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Paper Citation Record · LEDGER

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

As of 13 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 17 inbound Pith citation observations for arXiv:2512.23236.

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

pith.paper-citation-record.v1
2512.23236 v4

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T13:45:26.384520Z

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 17 of 17 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:49:17.692396Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T15:59:56.477055Z

Reference resolution

31 of 31 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved31
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b21ffa7d-8c19-4d7c-b753-10221183612c · outbound

This paper cites "" Generate test inputs for the conv1d benchmark. Returns: List of input tuples for benchmarking.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta "" Generate test inputs for the conv1d benchmark. Returns: List of input tuples for benchmarking

Reference 1

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source=pdf_text observed=2026-08-03T13:45:26.256280Z digest=sha256:520cb8f724421161d3f78da6d6f2c7c3eefb0dfa831cba80c9b3e7ca75d1f949

Observation 5dc3be4f-1921-4f9b-b3be-74c3e3e9f265 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Evaluating Large Language Models Trained on Code

Reference 4

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source=pdf_text observed=2026-08-03T13:45:23.245038Z digest=sha256:b6c3337b5c6c8e41a8d12471086183d7baa84df7587ef2b632285cf92a05d609

Observation 9651214a-fb95-4701-9ae9-a09d438c2181 · outbound

This paper cites RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

Reference 6

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source=pdf_text observed=2026-08-03T13:45:23.514060Z digest=sha256:af816abcf6e9e08827c9c9d02c9e47703f34d951a11d95fece7c4ae4a4060a78

Observation eae177ea-07c3-4cd9-9be1-0870d84ec4f6 · outbound

This paper cites Conformer: Convolution-augmented Transformer for Speech Recognition.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Conformer: Convolution-augmented Transformer for Speech Recognition

Reference 7

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source=pdf_text observed=2026-08-03T13:45:23.606608Z digest=sha256:6cead05b3dd1b67e1e2003e9bed997ad98cc75d772e73dfdcc733cfbb0c0c501

Observation ac5b9827-210b-4ca7-86d0-4fc20638f9b2 · outbound

This paper cites an unresolved cited work.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Unresolved cited work

Reference 8

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source=pdf_text observed=2026-08-03T13:45:23.774742Z digest=sha256:b80d71e1e04cfe284972de8b0311704aa927f91b602a6034c0d9c2b772827dca

Observation 3c9456ce-532c-427e-b8a2-42d69905c488 · outbound

This paper cites Filescale: Fast and elastic metadata management for distributed file systems.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Filescale: Fast and elastic metadata management for distributed file systems

Reference 11

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source=pdf_text observed=2026-08-03T13:45:23.995333Z digest=sha256:f0269e479452e7c9d5dc03b412ca867e10927ef317c3c414ccc077f342d66d1a

Observation 41517040-ad4c-452d-950b-b7f825d66354 · outbound

This paper cites Flock: A Low-Cost Streaming Query Engine on FaaS Platforms.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Flock: A Low-Cost Streaming Query Engine on FaaS Platforms

Reference 12

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source=pdf_text observed=2026-08-03T13:45:24.149777Z digest=sha256:984240e292903cf38ac26ecc5c4ade1aa260700cafe88f799d371a8d5c5166a0

Observation 848ac868-f81a-4f48-bbfd-688372a5c7d0 · outbound

This paper cites Indexing code at scale with glean.Engineering at Meta, 2024.https://engineering.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Indexing code at scale with glean.Engineering at Meta, 2024.https://engineering

Reference 14

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source=pdf_text observed=2026-08-03T13:45:24.333393Z digest=sha256:60a329dfb8fd09ff46fbafb341fdcb2fccf027283943fd78305173db54a1918e

Observation 511ccc80-3cad-4e34-bb1b-b6fc877da6a1 · outbound

This paper cites doi: 10.14778/3476311.3476374.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta doi: 10.14778/3476311.3476374

Reference 16

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source=pdf_text observed=2026-08-03T13:45:24.486486Z digest=sha256:c40e1853c3129442c85228e6b92b446c103811681ec6e749eb18c3ad623b78f9

