Microscaling Data Formats for Deep Learning | Paper Citation Record

Observation 959aa0db-96b4-4463-baca-1b0164f3a122 · inbound

AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference cites this paper.

AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference Microscaling Data Formats for Deep Learning

Reference 63

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Hardware Trends Impacting Floating-Point Computations In Scientific Applications cites this paper.

Hardware Trends Impacting Floating-Point Computations In Scientific Applications Microscaling Data Formats for Deep Learning

Reference 29

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Interface for Sparse Linear Algebra Operations cites this paper.

Interface for Sparse Linear Algebra Operations Microscaling Data Formats for Deep Learning

Reference 45

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Observation f3aca87b-e858-4abc-a9a4-d958f173d0ee · inbound

FlexiBit: Fully Flexible Precision Bit-parallel Accelerator Architecture for Arbitrary Mixed Precision AI cites this paper.

FlexiBit: Fully Flexible Precision Bit-parallel Accelerator Architecture for Arbitrary Mixed Precision AI Microscaling Data Formats for Deep Learning

Reference 47

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Observation c3c6efde-3bb9-4f8f-b95c-5fb0b86ad6b8 · inbound

Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference cites this paper.

Flash Communication: Reducing Tensor Parallelization Bottleneck for Fast Large Language Model Inference Microscaling Data Formats for Deep Learning

Reference 58

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Observation 30a922f6-ae9a-4391-9fc2-12d018e30109 · inbound

BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference cites this paper.

BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference Microscaling Data Formats for Deep Learning

Reference 21

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Observation 43f6fff7-a872-4f6f-87a4-8e47fe87f75a · inbound

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity cites this paper.

SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and Temporal Sparsity Microscaling Data Formats for Deep Learning

Reference 22

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Observation 9245f61c-c08f-4b2b-b863-9419f3c24209 · inbound

Pushing the Limits of BFP on Narrow Precision LLM Inference cites this paper.

Pushing the Limits of BFP on Narrow Precision LLM Inference Microscaling Data Formats for Deep Learning

Reference 26

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Observation 3f317212-e3a0-455a-9f01-eabbdba8f908 · inbound

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation cites this paper.

Tilus: A Tile-Level GPGPU Programming Language for Low-Precision Computation Microscaling Data Formats for Deep Learning

Reference 47

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Observation ddd75e2a-0e21-4ac1-8c67-ae2f0c25e74b · inbound

FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference cites this paper.

FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference Microscaling Data Formats for Deep Learning

Reference 24

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Observation 7f4fea8b-ac56-4d43-b213-6f240e7505d1 · inbound

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities cites this paper.

Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities Microscaling Data Formats for Deep Learning

Reference 10

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Observation 6bd37c6d-4928-4491-9011-c5ec9b5cdc82 · inbound

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training cites this paper.

Gaussian Weight Sampling for Scalable, Efficient and Stable Pseudo-Quantization Training Microscaling Data Formats for Deep Learning

Reference 29

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Observation 897470db-ef0a-4c88-a08c-aea47e2fba63 · inbound

MXDOTP: A RISC-V ISA Extension for Enabling Microscaling (MX) Floating-Point Dot Products cites this paper.

MXDOTP: A RISC-V ISA Extension for Enabling Microscaling (MX) Floating-Point Dot Products Microscaling Data Formats for Deep Learning

Reference 2023

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Observation e0aade8b-4176-40b5-9bad-2d26c00f6f3f · inbound

Scaling Law for Quantization-Aware Training cites this paper.

Scaling Law for Quantization-Aware Training Microscaling Data Formats for Deep Learning

Reference 34

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Observation 889b3360-a0d1-49be-9c43-d9a2f099cc22 · inbound

FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-design cites this paper.

