MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization | Paper Citation Record

Observation 5e23fc2d-5212-4b40-8ae1-584000dc5beb · outbound

This paper cites Llama 3 model card.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Llama 3 model card

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.428754Z

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Observation 2314d70a-af98-4cb8-a855-191a77c93bd6 · outbound

This paper cites A pid controller approach for stochastic optimization of deep networks.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization A pid controller approach for stochastic optimization of deep networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.430623Z

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Observation b3a0eef5-718c-41e2-8242-d45f4b65cf6c · outbound

This paper cites Quarot: Outlier-free 4-bit inference in rotated llms.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Quarot: Outlier-free 4-bit inference in rotated llms

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.427049Z

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Observation 5eb3791e-8a8e-4040-bbce-71e4a464964c · outbound

This paper cites PIQA: Reasoning about physical commonsense in natural language.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization PIQA: Reasoning about physical commonsense in natural language

Reference 4

Resolution: verified exact; doi, observed 2026-05-20T12:58:17.508926Z

Source-reported events for the cited work

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Observation d42e02bb-3043-449b-82f9-ebe753fa80b8 · outbound

This paper cites PID Control-Based Self-Healing to Improve the Robustness of Large Language Models.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization PID Control-Based Self-Healing to Improve the Robustness of Large Language Models

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:58:17.756074Z

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Observation 5623fd9b-d973-4431-a404-c35a09d6a3a2 · outbound

This paper cites an unresolved cited work.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-05-20T12:58:18.454596Z

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Observation 29262097-b4cf-47d6-92a3-1ba94498735b · outbound

This paper cites B ool Q : Exploring the Surprising Difficulty of Natural Yes/No Questions.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization B ool Q : Exploring the Surprising Difficulty of Natural Yes/No Questions

Reference 7

Resolution: verified exact; doi, observed 2026-05-20T12:58:17.503138Z

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Observation e3b3c9ca-827e-4fe2-9394-8da7a27453a9 · outbound

This paper cites Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:58:17.759079Z

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Observation e84ad82b-944e-4006-82c4-0ba1916ba47e · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:58:17.751899Z

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Observation abf7c485-52df-4c68-8b36-bf1c0b65f993 · outbound

This paper cites Optimal brain compression: A framework for accurate post-training quantization and pruning.Advances in Neural Information Processing Systems, 35:4475–4488.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Optimal brain compression: A framework for accurate post-training quantization and pruning.Advances in Neural Information Processing Systems, 35:4475–4488

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.432784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6c5996bb-ba67-45bc-bdbd-621fe705e684 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:58:17.768349Z

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Observation 73775a1e-3e24-4c66-a7df-007700d5a8c9 · outbound

This paper cites Pushing the limit of post-training quantization.IEEE Transactions on Pattern Analysis and Machine Intelligence.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Pushing the limit of post-training quantization.IEEE Transactions on Pattern Analysis and Machine Intelligence

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.437162Z

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Observation 34d22181-1ab6-4e48-ba6b-d1c5df953790 · outbound

This paper cites Knowledge distillation: A survey.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Knowledge distillation: A survey

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.439053Z

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Observation 3f247221-c1ed-48ba-8ca2-840e6aefba1a · outbound

This paper cites Optimal brain surgeon and general network pruning.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Optimal brain surgeon and general network pruning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.450717Z

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Observation 48179e1d-75e6-488f-b921-f7e7f901787b · outbound

This paper cites Mb-taylorformer v2: Improved multi-branch linear transformer expanded by taylor formula for image restoration.IEEE Transactions on Pattern Analysis and Machine Intelligence.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Mb-taylorformer v2: Improved multi-branch linear transformer expanded by taylor formula for image restoration.IEEE Transactions on Pattern Analysis and Machine Intelligence

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.446857Z

Source-reported events for the cited work

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Observation 5d0af67b-3a30-4807-8a54-3e4b8597e6cd · outbound