Observation aca01176-5f3e-4d19-8d4a-a417cdc0e728 · outbound

This paper cites Deep Learning Recommendation Model for Personalization and Recommendation Systems.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Deep Learning Recommendation Model for Personalization and Recommendation Systems

Reference 17

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source=pdf_text observed=2026-08-03T13:45:24.615459Z digest=sha256:7e99929bb9bcb8e3d6b38ec0086c7ef2ba260bf24ff6751e2afec98202a3a4d5

Observation b8deef5d-b6dd-4c45-b18c-28a1c8c23686 · outbound

This paper cites AlphaEvolve: A coding agent for scientific and algorithmic discovery.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta AlphaEvolve: A coding agent for scientific and algorithmic discovery

Reference 18

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source=pdf_text observed=2026-08-03T13:45:24.718311Z digest=sha256:58c557cb67d350281460ff321689207b7bd98f7da9c353fc0b21996635e19cc9

Observation 3d01128d-c344-4a7e-a199-3dfe6028e2ba · outbound

This paper cites an unresolved cited work.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-03T13:45:24.859452Z digest=sha256:4a61dc1dea70738c460a3d505a4af092fc752f6c5fbc2301e24e553a46535cb9

Observation 1a9bc960-66be-44df-b5ce-0b42d841e16d · outbound

This paper cites KernelBench: Can LLMs Write Efficient GPU Kernels?.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta KernelBench: Can LLMs Write Efficient GPU Kernels?

Reference 20

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source=pdf_text observed=2026-08-03T13:45:24.924747Z digest=sha256:dba5363ce723926babc9a8cf27b4f9fb0248b8fa868a9164a2edd4ca1f751d04

Observation 6c2cdce6-c2d1-4158-b44d-b06399502b62 · outbound

This paper cites Code Llama: Open Foundation Models for Code.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Code Llama: Open Foundation Models for Code

Reference 21

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source=pdf_text observed=2026-08-03T13:45:25.073445Z digest=sha256:4d5b9c4d061ac8d388abfcbfd34139abdb760cce3139f3678f9fb59c53e81af0

Observation d28a9f2a-d0d1-4f79-a16c-ae971cecde5b · outbound

This paper cites Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters

Reference 22

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source=pdf_text observed=2026-08-03T13:45:25.169837Z digest=sha256:69a77e7823b3f3976c569627c9f89f19ddc2905bbf2e1005b036eb78940fc50c

Observation 7e65ae80-c715-4b3c-93ec-e803ad6f993c · outbound

This paper cites an unresolved cited work.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Unresolved cited work

Reference 23

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source=pdf_text observed=2026-08-03T13:45:25.283340Z digest=sha256:69da3432bd0d72994c425be42b32c32e18cfe7deec16d83b2d8738b6c2b84806

Observation 392fefee-f7ba-4809-b6b5-2dd34a97aef5 · outbound

This paper cites Miller, Abhishek Charnalia, Derek Dunfield, Carole-Jean Wu, Pontus Stenetorp, Nicola Cancedda, Jakob Nicolaus Foerster, and Yoram Bachrach.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Miller, Abhishek Charnalia, Derek Dunfield, Carole-Jean Wu, Pontus Stenetorp, Nicola Cancedda, Jakob Nicolaus Foerster, and Yoram Bachrach

Reference 24

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source=pdf_text observed=2026-08-03T13:45:25.429320Z digest=sha256:d938ce73b60c6da8200e2d548e97831c73ce82dbd550178aa61e2b16cc9680a1

Observation 5532370f-e610-486e-993f-7121016e220e · outbound

This paper cites Catransformers: Carbon aware transformers through joint model-hardware optimization, 2025a.https://arxiv.org/abs/2505.01386.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Catransformers: Carbon aware transformers through joint model-hardware optimization, 2025a.https://arxiv.org/abs/2505.01386

Reference 25

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source=pdf_text observed=2026-08-03T13:45:25.585191Z digest=sha256:68e3f329a87930c70ba3459cbdf1d8a5433d9c1b1f4bb0ad2f5e6252889bcaa5

Observation 922ae791-e02a-400c-ac92-4fa5eba117d5 · outbound

This paper cites Tritonrl: Training llms to think and code triton without cheating.arXiv preprint arXiv:2510.17891,.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Tritonrl: Training llms to think and code triton without cheating.arXiv preprint arXiv:2510.17891,

