Paper Citation Record · LEDGER

Differentiable Model Compression via Pseudo Quantization Noise

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2104.09987.

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

pith.paper-citation-record.v1
2104.09987 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T12:09:57.899034Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T19:16:00.122550Z

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 d2f365cf-8685-4d37-8948-dc02ef26d0e7 · inbound

High Fidelity Neural Audio Compression cites this paper.

High Fidelity Neural Audio Compression Differentiable Model Compression via Pseudo Quantization Noise

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-13T21:49:52.277417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f3fad53e-445b-4241-89fc-8796cdd25ec0 · inbound

Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs cites this paper.

Gradual Binary Search and Dimension Expansion : A general method for activation quantization in LLMs Differentiable Model Compression via Pseudo Quantization Noise

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T12:09:57.899034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6cb5fac-382e-4848-a08a-d79d49983cce · inbound

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training cites this paper.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Differentiable Model Compression via Pseudo Quantization Noise

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-15T21:03:32.720775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ccee1b2-44c4-4c9a-a0bb-7ee525d54b1f · inbound

CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training cites this paper.

CAGE: Curvature-Aware Gradient Estimation For Accurate Quantization-Aware Training Differentiable Model Compression via Pseudo Quantization Noise

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T08:50:45.091961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea09cb5f-2df7-495a-a03f-f9ec7d7ff182 · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling Differentiable Model Compression via Pseudo Quantization Noise

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:36:02.316175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c34b37f4-874c-418a-8c40-d32b8c445576 · inbound

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling cites this paper.

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling Differentiable Model Compression via Pseudo Quantization Noise

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:02:42.186980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3807a7fe-4c4b-482b-b92c-07a35c1083dc · inbound

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not cites this paper.

Quantized Reasoning Models Think They Need to Think Longer, but They Do Not Differentiable Model Compression via Pseudo Quantization Noise

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-01T19:16:00.123933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.