TrainVerify: Equivalence-Based Verification for Distributed LLM Training | Paper Citation Record

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This paper cites https://docs.nvidia.com/cuda/.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training https://docs.nvidia.com/cuda/

Reference 1

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 2

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This paper cites https://github.com/microsoft/DeepSpeed/commit/a85b6e472534d2e 0b61fe234fae4f6a2332c95bf.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training https://github.com/microsoft/DeepSpeed/commit/a85b6e472534d2e 0b61fe234fae4f6a2332c95bf

Reference 3

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 5

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 7

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 9

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 10

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 11

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This paper cites https://github.com/NVIDIA/Megatron-LM/commit/32bbb76d5767fd bf8dc60d4ef07d103cef8aca02.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training https://github.com/NVIDIA/Megatron-LM/commit/32bbb76d5767fd bf8dc60d4ef07d103cef8aca02

Reference 12

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This paper cites https://github.com/N VIDIA/Megatron-LM/commit/9ad1944db1f97000377dc5aee36dcd65 6b1ae4a2.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training https://github.com/N VIDIA/Megatron-LM/commit/9ad1944db1f97000377dc5aee36dcd65 6b1ae4a2

Reference 13

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This paper cites https://github.com/NVIDIA/Megatron-LM/commit/6bd7 4b0e84577317c06c303f4dae26d249ab55d1#diff-c27f9f2765a43b5c78 1756ce9fe4b9abcc3618ec96157f5dc67c4a00a7900b73R777.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training https://github.com/NVIDIA/Megatron-LM/commit/6bd7 4b0e84577317c06c303f4dae26d249ab55d1#diff-c27f9f2765a43b5c78 1756ce9fe4b9abcc3618ec96157f5dc67c4a00a7900b73R777

Reference 14

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 15

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 16

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 17

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This paper cites https://github.com/NVIDIA/Megatron-LM/issues/673.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training https://github.com/NVIDIA/Megatron-LM/issues/673

Reference 18

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 19

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This paper cites https: //github.com/deepspeedai/DeepSpeed/issues/6714.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training https: //github.com/deepspeedai/DeepSpeed/issues/6714

Reference 20

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This paper cites https://numpy.org/.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training https://numpy.org/

Reference 21

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 22

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 23

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 24

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This paper cites GPT-4 Technical Report.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training GPT-4 Technical Report

Reference 25

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This paper cites Gemini: A Family of Highly Capable Multimodal Models.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Gemini: A Family of Highly Capable Multimodal Models

Reference 26

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This paper cites Tensorright: Auto- mated verification of tensor graph rewrites.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Tensorright: Auto- mated verification of tensor graph rewrites

Reference 27

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This paper cites Automaticdifferentiationinmachinelearning: a survey.Journal of machine learning research, 18(153):1–43, 2018.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Automaticdifferentiationinmachinelearning: a survey.Journal of machine learning research, 18(153):1–43, 2018

Reference 28

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This paper cites Maximizing Parallelism in Distributed Training for Huge Neural Networks.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Maximizing Parallelism in Distributed Training for Huge Neural Networks

Reference 29

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Language models are few-shot learners

Reference 30

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This paper cites Tvm: An automated end-to-end optimizing compiler for deep learning.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Tvm: An automated end-to-end optimizing compiler for deep learning

Reference 31

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This paper cites The rising costs of training frontier AI models.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training The rising costs of training frontier AI models

Reference 32

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Z3:Anefficientsmtsolver

Reference 33

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This paper cites Deepseek-v3 technical report, 2025.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Deepseek-v3 technical report, 2025

Reference 34

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This paper cites Real-timeself-drivingcarnavigationusingdeepneuralnetwork.In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), pages 7–12, 2018.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Real-timeself-drivingcarnavigationusingdeepneuralnetwork.In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD), pages 7–12, 2018

Reference 35

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training The Llama 3 Herd of Models

