Paper Citation Record · LEDGER

Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2406.12016.

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

pith.paper-citation-record.v1
2406.12016 v2

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 2026-08-07T06:34:17.273281+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:47:33.687505Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-14T21:19:29.134491Z

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 20d59e83-e6ab-4eaf-9d29-6d51fb6dc8e4 · inbound

SmoothRot: Combining Channel-Wise Scaling and Rotation for Quantization-Friendly LLMs cites this paper.

SmoothRot: Combining Channel-Wise Scaling and Rotation for Quantization-Friendly LLMs Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:47:33.687505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2bb6b72-cc2a-47c0-9b84-e450ce09f459 · inbound

OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference cites this paper.

OrthoRank: Token Selection via Sink Token Orthogonality for Efficient LLM inference Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T20:08:08.298473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e24cac0b-f4f2-49dc-8d33-b784f73db151 · inbound

Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs cites this paper.

Physics- and geometry-aware spatio-spectral graph neural operator for time-independent and time-dependent PDEs Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T20:59:36.904918Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 494fae0f-631f-4625-b04d-5b7192da51d5 · inbound

Attention's forward pass and Frank-Wolfe cites this paper.

Attention's forward pass and Frank-Wolfe Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-05T21:05:02.757268Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c60cd6f-b44c-42f4-b3d6-f47676773e1f · inbound

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models cites this paper.

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:36:42.363601Z

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.

Observation 2a0b47b8-e58f-4db5-8898-38a289ecc985 · inbound

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models cites this paper.

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models Prefixing Attention Sinks can Mitigate Activation Outliers for Large Language Model Quantization

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:29.136886Z

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.