Paper Citation Record · LEDGER

BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2102.10462.

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

pith.paper-citation-record.v1
2102.10462 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T10:26:45.528383Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T12:28:16.825530Z

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 4b6c4bfa-8f5c-47dc-a14a-529e2f8848f6 · inbound

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits cites this paper.

LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-08T10:26:45.528383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 744569d2-cc5e-4c48-813c-de21f8ce02aa · inbound

MSQ: Memory-Efficient Bit Sparsification Quantization cites this paper.

MSQ: Memory-Efficient Bit Sparsification Quantization BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T11:56:59.840476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5152e8ec-9aa5-4317-afd1-5cc89cccb23c · inbound

Principled Approximation Methods for Efficient and Scalable Deep Learning cites this paper.

Principled Approximation Methods for Efficient and Scalable Deep Learning BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-05T13:57:36.490033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29317db9-25e8-482b-b93c-4315196e1aa6 · inbound

STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training cites this paper.

STQuant: Spatio-Temporal Adaptive Framework for Optimizer Quantization in Large Multimodal Model Training BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:50:57.088595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 91bb1591-8cde-4df9-9a84-cf7e147d652d · inbound

GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets cites this paper.

GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:28:16.827194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c81511d2-9f27-4415-abd4-86b898219107 · inbound

TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models cites this paper.

TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models BSQ: Exploring Bit-Level Sparsity for Mixed-Precision Neural Network Quantization

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-08T01:04:45.875286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.