Paper Citation Record · LEDGER

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach

As of 23 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2505.02146.

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

pith.paper-citation-record.v1
2505.02146 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

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measured 52 of 52 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:34:04.484160Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T10:34:05.194529Z

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 80e05c6d-5022-4c52-b789-4a3751ddb5ec · outbound

This paper cites https:// developer.nvidia.com/cublas.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https:// developer.nvidia.com/cublas

Reference 1

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This paper cites https://www.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://www

Reference 2

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Observation c184edd7-4be2-4d0a-9b3a-da38cc0bb2ff · outbound

This paper cites https://www.cambricon.com/.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://www.cambricon.com/

Reference 3

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This paper cites https://aws.amazon.com/.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://aws.amazon.com/

Reference 4

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Observation 52a6628c-1a21-494e-a9b5-c3aa760662dc · outbound

This paper cites https://github.com/gotranspile/cxgo.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://github.com/gotranspile/cxgo

Reference 5

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Observation 8438c5e9-1deb-4464-b22f-5d233171777a · outbound

This paper cites https:// cloud.google.com/.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https:// cloud.google.com/

Reference 6

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This paper cites https://github.com/ROCm/HIPIFY.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://github.com/ROCm/HIPIFY

Reference 7

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This paper cites https://openai.com/o1/.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://openai.com/o1/

Reference 8

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This paper cites https://www.graphcore.ai/ products/ipu.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://www.graphcore.ai/ products/ipu

Reference 9

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This paper cites https: //azure.microsoft.com/.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https: //azure.microsoft.com/

Reference 10

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Observation 4c6b4350-a96c-4cda-b1f4-3188f6352fe0 · outbound

This paper cites https://developer.nvidia.com/ cudnn.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://developer.nvidia.com/ cudnn

Reference 11

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This paper cites https://www.nvidia.cn/ data-center/tensor-cores/.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://www.nvidia.cn/ data-center/tensor-cores/

Reference 12

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This paper cites https://github.com/immunant/ c2rust.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://github.com/immunant/ c2rust

Reference 13

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Observation df73e265-a7b2-4ac3-9fb8-e7416291a3c3 · outbound

This paper cites https://github.com/intel/mkl-dnn.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach https://github.com/intel/mkl-dnn

Reference 14

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Observation d2b5c24a-0d53-4767-b7ad-f33c973e5ff4 · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Neural Machine Translation by Jointly Learning to Align and Translate

Reference 16

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Observation f7c6e283-8f4a-4154-95d8-c60e0c6049d9 · outbound

This paper cites Mosaic: An interoperable compiler for tensor algebra.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Mosaic: An interoperable compiler for tensor algebra

Reference 17

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Observation a9dc7db1-42e5-49f3-ae52-121381dd6b2e · outbound

This paper cites Seshia, and Alvin Cheung.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Seshia, and Alvin Cheung

Reference 18

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Observation f02b47b2-96b2-47c6-915e-81d9fd0b5e16 · outbound

This paper cites Browne, Edward Powley, Daniel White- house, Simon M.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Browne, Edward Powley, Daniel White- house, Simon M

Reference 19

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Observation 625cea9c-bb2b-4931-9e8d-8685d2913efd · outbound

This paper cites Sparks of Artificial General Intelligence: Early experiments with GPT-4.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Sparks of Artificial General Intelligence: Early experiments with GPT-4

Reference 20

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This paper cites Evaluating Large Language Models Trained on Code.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Evaluating Large Language Models Trained on Code

Reference 21

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Teaching Large Language Models to Self-Debug

Reference 22

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 23

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This paper cites Cox: Exposing cuda warp-level functions to cpus.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Cox: Exposing cuda warp-level functions to cpus

Reference 24

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Observation 52764abc-b7ec-488f-804f-b1080400b504 · outbound

This paper cites Deep residual learning for image recognition.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Deep residual learning for image recognition

Reference 25

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 26

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This paper cites Exocompi- lation for productive programming of hardware accel- erators.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Exocompi- lation for productive programming of hardware accel- erators

Reference 27

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This paper cites Martini: The little match and replace tool for automatic application rewriting with code exam- ples.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Martini: The little match and replace tool for automatic application rewriting with code exam- ples

Reference 28

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This paper cites In- datacenter performance analysis of a tensor process- ing unit.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach In- datacenter performance analysis of a tensor process- ing unit

Reference 29

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Taco: A tool to gen- erate tensor algebra kernels

Reference 30

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This paper cites Starcoder: may the source be with you!, 2023.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Starcoder: may the source be with you!, 2023

Reference 31

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Competition-level code generation with alphacode

Reference 32

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This paper cites C2taco: Lifting tensor code to taco.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach C2taco: Lifting tensor code to taco

Reference 33

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Z3: an effi- cient smt solver

Reference 34

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis

Reference 35

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach GPT-4 Technical Report

Reference 36

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Efficient compilation of cuda kernels for high- performance computing on fpgas

Reference 37

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Chasins, and Rastislav Bodík

Reference 38

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Seshia, and Alvin Cheung

Reference 39

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Unsupervised translation of programming languages

Reference 40

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Generative neu- ral machine translation

Reference 41

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Simonyan and A

Reference 42

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Gemini: A Family of Highly Capable Multimodal Models

Reference 43

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 44

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Im- provements to bm25 and language models examined

Reference 45

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Observation d12e8ec4-15b9-428e-86ea-7c2f9a4fa4bb · outbound

This paper cites Vectorization for dig- ital signal processors via equality saturation extended abstract.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Vectorization for dig- ital signal processors via equality saturation extended abstract

Reference 46

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Polyhedral parallel code generation for cuda

Reference 47

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This paper cites CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Reference 48

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QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Unresolved cited work

Reference 49

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Observation 93875104-612b-44c0-a274-fcb30f419f25 · outbound

This paper cites Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-16T01:04:39.331949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29469557-66ab-4bb3-86f0-ca8a4e48f075 · outbound

This paper cites Vision transformer with deformable at- tention.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach Vision transformer with deformable at- tention

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T01:04:39.566390Z

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 f3e58cc1-5028-41ae-9cfd-e0beb20bed28 · outbound

This paper cites CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X.

QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-16T01:04:39.340420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7f31bb79-1361-4a0e-94ac-20c752b3b97b · inbound

QiMeng: Fully Automated Hardware and Software Design for Processor Chip cites this paper.

QiMeng: Fully Automated Hardware and Software Design for Processor Chip QiMeng-Xpiler: Transcompiling Tensor Programs for Deep Learning Systems with a Neural-Symbolic Approach

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-08-07T10:34:05.200280Z

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.