Reference 36

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This paper cites On neural network equivalence checking using smt solvers, 2022.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training On neural network equivalence checking using smt solvers, 2022

Reference 37

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This paper cites Gpipe: Efficient training of giant neural networks using pipeline parallelism.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Gpipe: Efficient training of giant neural networks using pipeline parallelism

Reference 38

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This paper cites Oobleck: Resilient distributed training of large models using pipelinetemplates.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Oobleck: Resilient distributed training of large models using pipelinetemplates

Reference 39

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This paper cites Taso: optimizing deep learning computation with automatic generation of graph substitutions.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Taso: optimizing deep learning computation with automatic generation of graph substitutions

Reference 40

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 41

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Scaling Laws for Neural Language Models

Reference 42

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This paper cites Reluplex: An efficient smt solver for verifying deep neural networks.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Reluplex: An efficient smt solver for verifying deep neural networks

Reference 43

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training Reducing activation recomputation in large transformer models

Reference 44

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This paper cites Sequence Parallelism: Long Sequence Training from System Perspective.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Sequence Parallelism: Long Sequence Training from System Perspective

Reference 45

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TrainVerify: Equivalence-Based Verification for Distributed LLM Training nnScaler: Constraint-guided parallelization plan generation for deep learning training

Reference 46

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This paper cites Algorithms for Verifying Deep Neural Networks.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Algorithms for Verifying Deep Neural Networks

Reference 47

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This paper cites Aceso: Efficient parallel DNN training through iterative bottleneck alleviation.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Aceso: Efficient parallel DNN training through iterative bottleneck alleviation

Reference 48

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This paper cites Ring Attention with Blockwise Transformers for Near-Infinite Context.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Ring Attention with Blockwise Transformers for Near-Infinite Context

Reference 49

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This paper cites Nnsmith: Generating diverse and valid test cases for deep learning compilers.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Nnsmith: Generating diverse and valid test cases for deep learning compilers

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:59.068853Z

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This paper cites Uncovering nested data parallelism and data reuse in dnn computation with fractaltensor.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Uncovering nested data parallelism and data reuse in dnn computation with fractaltensor

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:59.053794Z

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This paper cites Differential testing for software.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Differential testing for software

Reference 52

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This paper cites Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B

Reference 53

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This paper cites Towardsformalrepairandverificationofindustry-scaledeep 14 neural networks.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Towardsformalrepairandverificationofindustry-scaledeep 14 neural networks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:59.014521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 489894a5-e548-4ba7-ab01-01583adbcb35 · outbound

This paper cites Pipedream: generalized pipeline parallelism for dnn training.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Pipedream: generalized pipeline parallelism for dnn training

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.311310Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bdb5b81-5e7e-49c5-8220-3c48db58ad50 · outbound

This paper cites Automatic differentiation in pytorch.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Automatic differentiation in pytorch

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.315674Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d039ec3a-e778-4cb6-b93a-3957e95fd72f · outbound

This paper cites PyTorch.https://pytorch.org//.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training PyTorch.https://pytorch.org//

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.980289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1b13a9fa-98db-4cb3-8a2d-ee16fc65078e · outbound

This paper cites Deepspeed-moe: Advancing mixture-of-experts infer- ence and training to power next-generation ai scale.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Deepspeed-moe: Advancing mixture-of-experts infer- ence and training to power next-generation ai scale

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.965513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6d19a884-936b-4e67-a812-96cebbb8af22 · outbound

This paper cites ZeRO: Memory Optimizations Toward Training Trillion Parameter Models.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training ZeRO: Memory Optimizations Toward Training Trillion Parameter Models

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.328427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad11c4bd-9f04-4479-a608-94e42222dd6a · outbound

This paper cites Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.333521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe1e40c1-e814-461c-8c09-9d4b2c1bd1f3 · outbound

This paper cites Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.337796Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d02a2c3-f003-4d66-8f57-697db8005137 · outbound