Reference 26

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source=pdf_text observed=2026-08-03T13:45:25.682913Z digest=sha256:c4fbbcdee6d53654a345545337b65d43d9fb1d1a28c886031c855d5302db7dc0

Observation 81b273f5-8768-4a37-b0f8-5d9f82937e80 · outbound

This paper cites Sustainable AI: Environmental Implications, Challenges and Opportunities.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Sustainable AI: Environmental Implications, Challenges and Opportunities

Reference 27

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source=pdf_text observed=2026-08-03T13:45:25.794734Z digest=sha256:858fdc004c5f1310e00027eb2c5cc462af61e3f21b4a6f6359e97d2f83a734de

Observation eab3c915-f0ae-4cf8-86b4-400a17abe466 · outbound

This paper cites DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta DHEN: A Deep and Hierarchical Ensemble Network for Large-Scale Click-Through Rate Prediction

Reference 28

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source=pdf_text observed=2026-08-03T13:45:25.896757Z digest=sha256:ea89b5f4eb9f1607341bef793a2a3dccc0c07017178e7d874e35d305643756b0

Observation 12bec119-2815-42f1-8a77-e766cf650bb1 · outbound

This paper cites Wukong: Towards a Scaling Law for Large-Scale Recommendation.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Wukong: Towards a Scaling Law for Large-Scale Recommendation

Reference 29

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source=pdf_text observed=2026-08-03T13:45:25.951325Z digest=sha256:c73a4f691f25a58bdd4861adc5ceae6e25b7b19b9ec31a784b44695cde65163c

Observation 7546709b-61fe-485b-b6b0-abf00c3cc622 · outbound

This paper cites cuda"):.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta cuda"):

Reference 30

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source=pdf_text observed=2026-08-03T13:45:26.094302Z digest=sha256:41c1ba565896e9878bb0fbd2b34ec24402e804cff3a9b76e5936d18278ebdf55

Observation d5b5b740-85d5-473b-bd31-563494051280 · outbound

This paper cites cuda" ) print(.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta cuda" ) print(

Reference 200

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source=pdf_text observed=2026-08-03T13:45:26.384520Z digest=sha256:00342c3b67f2dd47f89d4180fe64a04e7e724b97bb6661bc70fa6e1525e7a6c5

Observation f7d6f04c-c5b9-457f-8aaa-d35c3d966914 · outbound

This paper cites MLIR: A Compiler Infrastructure for the End of Moore's Law.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta MLIR: A Compiler Infrastructure for the End of Moore's Law

Reference 2006

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source=pdf_text observed=2026-08-03T13:45:23.834738Z digest=sha256:3d1862439db8f071c2860bef3f2556830404a3ff5a1fafb163876e2e98cf98b6

Observation 93105a98-378f-465a-bb5e-632364e23704 · outbound

This paper cites Deepgemm.github, 2025.https://github.com/deepseek-ai/DeepGEMM.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Deepgemm.github, 2025.https://github.com/deepseek-ai/DeepGEMM

Reference 2017

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source=pdf_text observed=2026-08-03T13:45:23.430451Z digest=sha256:961ef085106f614722e3e2fac07ca798ac6198251e2fbe116e0cf6acf6501ac4

Observation 3d58e3ee-7e3e-4b98-8dc0-8d796741b770 · outbound

This paper cites Gevo-ml: a proposal for optimizing ml code with evolutionary computation.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Gevo-ml: a proposal for optimizing ml code with evolutionary computation

Reference 2019

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source=pdf_text observed=2026-08-03T13:45:24.226682Z digest=sha256:da874a8ccce47164a55b0feff122a444882ed4e48d3d9b8a50574f3e1cca7891

Observation 257561b4-aae0-4ed0-9d2f-82d6f9394b58 · outbound

This paper cites an unresolved cited work.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Unresolved cited work

Reference 2021

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source=pdf_text observed=2026-08-03T13:45:24.403338Z digest=sha256:24ee5f7d7eab761363d74c2d887a785b171911f506bde3cbc0cd4f4907c5eaab