This paper cites Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Owq: Outlier-aware weight quantization for efficient fine-tuning and inference of large language models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.452868Z

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Observation 5116b8df-976d-46e2-93ff-3125cb07332f · outbound

This paper cites Rethinking Residual Errors in Compensation-based LLM Quantization.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Rethinking Residual Errors in Compensation-based LLM Quantization

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:58:17.774104Z

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Observation ed23cfc9-80c5-4b50-b812-3cf219f8499c · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:58:17.793673Z

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Observation 2b0a8e15-181d-486d-9aff-68e278f833b0 · outbound

This paper cites GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization GPTAQ: Efficient Finetuning-Free Quantization for Asymmetric Calibration

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:58:17.782611Z

Source-reported events for the cited work

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Observation e29c620b-4519-4a39-99f9-a4bff840fc21 · outbound

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

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Repq-vit: Scale reparameterization for post- training quantization of vision transformers

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.471625Z

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Observation 94bce6eb-38bb-4225-9c2b-653bb845995e · outbound

This paper cites Lightweight deep learning for resource-constrained environments: A survey.ACM Computing Surveys, 56(10):1–42.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Lightweight deep learning for resource-constrained environments: A survey.ACM Computing Surveys, 56(10):1–42

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.473506Z

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Observation d241124c-6c77-482f-a11a-e77cc63a4737 · outbound

This paper cites Low-bit Model Quantization for Deep Neural Networks: A Survey.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Low-bit Model Quantization for Deep Neural Networks: A Survey

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:58:17.777018Z

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Observation a154513e-13b7-4b59-b26a-e50e45ae674d · outbound

This paper cites Swin transformer: Hierarchical vision transformer using shifted windows.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Swin transformer: Hierarchical vision transformer using shifted windows

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.467803Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6dcb3097-e125-4c1b-9c8c-0ad798ae27bc · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization SpinQuant: LLM quantization with learned rotations

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:58:17.764929Z

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Observation 526e03ff-6925-4153-a669-2c49b631071e · outbound

This paper cites an unresolved cited work.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Unresolved cited work

Reference 25

Resolution: unresolved; raw_fallback, observed 2026-05-20T12:58:18.460011Z

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Observation 2317e166-c1d2-4f14-8751-0a3d3e8924b2 · outbound

This paper cites Meta pid attention network for flexible and efficient real-world noisy image denoising.IEEE Transactions on Image Processing, 31:2053–2066.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Meta pid attention network for flexible and efficient real-world noisy image denoising.IEEE Transactions on Image Processing, 31:2053–2066

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.469742Z

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Observation 42b0cbb7-d798-40c1-9e71-dfe5f3d8f928 · outbound

This paper cites Pointer Sentinel Mixture Models.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Pointer Sentinel Mixture Models

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:58:17.790762Z

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Observation 5025ebf4-dd69-473d-9672-16c1806de925 · outbound

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

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Up or down? adaptive rounding for post-training quantization

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.442799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f0d27acc-5663-4c71-83c8-470004c4800d · outbound

This paper cites Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for image dehazing.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Mb-taylorformer: Multi-branch efficient transformer expanded by taylor formula for image dehazing

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.444848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6449b9e5-bc47-484e-b246-3da6dc2f796a · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.479584Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1af070ae-20eb-47e5-b381-386491c62a11 · outbound

This paper cites Imagenet large scale visual recognition challenge.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Imagenet large scale visual recognition challenge

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.481442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b960424b-52f1-4d50-a7da-2eb77c5d7207 · outbound

This paper cites WinoGrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization WinoGrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106

Reference 32

Resolution: malformed identifier; raw_fallback, observed 2026-05-20T12:58:18.440933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8168c6ff-68bd-480f-aa48-cd9e9a9dd439 · outbound

