Paper Citation Record · LEDGER

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models

As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2607.11359.

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

pith.paper-citation-record.v1
2607.11359 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T06:58:40.928481Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

35 of 35 outbound references displayed

verified exact0
verified fuzzy0
unresolved35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 4a1963c6-15ae-4bc7-99f6-e11086f4e7f7 · outbound

This paper cites Brecq: Pushing the limit of post-training quantization by block reconstruction,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Brecq: Pushing the limit of post-training quantization by block reconstruction,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:37.458282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4447d91a-7631-4295-ade0-95d7dce94bc9 · outbound

This paper cites Learned step size quantization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Learned step size quantization,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:37.527165Z

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Observation 3d54d086-7066-4155-aeed-a8179e11b535 · outbound

This paper cites Up or down? adaptive rounding for post-training quantization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Up or down? adaptive rounding for post-training quantization,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:37.616049Z

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Observation a815b5a1-7bc5-4d57-bd77-54ec2cab3a3a · outbound

This paper cites Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Qdrop: Randomly dropping quantization for extremely low-bit post-training quantization,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:37.706055Z

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Observation 50a06214-1c86-4a40-bf5f-db02ec0e4691 · outbound

This paper cites Flatquant: Flatness matters for llm quantization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Flatquant: Flatness matters for llm quantization,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:37.852716Z

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Observation 5e220cf3-2975-4372-8ed6-f7a0f1bba037 · outbound

This paper cites Lsq+: Improving low-bit quantization through learnable offsets and better initialization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Lsq+: Improving low-bit quantization through learnable offsets and better initialization,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:37.996908Z

Source-reported events for the cited work

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Observation 5ee9ebed-cbc8-4c33-bbfe-7d9a492832ab · outbound

This paper cites Nipq: Noise proxy-based integrated pseudo-quantization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Nipq: Noise proxy-based integrated pseudo-quantization,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.075132Z

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Observation c1104e3f-233e-49df-96cd-8eefade3b11f · outbound

This paper cites Quan- tization meets ood: Generalizable quantization-aware training from a flatness perspective,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Quan- tization meets ood: Generalizable quantization-aware training from a flatness perspective,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.131587Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81528dcf-60cb-4165-8c32-dc368737d6b1 · outbound

This paper cites Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.171290Z

Source-reported events for the cited work

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Observation 3409d350-9e97-4abb-891b-4d13f7420c1f · outbound

This paper cites PD-Quant: Post-Training Quantization Based on Prediction Difference Metric,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models PD-Quant: Post-Training Quantization Based on Prediction Difference Metric,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.273121Z

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Observation 4509bde5-561a-40a4-857e-8babadcda3e2 · outbound

This paper cites Erq: error reduction for post-training quantization of vision transformers,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Erq: error reduction for post-training quantization of vision transformers,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.381313Z

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Observation ee9b95b5-f7f0-49a4-aff0-4e2acfb7015e · outbound

This paper cites Quantization without tears,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Quantization without tears,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.519493Z

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Observation 5121cb71-5343-490c-b173-1186b3822049 · outbound

This paper cites Data- free quantization through weight equalization and bias correction,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Data- free quantization through weight equalization and bias correction,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.633613Z

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Unavailable: canonical work link unavailable.

Observation b596f307-cf74-4377-8b9b-8553d917ef0f · outbound

This paper cites Zeroq: A novel zero shot quantization framework,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Zeroq: A novel zero shot quantization framework,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.744016Z

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Observation b07e8bbb-f71e-44f4-895d-a825e44d9552 · outbound

This paper cites Solving oscillation problem in post-training quantization through a theoretical perspective,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Solving oscillation problem in post-training quantization through a theoretical perspective,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.851926Z

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Observation 754b1706-1906-4b13-87fc-190bfc65508e · outbound

This paper cites Quantization error propagation: Revisiting layer-wise post-training quantization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Quantization error propagation: Revisiting layer-wise post-training quantization,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:38.970407Z

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Observation d9930fbd-e5af-4372-8888-c84d2281224d · outbound

