Paper Citation Record · LEDGER

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

As of 23 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 4 inbound Pith citation observations for arXiv:2501.13484.

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

pith.paper-citation-record.v1
2501.13484 v3

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T16:14:46.556506Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T20:34:29.580725Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T15:05:48.313263Z

Reference resolution

46 of 46 outbound references displayed

verified exact1
verified fuzzy14
unresolved31
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7f42593b-d7f5-4888-b9cd-fec118199d59 · outbound

This paper cites Slicegpt: Compress large language models by deleting rows and columns.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Slicegpt: Compress large language models by deleting rows and columns

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.668736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2f4701b4-fdcb-4621-930a-40f55398cacb · outbound

This paper cites QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.317792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bd39e81-fbd5-4d24-a68d-fad3bc0d67d2 · outbound

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

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Piqa: Reasoning about physical commonsense in natural language

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.647124Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b5651004-97c9-448a-9d1a-f47f14a41169 · outbound

This paper cites A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods A Systematic Classification of Knowledge, Reasoning, and Context within the ARC Dataset

Reference 5

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Source-reported events for the cited work

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Observation cfcdb57f-a806-4e00-accf-f31ce7974f1a · outbound

This paper cites Quip: 2-bit quantization of large language models with guarantees.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Quip: 2-bit quantization of large language models with guarantees

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.625010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 03ea1aac-14a8-4190-aeb1-3fccb9d8bf26 · outbound

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

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.339758Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4f8b1d5b-2cdf-4304-a47b-ca8b62476791 · outbound

This paper cites Karhunen-loeve transform.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Karhunen-loeve transform

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.604312Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d30aa538-f4a1-496d-956e-30d696bbc8af · outbound

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

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.349875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e1dde2a-3045-4aae-b96e-f58e1b60782c · outbound

This paper cites Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Model Quantization and Hardware Acceleration for Vision Transformers: A Comprehensive Survey

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.355327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3ef197a-c831-46d0-8d95-27300f031c21 · outbound

This paper cites Unified matrix treatment of the fast walsh-hadamard transform.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Unified matrix treatment of the fast walsh-hadamard transform

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.582556Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ae32f3b4-1dcd-4ff9-acd1-ccf5a2b3a2fe · outbound

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

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.365920Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04ee7fbd-c4f0-4e83-8dc2-0819bd90ba79 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.370947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9fadad4-d86e-4b90-8e3a-decb807b6c80 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Efficiently Modeling Long Sequences with Structured State Spaces

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.376148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f6d2e40-1ea0-4071-927c-229c79d75d7d · outbound

This paper cites I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods I-LLM: Efficient Integer-Only Inference for Fully-Quantized Low-Bit Large Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.381014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc699f2d-f74a-4fc6-8527-123b487e7393 · outbound

This paper cites LocalMamba: Visual State Space Model with Windowed Selective Scan.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods LocalMamba: Visual State Space Model with Windowed Selective Scan

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.385890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82455a86-902a-496c-bc02-27f7df623caa · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.390898Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a7af350-c4d3-4817-b52a-f0858b01196e · outbound

This paper cites A new approach to linear filtering and prediction problems.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods A new approach to linear filtering and prediction problems

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.543323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f2488756-b966-43e8-a14a-eaba6a0f6667 · outbound

This paper cites Quantizing deep convolutional networks for efficient inference: A whitepaper.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Quantizing deep convolutional networks for efficient inference: A whitepaper

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.400778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e12e6d92-50f5-42e8-8958-99978a0d4ce0 · outbound

This paper cites Imagenet classification with deep convolutional neural networks.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Imagenet classification with deep convolutional neural networks

Reference 20

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Source-reported events for the cited work

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Observation b6e693fb-20aa-462d-af8f-5b2e623f9861 · outbound

This paper cites Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.412519Z

Source-reported events for the cited work

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Observation bb30173c-bc6b-4dfd-a2a0-edc4985c3984 · outbound

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

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Repq-vit: Scale reparameterization for post-training quantization of vision transformers

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.506307Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d5b208c3-cbb2-4f8c-8871-843a36337700 · outbound

This paper cites AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.425294Z

Source-reported events for the cited work

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Observation aef689ca-d152-491d-801d-80513d939892 · outbound

This paper cites FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods FQ-ViT: Post-Training Quantization for Fully Quantized Vision Transformer

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.431120Z

Source-reported events for the cited work

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Observation c6ed0135-9c31-4687-9104-7306ab81f859 · outbound

This paper cites Pd-quant: Post-training quantization based on prediction difference metric.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Pd-quant: Post-training quantization based on prediction difference metric

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.484690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c348ce22-1544-4795-b684-d5328998ded5 · outbound

This paper cites VMamba: Visual State Space Model.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods VMamba: Visual State Space Model

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.442279Z

Source-reported events for the cited work

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Observation 5eba9eba-7919-4adb-84fc-aba9494357a4 · outbound

This paper cites torch.addcmul.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods torch.addcmul

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.462238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation c95159b4-e9db-4c16-8b59-782345d4a427 · outbound

This paper cites Imagenet large scale visual recognition challenge.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Imagenet large scale visual recognition challenge

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.453292Z

Source-reported events for the cited work

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Observation 8624be7f-b54a-4cc6-8de7-56019207d932 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Winogrande: An adversarial winograd schema challenge at scale