Observation 5be8a002-a8eb-4657-ae2e-de270d1fd6d1 · outbound

This paper cites AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs

Reference 2022

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source=pdf_text observed=2026-08-03T13:45:23.894739Z digest=sha256:8d602a92e22a98d9076d29b3a45828d60dcf1e4527c855fb8126f72371bda976

Observation e063c74a-bf05-49cb-808f-89a132b5fac5 · outbound

This paper cites Kevin: Multi-Turn RL for Generating CUDA Kernels.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Kevin: Multi-Turn RL for Generating CUDA Kernels

Reference 2023

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source=pdf_text observed=2026-08-03T13:45:23.116995Z digest=sha256:e2d5b29204eb8cb864662cb81fead8447d338d7bf855fa4b4db9a6589cb4d46a

Observation ef5b3cd2-29b0-478c-9fdf-e3ad6ae03424 · outbound

This paper cites Anthropic.

KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta Anthropic

Reference 2024

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source=pdf_text observed=2026-08-03T13:45:23.071109Z digest=sha256:d7cab55454bf88f42f36fe03b1a4d3592cda80c1462c424951b2557ebe821577

Pith citing papers

Observation c63b94c3-8612-446c-8794-15ff1b6145bb · inbound

AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization cites this paper.

AscendOptimizer: Episodic Agent for Ascend NPU Operator Optimization KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 18

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arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-21T10:16:15.305706Z digest=sha256:b6f283a16dce94b35157aad1f30f5c9c307a2ad9933fb84f34635b94632e2411

Observation 59155007-d8bb-4848-a850-a2355e7c9f4f · inbound

Autopoiesis: A Self-Evolving System Paradigm for LLM Serving Under Runtime Dynamics cites this paper.

Autopoiesis: A Self-Evolving System Paradigm for LLM Serving Under Runtime Dynamics KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 24

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arxiv_id, observed 2026-07-08T02:18:09.597449Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T17:30:40.021376Z digest=sha256:eb76057f0263a468bdd24eb8726863141586d0120268dd903873684d819ab591

Observation 4e6f61db-41ca-4534-9f8d-2c27dcd40d0a · inbound

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs cites this paper.

DITRON: Distributed Multi-level Tiling Compiler for Parallel Tensor Programs KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 14

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arxiv_id, observed 2026-07-08T02:18:09.597449Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T15:57:35.983246Z digest=sha256:42c268b75d0628b25905d56c1cf11aaf72f61b99f6824a73d266dd544b69b031

Observation 93f8ced4-091f-4908-8d68-04dcea875cbc · inbound

Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design cites this paper.

Agentic Discovery of Neural Architectures: AIRA-Compose and AIRA-Design KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 2

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metadata mismatch
arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-05-20T18:58:11.197587Z digest=sha256:929bd3e7d53e7dd6b893ecad7168805888a92a7b97f1c8e4263fde0f518745d0

Observation 1a3fef23-285b-40d1-919b-59aa1f09630b · inbound

FastKernels: Benchmarking GPU Kernel Generation in Production cites this paper.

FastKernels: Benchmarking GPU Kernel Generation in Production KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-25T05:20:08.724490Z digest=sha256:b8e975c01d58f7e6b3c4dafc4c9dd9c4e81e8abfe9ed69aa1f4ec8ad876ebf50

Observation 41ffd550-9cf2-46f8-b35f-7df9acbe8c9c · inbound

PassNet: Scaling Large Language Models for Graph Compiler Pass Generation cites this paper.

PassNet: Scaling Large Language Models for Graph Compiler Pass Generation KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-29T07:53:06.859337Z digest=sha256:49510775f93172e1572a86f8838c778215e9f4ca6b90c75b4a0c0df0ac5c34d4

Observation 44c3d5cf-c889-4d4a-a0ce-48f8339eb77a · inbound

From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs cites this paper.

From Human Guidance to Autonomy: Agent Skill System for End-to-End LLM Deployment on Spatial NPUs KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-29T13:52:02.579141Z digest=sha256:68c8c91e437f6512724002dc9990fd26083ef48f52f01c449b163d5fbf49b7d6

Observation 8efa09c6-9076-47b9-805b-659a7d033b6b · inbound

SpecGen: Accelerating Agentic Kernel Optimization with Speculative Generation cites this paper.