FPQVAR: Floating Point Quantization for Visual Autoregressive Model with FPGA Hardware Co-design Microscaling Data Formats for Deep Learning

Reference 34

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Observation 99a00f12-8207-439c-b6f2-350163c31afb · inbound

How to keep pushing ML accelerator performance? Know your rooflines! cites this paper.

How to keep pushing ML accelerator performance? Know your rooflines! Microscaling Data Formats for Deep Learning

Reference 36

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Observation 7b4d5fc0-00e7-4635-bd23-bcd65a4c4de4 · inbound

FP4 All the Way: Fully Quantized Training of LLMs cites this paper.

FP4 All the Way: Fully Quantized Training of LLMs Microscaling Data Formats for Deep Learning

Reference 15

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Observation b05af5ff-ab51-4ff7-b3ef-96242990f417 · inbound

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation cites this paper.

Beyond the Buzz: A Pragmatic Take on Inference Disaggregation Microscaling Data Formats for Deep Learning

Reference 13

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Observation ea954705-fd04-42aa-92d2-feba37489914 · inbound

Recipes for Pre-training LLMs with MXFP8 cites this paper.

Recipes for Pre-training LLMs with MXFP8 Microscaling Data Formats for Deep Learning

Reference 3

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Observation 2d06fb11-d978-4def-ab53-7d7750db16ee · inbound

Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks cites this paper.

Log-Normal Multiplicative Dynamics for Stable Low-Precision Training of Large Networks Microscaling Data Formats for Deep Learning

Reference 51

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Observation bc6af1a2-d133-4866-81ad-f447763dcaa4 · inbound

Characterization and Mitigation of Training Instabilities in Microscaling Formats cites this paper.

Characterization and Mitigation of Training Instabilities in Microscaling Formats Microscaling Data Formats for Deep Learning

Reference 39

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Observation 260f865f-bf22-41ba-941c-9f2c8516265b · inbound

Hybrid Systolic Array Accelerator with Optimized Dataflow for Edge Large Language Model Inference cites this paper.

Hybrid Systolic Array Accelerator with Optimized Dataflow for Edge Large Language Model Inference Microscaling Data Formats for Deep Learning

Reference 21

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Observation b732f6c7-90c8-4a87-af9b-f6255f175922 · inbound

TorchAO: PyTorch-Native Training-to-Serving Model Optimization cites this paper.

TorchAO: PyTorch-Native Training-to-Serving Model Optimization Microscaling Data Formats for Deep Learning

Reference 11

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Observation 8ecaf391-d4bf-46f3-b78c-4663723696ff · inbound

Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference cites this paper.

Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference Microscaling Data Formats for Deep Learning

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-18T17:51:42.214887Z

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Observation 92e42020-4801-456c-9c6a-fbbfd4e89d65 · 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 Microscaling Data Formats for Deep Learning

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-17T02:23:52.774424Z

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Observation 5bacdbd1-1915-4f66-80a4-195e592fc989 · inbound

SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision cites this paper.

SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision Microscaling Data Formats for Deep Learning

Reference 10

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Observation b922ee21-5e4e-48e3-84d8-c4dd2ff40647 · inbound

ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs cites this paper.

ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs Microscaling Data Formats for Deep Learning

Reference 10

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Observation 26067140-9557-461d-97a0-fc26efc44524 · inbound

DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce cites this paper.

DynamiQ: Accelerating Gradient Synchronization using Compressed Multi-hop All-reduce Microscaling Data Formats for Deep Learning

Reference 59

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Observation 9138fc19-8e25-4027-b643-e08a85025d27 · inbound

Diagonal-Tiled Mixed-Precision Attention for Efficient Low-Bit MXFP Inference cites this paper.

Diagonal-Tiled Mixed-Precision Attention for Efficient Low-Bit MXFP Inference Microscaling Data Formats for Deep Learning

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-13T17:33:02.303676Z

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Observation 991abe71-dca1-4f68-bf04-e8755d7693d3 · inbound

LOCALUT: Harnessing Capacity-Computation Tradeoffs for LUT-Based Inference in DRAM-PIM cites this paper.

LOCALUT: Harnessing Capacity-Computation Tradeoffs for LUT-Based Inference in DRAM-PIM Microscaling Data Formats for Deep Learning

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:15:49.669653Z

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Observation ff7dc2f2-3774-4385-9ac7-c5b2ebeaeddf · inbound

HiFloat4 Format for Language Model Pre-training on Ascend NPUs cites this paper.

HiFloat4 Format for Language Model Pre-training on Ascend NPUs Microscaling Data Formats for Deep Learning

Reference 13

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T08:05:59.824512Z

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Observation 6fb18462-2aea-4ee2-b1c3-9dc2d7b98fd3 · inbound

OSC: Hardware Efficient W4A4 Quantization via Outlier Separation in Channel Dimension cites this paper.

OSC: Hardware Efficient W4A4 Quantization via Outlier Separation in Channel Dimension Microscaling Data Formats for Deep Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T09:05:59.520668Z

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Observation c48a6cc7-ca14-4642-9ecd-25e9b0f4f0e9 · inbound

StreamIndex: Memory-Bounded Compressed Sparse Attention via Streaming Top-k cites this paper.

StreamIndex: Memory-Bounded Compressed Sparse Attention via Streaming Top-k Microscaling Data Formats for Deep Learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-09T06:15:37.680566Z

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Observation 4e18bf1c-568d-4e90-9799-6564fc0f453e · inbound

Pretraining large language models with MXFP4 on Native FP4 Hardware cites this paper.

Pretraining large language models with MXFP4 on Native FP4 Hardware Microscaling Data Formats for Deep Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-12T05:41:26.364189Z

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Observation 2dd452da-9ee5-4db2-9ce8-19a2f9ab37c3 · inbound

Pretraining large language models with MXFP4 on Native FP4 Hardware cites this paper.

Pretraining large language models with MXFP4 on Native FP4 Hardware Microscaling Data Formats for Deep Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-14T21:19:28.727720Z

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Observation c9c9f698-f1df-4444-9864-de54501e14a4 · inbound

Pretraining large language models with MXFP4 on Native FP4 Hardware cites this paper.

Pretraining large language models with MXFP4 on Native FP4 Hardware Microscaling Data Formats for Deep Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:19:46.436060Z

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Observation 6f91f873-ce09-485d-9fa8-6aa85ab53d52 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Microscaling Data Formats for Deep Learning

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-12T06:06:28.153739Z

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Observation 9b8803d6-86b6-4a38-96d5-abadc95c0592 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Microscaling Data Formats for Deep Learning

Reference 67

Resolution: verified exact; arxiv_id, observed 2026-05-15T04:59:46.216734Z

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Observation c8b3b680-1f3f-4f4e-abf6-ef929b62cb3c · inbound

The Entropy of Floating-Point Numbers cites this paper.

The Entropy of Floating-Point Numbers Microscaling Data Formats for Deep Learning

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-13T01:27:01.603906Z

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Observation d9b819fc-1efd-4f57-bca2-adf82abf8687 · inbound

SOAR: Scale Optimization for Accurate Reconstruction in NVFP4 Quantization cites this paper.

SOAR: Scale Optimization for Accurate Reconstruction in NVFP4 Quantization Microscaling Data Formats for Deep Learning

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T06:02:23.831066Z

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Observation 33871e95-793f-44b0-98f6-1c01b0f10fa3 · inbound

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models cites this paper.

Grid Games: The Power of Multiple Grids for Quantizing Large Language Models Microscaling Data Formats for Deep Learning

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:47:26.456839Z

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Observation 0d46cba1-eb42-4a06-a9c9-599a942712af · inbound

Multi-Scale Dequant: Eliminating Dequantization Bottleneck via Activation Decomposition for Efficient LLM Inference cites this paper.

Multi-Scale Dequant: Eliminating Dequantization Bottleneck via Activation Decomposition for Efficient LLM Inference Microscaling Data Formats for Deep Learning

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:58:34.206283Z

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Observation d601843e-fc0b-4f28-aa46-e865bb0f7d35 · inbound

A Hardware-Aware, Per-Layer Methodology for Post-Training Quantization of Large Language Models cites this paper.

A Hardware-Aware, Per-Layer Methodology for Post-Training Quantization of Large Language Models Microscaling Data Formats for Deep Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-06-30T21:05:03.956703Z

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Observation 17ee07c3-6a31-4afa-8d91-289e28cc800f · inbound

LongLive-2.0: An NVFP4 Parallel Infrastructure for Long Video Generation cites this paper.

LongLive-2.0: An NVFP4 Parallel Infrastructure for Long Video Generation Microscaling Data Formats for Deep Learning

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:03:13.294669Z

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Observation 69db121e-6ddc-4936-a720-0d3b37141821 · inbound

The Thermodynamic Costs of Simple Linear Regression cites this paper.

The Thermodynamic Costs of Simple Linear Regression Microscaling Data Formats for Deep Learning

Reference 46

Resolution: verified exact; arxiv_id, observed 2026-05-20T07:03:23.258985Z

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Observation aad4aede-8983-4ecf-87b1-85f69f9805d8 · inbound

ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention cites this paper.

ThriftAttention: Selective Mixed Precision for Long-Context FP4 Attention Microscaling Data Formats for Deep Learning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:30:22.804401Z

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Observation 135f833e-3ba9-43e5-af4f-4ea77dfc875e · inbound

MASQ: Accelerating Masked Diffusion via Stage-Wise Multi-Precision Quantization cites this paper.

MASQ: Accelerating Masked Diffusion via Stage-Wise Multi-Precision Quantization Microscaling Data Formats for Deep Learning

Reference 33

Resolution: metadata mismatch; arxiv_id, observed 2026-05-25T03:00:15.853710Z

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Observation b373f641-574f-42ba-917d-93f9d7edce77 · inbound

MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation cites this paper.

MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation Microscaling Data Formats for Deep Learning

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:44:39.221357Z

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Observation bb2595bb-1b2b-4090-9cd2-22061cbbe22e · inbound

Cassandra: Enabling Reasoning LLMs at Edge via Self-Speculative Decoding cites this paper.

Cassandra: Enabling Reasoning LLMs at Edge via Self-Speculative Decoding Microscaling Data Formats for Deep Learning

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-07-01T16:25:49.798946Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05e0a655-bb1e-40b1-ba1c-b15cc2f4fa1c · inbound

O-POPE: High-Frequency Pipelined Outer Product based GEMM acceleration with minimal buffering overhead cites this paper.

O-POPE: High-Frequency Pipelined Outer Product based GEMM acceleration with minimal buffering overhead Microscaling Data Formats for Deep Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-02T01:16:25.114709Z

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1c44425-878a-4bf9-ad33-a0bc5552a848 · inbound

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats cites this paper.

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats Microscaling Data Formats for Deep Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-02T02:06:26.120872Z

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Observation 481d94f3-5b30-4a98-b9f7-de44a7593944 · inbound

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats cites this paper.

dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats Microscaling Data Formats for Deep Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-15T10:56:54.155632Z

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Observation e902f905-60a7-459b-8bd5-03b9000ab0a7 · inbound

GoldenFloat: A Phi-Derived Static-Split Floating-Point Family from GF4 to GF1024 with a Lucas-Exact Integer Identity cites this paper.

GoldenFloat: A Phi-Derived Static-Split Floating-Point Family from GF4 to GF1024 with a Lucas-Exact Integer Identity Microscaling Data Formats for Deep Learning

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:26:54.592249Z

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Observation 0377c884-1de7-4aca-9b77-524deeb31b7d · inbound

Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature cites this paper.

Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature Microscaling Data Formats for Deep Learning

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:54:37.877107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3be37054-5140-45bf-b02a-c094514b5651 · inbound

An 83-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8, BF16, MXFP4, and Microscaling Formats cites this paper.

An 83-Format Numeric Catalog with Bit-Exact Conformance Vectors: A Vendor-Neutral Reference for FP8, BF16, MXFP4, and Microscaling Formats Microscaling Data Formats for Deep Learning

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-07-03T03:47:35.977373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1afda128-a2e0-45ec-9c83-f49e7ca59e58 · inbound

ReSET: Accurate Latency-Critical NVFP4 Reasoning via Step-Aware Temperature Scaling cites this paper.

ReSET: Accurate Latency-Critical NVFP4 Reasoning via Step-Aware Temperature Scaling Microscaling Data Formats for Deep Learning

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:38:19.595324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 10c04bb2-3aca-450f-be0b-9ac900872458 · inbound

Rethinking Shrinkage Bias in LLM FP4 Pretraining: Geometric Origin, Systemic Impact, and UFP4 Recipe cites this paper.

Rethinking Shrinkage Bias in LLM FP4 Pretraining: Geometric Origin, Systemic Impact, and UFP4 Recipe Microscaling Data Formats for Deep Learning

Reference 50

Resolution: verified exact; arxiv_id, observed 2026-07-04T04:29:34.888762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a1689784-7c2d-4ab1-a94d-10375afd1e29 · inbound

HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models cites this paper.

HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models Microscaling Data Formats for Deep Learning

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-07-04T10:29:45.538537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a5cc00b9-0efd-4b55-aa55-d5d2d391f9cd · inbound

SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference cites this paper.

SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference Microscaling Data Formats for Deep Learning

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-07-04T12:59:52.348306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 024a417c-01ec-4800-92ba-301d29e9da18 · inbound

Full-Stack FP4: Stable LLM Pretraining with Quantized Projections, Optimizers, and Attention cites this paper.

Full-Stack FP4: Stable LLM Pretraining with Quantized Projections, Optimizers, and Attention Microscaling Data Formats for Deep Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-11T19:17:59.044982Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0988e4e9-1dd2-4acc-90a4-5efb10eec4d8 · inbound

WINT: A Novel Weighted Integer Representation with Improved Error Characteristics cites this paper.

WINT: A Novel Weighted Integer Representation with Improved Error Characteristics Microscaling Data Formats for Deep Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-14T15:56:34.283388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 345071d8-d100-4f97-9f30-ebfbf078e0d6 · inbound

Jack of All Scales: A Versatile FPGA Tensor Block for MXFP Precisions cites this paper.

Jack of All Scales: A Versatile FPGA Tensor Block for MXFP Precisions Microscaling Data Formats for Deep Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T03:25:29.316109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be13ca87-9323-40a3-bad4-990ba4de8185 · inbound

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference cites this paper.

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference Microscaling Data Formats for Deep Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T17:12:27.562005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d150366b-146d-4864-8500-6d7060c83dfc · inbound

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference cites this paper.

MXSens: Sensitivity-Aware Mixed-Precision Quantization for Efficient LLM Inference Microscaling Data Formats for Deep Learning

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-01T17:12:28.283144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16918522-e25b-46b1-aa71-5d4ea41f551a · inbound

CANN Bench: Benchmarking Agent Generated Kernels against Real NPU and Algorithmic Limits cites this paper.

CANN Bench: Benchmarking Agent Generated Kernels against Real NPU and Algorithmic Limits Microscaling Data Formats for Deep Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T08:11:51.850576Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e42587c1-dcac-4350-8f5e-00f64a879e1f · inbound

MXAttention: Data-Free Optimal Scaling and Pre-Normalization Quantization for MXFP4 Attention cites this paper.

MXAttention: Data-Free Optimal Scaling and Pre-Normalization Quantization for MXFP4 Attention Microscaling Data Formats for Deep Learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-31T16:32:52.030874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aca84154-2b5c-41cb-816f-ca068542a253 · inbound

Stable FP4 Training via Transposition-Invariant Block Quantization cites this paper.

Stable FP4 Training via Transposition-Invariant Block Quantization Microscaling Data Formats for Deep Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T05:04:00.375801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d02397a-cb4b-4f3f-a300-63f457fec6d0 · inbound

GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference cites this paper.

GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference Microscaling Data Formats for Deep Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-01T03:16:46.884728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2399894d-50e4-4d9a-9916-01847cc72137 · inbound

LightRot: A Light-Weighted Rotation Scheme and Architecture for Accurate Low-Bit Large Language Model Inference cites this paper.

LightRot: A Light-Weighted Rotation Scheme and Architecture for Accurate Low-Bit Large Language Model Inference Microscaling Data Formats for Deep Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-01T03:01:45.331505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07becc51-2ceb-46cf-a8fa-f4e98530c00b · inbound

Studying quantization trade-offs for efficient inference deployment in machine translation cites this paper.

Studying quantization trade-offs for efficient inference deployment in machine translation Microscaling Data Formats for Deep Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-03T07:51:22.144778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a406126b-0fa0-4da3-ba75-2c0fab223ca1 · inbound

Studying quantization trade-offs for efficient inference deployment in machine translation cites this paper.

Studying quantization trade-offs for efficient inference deployment in machine translation Microscaling Data Formats for Deep Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-05T04:25:22.011849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af45e318-48cd-4b14-b734-4317077b8f5e · inbound

FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling cites this paper.

FOCUS: FP4 Optimization via Coupled-Relaxation and Dual-Granularity Scaling Microscaling Data Formats for Deep Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-04T19:49:24.965978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0feeb5e7-3265-4323-a76d-44f7884cf796 · inbound

One QK Channel, Many Sources: Guarding Low-Precision Attention Collapse cites this paper.

One QK Channel, Many Sources: Guarding Low-Precision Attention Collapse Microscaling Data Formats for Deep Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-04T15:20:16.175942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4833d5a2-b500-44c4-a4c2-19fff570c7ee · inbound

When Does Disaggregation Pay? Simulating Prefill--Decode--Attention--FFN Specialization for Agentic LLM Inference cites this paper.

When Does Disaggregation Pay? Simulating Prefill--Decode--Attention--FFN Specialization for Agentic LLM Inference Microscaling Data Formats for Deep Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T13:48:36.545613Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8149f9c-a0c1-4b1e-84be-2c3e9c5b26d4 · inbound

Heterogeneity-Aware Microscaling for Efficient Low-Bit LLM Inference cites this paper.

Heterogeneity-Aware Microscaling for Efficient Low-Bit LLM Inference Microscaling Data Formats for Deep Learning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-05T10:54:30.865642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88f16c11-468e-465d-97e0-d854675b5a6f · inbound

Motif 3: Technical Report cites this paper.

Motif 3: Technical Report Microscaling Data Formats for Deep Learning

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-11T23:12:10.143929Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 73f6dc94-4c05-4d38-87bb-2e905ce944fd · inbound

CurveFP: Co-Designing Numerical Representation and Product Arithmetic for Language Models cites this paper.

CurveFP: Co-Designing Numerical Representation and Product Arithmetic for Language Models Microscaling Data Formats for Deep Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T00:51:15.605710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d743f1df-8ce5-427e-802b-41fe267b5fcc · inbound

CurveFP: Co-Designing Numerical Representation and Product Arithmetic for Language Models cites this paper.

CurveFP: Co-Designing Numerical Representation and Product Arithmetic for Language Models Microscaling Data Formats for Deep Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-14T04:46:37.255337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Coverage vector

Reference resolution

Outbound references

Pith citing papers