Paper Citation Record · LEDGER

Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2406.17415.

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

pith.paper-citation-record.v1
2406.17415 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 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 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T04:25:21.937482Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T15:57:06.548416Z

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 21ec9fd3-ee4f-49dc-a081-6b568a350763 · inbound

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models cites this paper.

Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-16T12:40:54.792220Z

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 95472c66-1c38-422f-93ad-121ff81417d4 · inbound

Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models cites this paper.

Do All Individual Layers Help? An Empirical Study of Task-Interfering Layers in Vision-Language Models Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-21T14:50:15.318790Z

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 aaa40f48-48c6-425a-b85b-6e4ebb43c074 · inbound

When Does Sparsity Mitigate the Curse of Depth in LLMs cites this paper.

When Does Sparsity Mitigate the Curse of Depth in LLMs Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-14T20:29:33.439034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d625e96-cec0-4548-8c99-7989d0e513d3 · inbound

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss cites this paper.

LAQuant: A Simple Overhead-free Large Reasoning Model Quantization by Layer-wise Lookahead Loss Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:16:29.946471Z

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 c19c535a-1d17-4799-9544-479f826b44d7 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T06:06:28.049662Z

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 67b43205-1147-4570-829a-8b896385b308 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T04:59:46.262831Z

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 d61ca676-a50a-45af-8d22-b0e94e5e67c6 · inbound

Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization cites this paper.

Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T15:57:06.550151Z

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 259bf201-80c0-476e-899d-e9594bea13e0 · inbound

ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM cites this paper.

ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T01:37:46.946786Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e9496c6-8a74-4112-b12c-3270a2bd04ef · inbound

MixQuant: Adaptive Mixed-Precision Quantization for Large Language Models cites this paper.

MixQuant: Adaptive Mixed-Precision Quantization for Large Language Models Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 141

Resolution: unresolved; no resolver link, observed 2026-08-01T03:51:32.782753Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38db4177-8821-4f83-8232-550eff86fdb1 · inbound

Studying quantization trade-offs for efficient inference deployment in machine translation cites this paper.

Studying quantization trade-offs for efficient inference deployment in machine translation Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T07:51:19.938021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efddbf83-b70d-4851-91f2-487e58417a44 · inbound

Studying quantization trade-offs for efficient inference deployment in machine translation cites this paper.

Studying quantization trade-offs for efficient inference deployment in machine translation Layer-Wise Quantization: A Pragmatic and Effective Method for Quantizing LLMs Beyond Integer Bit-Levels

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T04:25:21.937482Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.