This paper cites Deepid3: Face recognition with very deep neural networks, 2015.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Deepid3: Face recognition with very deep neural networks, 2015

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.937125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 875e469c-3a2a-488f-915b-9442b3bd21af · outbound

This paper cites Perturbing numerical calculations for statistical analysis of floating-point program (in) stability.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Perturbing numerical calculations for statistical analysis of floating-point program (in) stability

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.922595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a3b8e898-9694-4f66-8f41-18e54bff9562 · outbound

This paper cites PyTorch DistributedTensor (DTensor).https://github.c om/pytorch/pytorch/tree/master/torch/distributed/_tensor.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training PyTorch DistributedTensor (DTensor).https://github.c om/pytorch/pytorch/tree/master/torch/distributed/_tensor

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.908140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f650ec81-9ee3-4815-9243-318364f8ae74 · outbound

This paper cites TorchFX.https://pytorch.org/docs/stable/fx.html.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training TorchFX.https://pytorch.org/docs/stable/fx.html

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.893562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ccd8354c-dd77-4b3c-8b02-abe11663d9aa · outbound

This paper cites TorchScript.https://pytorch.org/docs/stable/jit.html.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training TorchScript.https://pytorch.org/docs/stable/jit.html

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.879689Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e7f1872e-6a61-4740-897b-4e76cf5a0e0a · outbound

This paper cites Revisiting Differential Verification: Equivalence Verification with Confidence.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Revisiting Differential Verification: Equivalence Verification with Confidence

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:34:58.484726Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 981b4a85-bb60-45ea-80e3-9debfa55b73c · outbound

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

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.369717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e308ecf-e9cb-4300-90d5-2cf1795deef9 · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.374518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7231bbae-c1f9-42cb-8930-57853c30c768 · outbound

This paper cites {PET}: Optimizing tensor programs with partially equivalent transformationsandautomatedcorrections.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training {PET}: Optimizing tensor programs with partially equivalent transformationsandautomatedcorrections

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.857548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 58c3379d-edf8-43ae-b18a-60d72ee8948c · outbound

This paper cites Beta-CROWN: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Beta-CROWN: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.383531Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6951e212-3030-4e9d-a0d0-5bbbda96dc15 · outbound

This paper cites Program slicing.IEEE Transactions on software engi- neering, (4):352–357, 1984.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Program slicing.IEEE Transactions on software engi- neering, (4):352–357, 1984

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.833428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 50d95501-9a94-4d1d-bc46-94a8b1e3c533 · outbound

This paper cites XLA — TensorFlow, compiled.https: //developers.googleblog.com/2017/03/xla-tensorflow-compiled.html.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training XLA — TensorFlow, compiled.https: //developers.googleblog.com/2017/03/xla-tensorflow-compiled.html

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.818882Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 95b47051-3488-459e-9e50-f3921741a216 · outbound

This paper cites OneFlow: Redesign the Distributed Deep Learning Framework from Scratch.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training OneFlow: Redesign the Distributed Deep Learning Framework from Scratch

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.396731Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 81f143b2-7800-45bf-a988-893d8ba12664 · outbound

This paper cites Gonzalez, and Ion Stoica.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Gonzalez, and Ion Stoica

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.804652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bd5b8130-54ef-4855-ab52-9b7ec36c704a · outbound

This paper cites Alpa: Automating inter-and{Intra-Operator} parallelism for distributed deep learning.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Alpa: Automating inter-and{Intra-Operator} parallelism for distributed deep learning

Reference 76

Resolution: malformed identifier; raw_fallback, observed 2026-08-15T19:34:58.789706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 314f5633-3e9b-4361-9f65-18a22233d285 · outbound

This paper cites striding.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training striding

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:34:58.775588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4d12a633-0eda-4103-92d2-d66407a1bf71 · outbound

This paper cites an unresolved cited work.

TrainVerify: Equivalence-Based Verification for Distributed LLM Training Unresolved cited work

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T19:34:58.231031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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