Paper Citation Record · LEDGER

Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2306.12929.

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

pith.paper-citation-record.v1
2306.12929 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 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 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:30:37.736038Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

11
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 4d39c079-9da9-4fc0-9d7d-bea441bbf1b8 · inbound

Massive Activations in Large Language Models cites this paper.

Massive Activations in Large Language Models Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 108

Resolution: verified exact; arxiv_id, observed 2026-05-16T07:02:53.971737Z

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.

Observation 0081ecd2-ce4c-4844-b8fd-ca56779c8cfb · inbound

Rethinking the Outlier Distribution in Large Language Models: An In-depth Study cites this paper.

Rethinking the Outlier Distribution in Large Language Models: An In-depth Study Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T13:30:37.736038Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7a78b1e-bcb8-4804-99bd-ffe86490b644 · inbound

FPTQuant: Function-Preserving Transforms for LLM Quantization cites this paper.

FPTQuant: Function-Preserving Transforms for LLM Quantization Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:43:43.611046Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97d43d9b-5efc-445c-b887-8668c52fb786 · inbound

On the Mathematical Impossibility of Safe Universal Approximators cites this paper.

On the Mathematical Impossibility of Safe Universal Approximators Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T20:41:54.104960Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81b3be1d-0219-4789-8cdd-03019ac48af3 · inbound

Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling cites this paper.

Four Over Six: More Accurate NVFP4 Quantization with Adaptive Block Scaling Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:23:52.675264Z

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.

Observation 8fc71837-9e23-4202-864d-ff34f593ba80 · inbound

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse cites this paper.

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-16T08:47:37.167558Z

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.

Observation a14c9409-2a34-4b7f-a3c9-3c23ae7a10ea · inbound

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse cites this paper.

Attention Sink Forges Native MoE in Attention Layers: Sink-Aware Training to Address Head Collapse Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-03T05:50:22.066445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae71c389-0771-4836-b3d8-1164e0e794f3 · inbound

Efficient Reasoning on the Edge cites this paper.

Efficient Reasoning on the Edge Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 121

Resolution: unresolved; no resolver link, observed 2026-07-13T23:28:12.790404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8748b626-7867-4533-8e98-906fb766a7f3 · inbound

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cites this paper.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:32:46.734785Z

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.

Observation 93d94ed7-f69f-4464-ab16-93cc34900542 · inbound

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers cites this paper.

Contribution Weights: A Geometrical Analysis of Self-Attention Transformers Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 146

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:32:47.285286Z

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.

Observation 59500c8e-b13d-475a-b6aa-defc26e634df · inbound

Sumi: Open Uniform Diffusion Language Model from Scratch cites this paper.

Sumi: Open Uniform Diffusion Language Model from Scratch Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-04T01:09:19.759555Z

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.

Observation 71ba6eee-d019-44c3-bf16-e5565592380e · inbound

Massive Activations Are Architecturally Robust: A Controlled Scratch/Commitment Residual Stream Test cites this paper.

Massive Activations Are Architecturally Robust: A Controlled Scratch/Commitment Residual Stream Test Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:59:20.505120Z

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.

Observation 08f32ffe-9fa9-4d8a-be0c-107e8ed041d5 · inbound

Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement cites this paper.

Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-02T03:22:12.687854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73249661-4f80-41e3-b20e-1ae17010694a · inbound

LISA: Linear-Indexed Sparse Attention for Efficient Long-Context Reasoning cites this paper.

LISA: Linear-Indexed Sparse Attention for Efficient Long-Context Reasoning Quantizable Transformers: Removing Outliers by Helping Attention Heads Do Nothing

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-02T12:48:53.848185Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.