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.458752Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc831b7e-4dca-4601-bcb3-2110279fd5c1 · outbound

This paper cites OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods OmniQuant: Omnidirectionally Calibrated Quantization for Large Language Models

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.464311Z

Source-reported events for the cited work

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Observation bea52629-f9b9-480b-ba0b-b6a5d1d0e949 · outbound

This paper cites Simplified State Space Layers for Sequence Modeling.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Simplified State Space Layers for Sequence Modeling

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.469466Z

Source-reported events for the cited work

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Observation 62f784f3-2bcc-4399-95c1-ed11e3a365c4 · outbound

This paper cites A dataset of 101 human action classes from videos in the wild.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods A dataset of 101 human action classes from videos in the wild

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.475442Z

Source-reported events for the cited work

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Observation 8cb2cee3-0c20-4c61-a29b-56f03e8258ff · outbound

This paper cites Quip\#: Even better llm quantization with hadamard incoherence and lattice codebooks.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Quip\#: Even better llm quantization with hadamard incoherence and lattice codebooks

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.404508Z

Source-reported events for the cited work

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Observation 2ea6bf3a-be1c-45d7-924e-94324c191b44 · outbound

This paper cites Attention is all you need.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Attention is all you need

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.381636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 55a7481f-ec00-4695-a08d-f24d434e9acc · outbound

This paper cites SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.489615Z

Source-reported events for the cited work

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Observation 578bb1cf-8d2d-49dc-bd59-9e23d4d89745 · outbound

This paper cites Visual mamba: A survey and new outlooks, 2024.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Visual mamba: A survey and new outlooks, 2024

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.362793Z

Source-reported events for the cited work

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Observation 118dd46d-21dc-4ee5-b55b-913e32af1e18 · outbound

This paper cites An efficient multi-task learning cnn for driver attention monitoring.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods An efficient multi-task learning cnn for driver attention monitoring

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.346388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b743fc78-0c29-4ff4-af73-e3932c5c5a91 · outbound

This paper cites LLMViewer.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods LLMViewer

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T16:14:47.323690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 90927be6-dc92-4287-aad6-e002203a5f00 · outbound

This paper cites WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods WKVQuant: Quantizing Weight and Key/Value Cache for Large Language Models Gains More

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.511215Z

Source-reported events for the cited work

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Observation a70d2fd4-50f5-4cf6-853e-e9bdf6479133 · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.517722Z

Source-reported events for the cited work

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Observation 5361a71a-f250-42eb-8917-83edbabcaf33 · outbound

This paper cites Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Cobra: Extending Mamba to Multi-Modal Large Language Model for Efficient Inference

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.523651Z

Source-reported events for the cited work

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Observation 5f5e1167-45ff-4288-a70c-94a1eec17eab · outbound

This paper cites A Survey on Efficient Inference for Large Language Models.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods A Survey on Efficient Inference for Large Language Models

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.529618Z

Source-reported events for the cited work

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Observation 8639bf61-1d28-4d8d-b6c7-485eebf19f32 · outbound

This paper cites Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.534903Z

Source-reported events for the cited work

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Observation 98404ac3-6fc0-46cd-a486-51ae0894e414 · outbound

This paper cites write newline.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods write newline

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.539991Z

Source-reported events for the cited work

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Observation 6276b66c-c696-409d-b823-4f46a0c60376 · outbound

This paper cites @esa (Ref.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods @esa (Ref

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.546000Z

Source-reported events for the cited work

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Observation 247a712b-3b9e-482e-96ad-0de084f66112 · outbound

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MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods Unresolved cited work

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-10T16:14:46.551417Z

Source-reported events for the cited work

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Observation 82adf530-104e-4af9-ae46-a79fefd7ce18 · outbound

This paper cites " @ K w?O ?+[.-m >2O eh> sqǙ)Sd ӌ*hL /RM vk.

MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods " @ K w?O ?+[.-m >2O eh> sqǙ)Sd ӌ*hL /RM vk

Reference 47

Resolution: verified exact; raw_fallback, observed 2026-08-10T16:14:46.737353Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

Observation 4134610e-ad4e-4a5a-89b2-0200d2f51928 · inbound

eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing cites this paper.

eMamba: Efficient Acceleration Framework for Mamba Models in Edge Computing MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-05T20:34:29.580725Z

Source-reported events for the cited work

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Observation 50a902e6-ef1f-44c6-b45e-3d89654e789f · inbound

COREY: Entropy-Guided Runtime Chunk Scheduling for Selective Scan Kernels cites this paper.

COREY: Entropy-Guided Runtime Chunk Scheduling for Selective Scan Kernels MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:06:03.457499Z

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Observation 74b763f6-104c-4bab-a49e-553070b9e02a · inbound

MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization cites this paper.

MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:05:48.314740Z

Source-reported events for the cited work

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Observation 1df91c03-7cf1-430c-9665-f874efb8ec6f · inbound

Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning cites this paper.

Modeling Vehicle-Type-Specific Pedestrian Crash Avoidance Behavior in Safety-Critical Interactions Using Smooth-Mamba Deep Reinforcement Learning MambaQuant: Quantizing the Mamba Family with Variance Aligned Rotation Methods

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-06-29T12:23:24.606797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.