SpecGen: Accelerating Agentic Kernel Optimization with Speculative Generation KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-26T23:35:32.175768Z digest=sha256:e9e756019055c3f1d3d7fa926d122db750d5f41ef901af0871d407a5e13b4567

Observation e2b28305-31b6-4fcd-8222-57fdedeeb66f · inbound

Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization cites this paper.

Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=arxiv_source observed=2026-06-26T01:12:04.486570Z digest=sha256:41300d6826cbbf37f0aefefcd913aa48516a69d7a0088bf611dea15e3371f0cd

Observation e2c9f095-4f19-434f-befa-8280e794d0a9 · inbound

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks cites this paper.

Evolution Fine-Tuning: Learning to Discover Across 371 Optimization Tasks KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-30T09:15:22.900338Z digest=sha256:71b400a82f8200635a1804e54b7404d55365e3d342a0ce3622452fce20cf07b9

Observation e79da50a-a3e5-4eca-85e7-8b69c34ee883 · inbound

Experience Graphs: The Data Foundation for Self-Improving Agents cites this paper.

Experience Graphs: The Data Foundation for Self-Improving Agents KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 27

Resolution
verified exact
arxiv_id, observed 2026-07-08T02:18:09.597449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-30T04:09:13.775462Z digest=sha256:f64eea7e227803585fd2d187aba247daf2fbc7ee132cfac64aba43016ce9352f

Observation 5f66e74a-7739-4045-9a94-3e6adce77fbd · inbound

Harness Engineering for LLM-Driven GPU Kernel Generation cites this paper.

Harness Engineering for LLM-Driven GPU Kernel Generation KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-01T16:30:09.495157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T16:30:09.495157Z digest=sha256:dcc881e6916cf2959ace3d8731ea552768da6e1ed36aaf6d342809397b9db1c9

Observation bdbbadb9-a7fc-4d87-b303-ffb90626ea05 · inbound

JAXBench: Benchmarking Autonomous TPU Kernel Optimization cites this paper.

JAXBench: Benchmarking Autonomous TPU Kernel Optimization KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-02T13:46:46.055177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T13:46:46.055177Z digest=sha256:08d71fbed206b88a7e49b5ae36da4e73f48eeb5f87d9b06fb0fbb986dc4dbb59

Observation 9b9bf2ec-22fe-4644-8be9-e2602e96d490 · inbound

MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation cites this paper.

MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 2025

Resolution
unresolved
no resolver link, observed 2026-08-02T10:39:34.871795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T10:39:34.871795Z digest=sha256:e8bd5a3aaed381dfac74ba229d6e10b7441cdc1d6309d2be9839027e0ea1eb45

Observation e4304ab4-ac96-43a4-a99d-c69f34731980 · inbound

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization cites this paper.

Compiler-Grounded Hierarchical Diagnosis for LLM-Based Triton Kernel Optimization KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-01T03:42:32.081445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T03:42:32.081445Z digest=sha256:226756f82ba2510717957897e5f0669459da586cc9c8ca829f387df5f6308c34

Observation 57133728-bec5-4bd2-aff7-0242d2d63708 · inbound

AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies cites this paper.

AtumAI: A Principled Framework for Agentic Generation of Datacenter Control-Plane Policies KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-04T04:39:20.554389Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T04:39:20.554389Z digest=sha256:cb584b0a7a2e220947cbc606115c53eee4682d6774470d270d8a61b4bf13472b

Observation 11e87be3-eb61-45b1-8ec6-697cddae1004 · inbound

Architectural Implications of Agentic AI Workflows cites this paper.

Architectural Implications of Agentic AI Workflows KernelEvolve: Scaling Agentic Kernel Coding for Heterogeneous AI Accelerators at Meta

Reference 43

Resolution
unresolved
no resolver link, observed 2026-08-06T23:49:17.692396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T23:49:17.692396Z digest=sha256:6aa13a6d5a7f5ecbd1d8408d83eaa3b758fc73e426a77abe748196a7745c3791