model (SAM) Low-bit quantiza- tion of neural networks for efficient inference,

Reference 23

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verified fuzzy
raw_fallback, observed 2026-08-06T18:20:01.664565Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.061223Z digest=sha256:0815de1e917f1bbed23247e10d4bfe538a0399ef0ab36d4e70147dda17bdab22

Observation fa441994-60f0-4354-b808-291ac2c7ae64 · outbound

This paper cites Zeroq: A novel zero shot quantization framework,.

Compress Any Segment Anything Model (SAM) Zeroq: A novel zero shot quantization framework,

Reference 24

Resolution
verified fuzzy
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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.068113Z digest=sha256:97c1ab0911ee5c498cd183578185a2a83f87be885e8610cb250b7a7698f143f3

Observation 46fe9380-95c6-4e1b-b6fb-42998ff258ca · outbound

This paper cites Tensor-train decomposition,.

Compress Any Segment Anything Model (SAM) Tensor-train decomposition,

Reference 25

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.072396Z digest=sha256:5f1782f76e3e329cfdd28aa91cd41e1bb084c55639c43f1585d6c6e371329138

Observation 7c0b7b17-ae73-4fd8-b8cb-4db1cfb69e45 · outbound

This paper cites Tensor Ring Decomposition.

Compress Any Segment Anything Model (SAM) Tensor Ring Decomposition

Reference 26

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:01.077415Z digest=sha256:787a1a609e23b142dcf32a8d4c7f00680981192e30be0bc8f9c39a618940346c

Observation c9feef32-523d-41e2-8d48-216a813d3df6 · outbound

This paper cites Data-free Weight Compress and Denoise for Large Language Models.

Compress Any Segment Anything Model (SAM) Data-free Weight Compress and Denoise for Large Language Models

Reference 27

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:01.082405Z digest=sha256:306b48478bc4327b7b1e5bae2c61c60c0969f784f85b621710741a1fa4160636

Observation b0722066-8566-4aea-8f39-bfbfff077365 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Compress Any Segment Anything Model (SAM) Distilling the Knowledge in a Neural Network

Reference 28

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no resolver link, observed 2026-08-06T18:20:01.086655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:01.086655Z digest=sha256:8b5e8895e315568d2a101150ee29c8a0c8dda52214900c0703bd3ccbea0f3fd2

Observation 5971c516-2150-45cb-8bbc-db871cc69197 · outbound

This paper cites Faster Segment Anything: Towards Lightweight SAM for Mobile Applications.

Compress Any Segment Anything Model (SAM) Faster Segment Anything: Towards Lightweight SAM for Mobile Applications

Reference 29

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no resolver link, observed 2026-08-06T18:20:01.091074Z

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:01.091074Z digest=sha256:1b4f08ecd13dd1b9f344b6b6f48714ced7f5b0552af5716c0f2484208af7033b

Observation 6a995ba9-e428-453e-8b41-3e8b3369e7f0 · outbound

This paper cites Ptq4sam: Post-training quantization for segment anything,.

Compress Any Segment Anything Model (SAM) Ptq4sam: Post-training quantization for segment anything,

Reference 30

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:01.629112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.096585Z digest=sha256:89a4c5d25efc3ca5812480c79152a8c2858251cfcf0321628da3039be8a53a8b

Observation d1abb24c-638a-4fd5-8a43-13d4bfe58538 · outbound

This paper cites an unresolved cited work.

Compress Any Segment Anything Model (SAM) Unresolved cited work

Reference 31

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.101799Z digest=sha256:054f25dbbe6a777d4384ddfda05a0cdbe753b16a2d4cabeeca53b3d6acf61743

Observation 0902caff-a80a-4045-8c0b-43a0e7a8679b · outbound

This paper cites Vector quantization,.

Compress Any Segment Anything Model (SAM) Vector quantization,

Reference 32

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verified fuzzy
raw_fallback, observed 2026-08-06T18:20:01.595960Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.105563Z digest=sha256:6447c85466acdd1dd2b2c84805a0343bbba808bc4664d8e803cdde5db3e3da9a

Observation f9313f3f-3547-4483-9261-2d551fef9c30 · outbound

This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

Compress Any Segment Anything Model (SAM) QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 33

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no resolver link, observed 2026-08-06T18:20:01.113405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:01.113405Z digest=sha256:6ebeb9187a6b699e3076e3ded5749ef8646abd27110484a6a0a3fa5b4c3775a1

Observation 97ad7334-a29c-4f8b-b580-6e5d62673f1e · outbound

This paper cites Exploring plain vision transformer backbones for object detection,.

Compress Any Segment Anything Model (SAM) Exploring plain vision transformer backbones for object detection,

Reference 34

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raw_fallback, observed 2026-08-06T18:20:01.574698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.118220Z digest=sha256:7ea3efdf60658ffc1912e4fab928a40ad8d61ef13c33e9ae77d17ff762a1ce39

Observation 2caba76f-b815-4115-9953-5f23af9e2718 · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

Compress Any Segment Anything Model (SAM) YOLOX: Exceeding YOLO Series in 2021

Reference 35

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no resolver link, observed 2026-08-06T18:20:01.123198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:20:01.123198Z digest=sha256:a520c89e8b0bd1197fc86a695700f095920fd3e849064aefc21cd5b05fb0c397

Observation 966bca85-63d2-4646-8515-567e0786d2dc · outbound

This paper cites Up or down? adaptive rounding for post-training quantization,.

Compress Any Segment Anything Model (SAM) Up or down? adaptive rounding for post-training quantization,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T18:20:01.549267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-06T18:20:01.127556Z digest=sha256:70f1e5aa8010397c161d164f5eed6c2e80934310100c5d68065281f3e3b5a2b8

Observation e113b409-bfdf-4e43-b994-4cbf424d735f · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

Compress Any Segment Anything Model (SAM) BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 37

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no resolver link, observed 2026-08-06T18:20:01.135021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Pith citing papers

No inbound Pith citation observations are available.