Pith. sign in

Paper Citation Record · LEDGER

Understanding and Overcoming the Challenges of Efficient Transformer Quantization

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2109.12948.

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

pith.paper-citation-record.v1
2109.12948 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:55:14.109364Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-02T12:36:56.029956Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 45a3d574-8780-4186-9f90-ab8356ad0364 · inbound

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 120

Resolution
verified exact
arxiv_id, observed 2026-05-13T13:35:36.041677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-13T13:35:35.972596Z digest=sha256:2a17e2b17b42827ef2891ec8c5945f858e2181bfb875a7ca1577c044cb0ceccc

Observation e82989ce-b32f-437b-a7e9-e613644e434e · inbound

Massive Activations in Large Language Models cites this paper.

Massive Activations in Large Language Models Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 107

Resolution
verified exact
arxiv_id, observed 2026-05-16T07:02:53.968703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-16T07:02:53.740597Z digest=sha256:55cdad190bc42c95785e71925bbaf87442c6789df4ab0e76fab9bee42f05b5a5

Observation 46096209-e9cd-482a-8745-83417ccf525d · inbound

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness cites this paper.

Post-Training Quantization of Generative and Discriminative LSTM Text Classifiers: A Study of Calibration, Class Balance, and Robustness Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-06T17:55:14.109364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:55:14.109364Z digest=sha256:84c4f30f3bca8338c6f78cb8057724451b2aff03b1e47d40bc78ed0432b6cfb3

Observation 49ff8c98-eafd-4f81-bb6b-602d93bc7237 · inbound

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation cites this paper.

I-Segmenter: Integer-Only Vision Transformer for Efficient Semantic Segmentation Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-04T17:57:09.346300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:57:09.346300Z digest=sha256:6b78a2611a38d613cf8775c22344b2cbb43c2536b106ccd984978164de0d3b18

Observation 8653d2c7-9cb7-43bd-8f0d-526ea9e400b7 · inbound

A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models cites this paper.

A Comprehensive FP8 Training Recipe for Reasoning-Enhanced Language Models Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-04T14:51:28.491836Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T14:51:28.491836Z digest=sha256:4438d0d461efa85f9c232e7a9c00ab8b243cfa4497717c954b1ed547941d90b1

Observation 4d9a76ec-d298-49ca-97e6-b9bcb050be85 · inbound

MUXQ: Mixed-to-Uniform Precision MatriX Quantization via Low-Rank Outlier Decomposition cites this paper.

MUXQ: Mixed-to-Uniform Precision MatriX Quantization via Low-Rank Outlier Decomposition Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:30:50.803740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T19:06:16.532479Z digest=sha256:8e69263fc9ab3426a88bfe8bca95167a0662edef108b045c0d4d6e023fd8308b

Observation d3029044-f5a8-4af6-aa63-fc8efe78c83b · inbound

$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic Space cites this paper.

$\text{Log}_\text{b}$Quant: Quantizing Language Models in Logarithmic Space Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 6

Resolution
verified exact
arxiv_id, observed 2026-07-02T12:36:56.031421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-07-02T12:35:58.613973Z digest=sha256:78fbb71acaf7578fae1c017742d34ea67007f9f959bdee7d8edc166755f08d1e

Observation c1bb3eca-1d65-4534-ab2a-c7672dd1587e · inbound

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference cites this paper.

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference Understanding and Overcoming the Challenges of Efficient Transformer Quantization

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-01T17:12:27.428735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T17:12:27.428735Z digest=sha256:85fd686de3c96cbca06c3646082580944e00032656717ace92bb0fe587d118bd