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Paper Citation Record · LEDGER

MLGO: a Machine Learning Guided Compiler Optimizations Framework

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2101.04808.

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

pith.paper-citation-record.v1
2101.04808 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T04:38:11.214987Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:19:34.879431Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 409b5751-bb98-468a-b4ca-4f6adb206f6a · inbound

Agentic Harness for Real-World Compilers cites this paper.

Agentic Harness for Real-World Compilers MLGO: a Machine Learning Guided Compiler Optimizations Framework

Reference 2024

Resolution
malformed identifier
no resolver link, observed 2026-08-03T02:35:32.035728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T02:35:32.035728Z digest=sha256:79390dae83d52629584c4455e508bf2986931f121e3ad710bf2d08da77c55533

Observation 06409774-7c02-4677-aa7d-7263a348f4d5 · inbound

AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve cites this paper.

AI-PROPELLER: Warehouse-Scale Interprocedural Code Layout Optimization with AlphaEvolve MLGO: a Machine Learning Guided Compiler Optimizations Framework

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-06-29T15:03:31.925524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-29T06:05:00.849044Z digest=sha256:30e44668f900232b21267bff5d6231d5e2ad9153a2786a5edfacff0865a0a794

Observation a4341cf8-a784-4210-8c0f-38f6459cbbd8 · inbound

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning cites this paper.

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning MLGO: a Machine Learning Guided Compiler Optimizations Framework

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-07-04T05:19:34.881393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-26T16:10:14.144156Z digest=sha256:41935e14bb7e1059ae388b020c3b25cea1ca664eee3ba5feaf7fae2de0856acf

Observation 4922a27f-7128-47c7-9b30-66c38b8b3737 · inbound

WarmTuner: Program-Specific Warm Starts for Compiler Autotuning via Offline-to-Online Reinforcement Learning cites this paper.

WarmTuner: Program-Specific Warm Starts for Compiler Autotuning via Offline-to-Online Reinforcement Learning MLGO: a Machine Learning Guided Compiler Optimizations Framework

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-01T01:25:24.759911Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T01:25:24.759911Z digest=sha256:f34ec1c064fd1ce48233cac824f7b750c5dc0549eae5456593bc5bb9ff150cf5

Observation 62d6bd44-6c80-45ee-a2ae-e4946ceb2d04 · inbound

Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss? cites this paper.

Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss? MLGO: a Machine Learning Guided Compiler Optimizations Framework

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-05T04:38:11.214987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T04:38:11.214987Z digest=sha256:d6767ed952e88a63cbd0b4b40066c4ae3f8c3c8c8d71152d5bd5341930dacc4e