This paper cites Globally optimal policy gradient algorithms for reinforcement learning with pid control policies.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Globally optimal policy gradient algorithms for reinforcement learning with pid control policies

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.477236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 12e3f5fd-ff5e-4919-abba-ee6264253070 · outbound

This paper cites A simple and effective pruning approach for large language models.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization A simple and effective pruning approach for large language models

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.448915Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b96b17fe-c99e-411d-b7a6-c40b18cbc28b · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Training data-efficient image transformers & distillation through attention

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.435193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 70cc7d35-0d01-48d6-b353-0af6e31efe63 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 36

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:58:17.788002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 391be1b4-f984-48d2-b99c-d145c673b518 · outbound

This paper cites QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization QDrop: Randomly Dropping Quantization for Extremely Low-bit Post-Training Quantization

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:58:17.771436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2497c24e-cf05-4225-b5af-6a8a2c98398d · outbound

This paper cites Adalog: Post-training quantization for vision transformers with adaptive logarithm quantizer.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Adalog: Post-training quantization for vision transformers with adaptive logarithm quantizer

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.456481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a6f44942-7f71-423c-b022-59e7cb148ef3 · outbound

This paper cites Fima-q: Post-training quantization for vision transformers by fisher information matrix approximation.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Fima-q: Post-training quantization for vision transformers by fisher information matrix approximation

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.475291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5c8f891c-1472-45e6-8984-c3828a776e03 · outbound

This paper cites Smoothquant: Accurate and efficient post-training quantization for large language models.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Smoothquant: Accurate and efficient post-training quantization for large language models

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.486677Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 082e53a2-1a6e-46f1-8d68-74048a375594 · outbound

This paper cites Qwen3 Technical Report.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Qwen3 Technical Report

Reference 41

Resolution: verified exact; local_arxiv, observed 2026-05-20T12:58:17.779815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3988bb82-32d0-4ce5-b050-a9e4da11b075 · outbound

This paper cites Self-supervised graph neural networks via low-rank decomposition.Advances in Neural Information Processing Systems, 36:34295–34307.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Self-supervised graph neural networks via low-rank decomposition.Advances in Neural Information Processing Systems, 36:34295–34307

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.461913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6b88339a-32df-478a-8720-9154d0569373 · outbound

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

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Ptq4vit: Post-training quantization for vision transformers with twin uniform quantization

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.465642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 49ad8247-3a57-4366-880b-b5ab0fd5e807 · outbound

This paper cites URL https:// doi.org/10.18653/v1/p19-1472.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization URL https:// doi.org/10.18653/v1/p19-1472

Reference 44

Resolution: verified exact; doi, observed 2026-05-20T12:58:17.506077Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 880b6185-d326-4001-ba20-ec3d344a0ed8 · outbound

This paper cites Decoupled knowledge distillation.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Decoupled knowledge distillation

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.458270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3da51741-7bf4-4666-aeb6-754beeffc9a6 · outbound

This paper cites First-order error matters: Accurate compensation for quantized large language models.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization First-order error matters: Accurate compensation for quantized large language models

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-20T12:58:18.463771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae691778-6fca-41da-99c4-3cfcc6e8bcc0 · outbound

This paper cites I&s-vit: An inclusive & stable method for pushing the limit of post-training vits quantization.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization I&s-vit: An inclusive & stable method for pushing the limit of post-training vits quantization

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-20T12:58:17.762173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 6f0022de-fc24-470f-8eb1-20ce931a7e95 · outbound

This paper cites Towards accurate post-training quantization of vision transformers via error reduction.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(4):2676–2692.

MARR: Module-Adaptive Residual Reconstruction for Low-Bit Post-Training Quantization Towards accurate post-training quantization of vision transformers via error reduction.IEEE Transactions on Pattern Analysis and Machine Intelligence, 47(4):2676–2692

Reference 48

Resolution: malformed identifier; arxiv_id, observed 2026-05-20T12:58:17.785413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers