Paper Citation Record · LEDGER

Microscaling Data Formats for Deep Learning

As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 47 inbound Pith citation observations for arXiv:2310.10537.

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

pith.paper-citation-record.v1
2310.10537 v3

Coverage vector

measured 0 of 0 reference resolution

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measured 47 of 47 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-04T06:34:03.388597+00:00

measured 47 of 47 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-03T16:35:53.163602Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-10T06:15:00.866473Z

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Outbound references

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Pith citing papers

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

Resolution: unresolved; no resolver link, observed 2026-08-03T16:35:53.163602Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-03T11:10:52.658894Z

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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

Resolution: unresolved; no resolver link, observed 2026-08-03T03:13:24.213416Z

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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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+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

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.

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

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+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

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+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

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+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

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+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

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No event found in the named queried sources as of 2026-08-04T06:34:03.388597+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 2026-08-04T06:34:03.388597+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

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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

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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

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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

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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

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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.