This paper cites Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:39.176184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2aa0e8a-fa46-40b3-8538-01f7d0b9e6a0 · outbound

This paper cites Fq- vit: Post-training quantization for fully quantized vision transformer,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Fq- vit: Post-training quantization for fully quantized vision transformer,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:39.281120Z

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Observation 005899b0-bdec-4fc1-b3de-ebf9e12c8460 · outbound

This paper cites Repq-vit: Scale reparameterization for post-training quantization of vision transformers,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Repq-vit: Scale reparameterization for post-training quantization of vision transformers,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:39.411176Z

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Observation 607d3c14-ee84-4d07-8b57-fc347f7a7171 · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Averaging Weights Leads to Wider Optima and Better Generalization

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:39.502554Z

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Observation 4973a14f-9678-42cc-a657-6b64b3e5fca9 · outbound

This paper cites Sharpness-aware minimization for efficiently improving generalization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Sharpness-aware minimization for efficiently improving generalization,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:39.630361Z

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Observation eb5529c3-6ddd-4701-ae67-8748e5a9a43b · outbound

This paper cites Anticorre- lated noise injection for improved generalization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Anticorre- lated noise injection for improved generalization,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:39.749741Z

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Observation 94548a3f-8998-4e04-8e11-0db7a3581005 · outbound

This paper cites Bit-shrinking: A quasistatic quantization strategy for post-training quantization,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Bit-shrinking: A quasistatic quantization strategy for post-training quantization,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:39.925233Z

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Observation 33a25619-6197-42b6-83b1-d65904b89e64 · outbound

This paper cites Stabilizing Quantization-Aware Training by Implicit-Regularization on Hessian Matrix.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Stabilizing Quantization-Aware Training by Implicit-Regularization on Hessian Matrix

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.092734Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3b76156-6da2-42cf-acd7-067530887c1c · outbound

This paper cites Learning multiple layers of features from tiny images,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Learning multiple layers of features from tiny images,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.196277Z

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Observation 85f5f8a1-427f-4d5c-8f6a-3c054fb3c276 · outbound

This paper cites Tiny imagenet visual recognition challenge,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Tiny imagenet visual recognition challenge,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.267293Z

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Observation dea28d9e-f811-4ef2-9d1e-33de22b68c2e · outbound

This paper cites Imagenet large scale visual recognition challenge,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Imagenet large scale visual recognition challenge,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.324978Z

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Observation c2d3fb87-c382-448c-939f-6570d9712b09 · outbound

This paper cites Deep residual learning for image recognition,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Deep residual learning for image recognition,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.386189Z

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Observation 4031b30a-87c6-49e0-9410-cbb8d788a46b · outbound

This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.459371Z

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Observation ac2282df-37e2-4a34-94c4-f00951abdd01 · outbound

This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Mobilenetv2: Inverted residuals and linear bottlenecks,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.555366Z

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Observation 80c823ad-196f-4084-b3ca-4da86abe2e01 · outbound

This paper cites Ghostnet: More features from cheap operations,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Ghostnet: More features from cheap operations,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.665308Z

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Observation 6bbe35ff-6666-4db7-a441-2b9ad4e5209e · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models U-net: Convolutional networks for biomedical image segmentation,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.714486Z

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Observation 3fcbafcb-6e28-424d-bad2-6e3db4f553fc · outbound

This paper cites The cityscapes dataset for semantic urban scene understanding,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models The cityscapes dataset for semantic urban scene understanding,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.751221Z

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Observation a4aeabb0-468d-489b-b8f7-72ad19dc0af8 · outbound

This paper cites Pytorch: An imperative style, high-performance deep learning library,.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models Pytorch: An imperative style, high-performance deep learning library,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.787178Z

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Observation 68d124ff-27aa-463a-9aa8-c0b70886a85a · outbound

This paper cites MQBench: Towards Reproducible and Deployable Model Quantization Benchmark.

Efficient Tuning Before Low-Bit Post-Training Quantization for Stochastic Gradient Descent-optimized Models MQBench: Towards Reproducible and Deployable Model Quantization Benchmark

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-02T06:58:40.928481Z

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Pith citing papers

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