Paper Citation Record · LEDGER

VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2503.19449.

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

pith.paper-citation-record.v1
2503.19449 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T11:06:23.674395Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T05:19:34.876120Z

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 b75a7756-93ad-4cb4-adc4-5e5543385a41 · inbound

GenTT: Generate Vectorized Codes for General Tensor Permutation cites this paper.

GenTT: Generate Vectorized Codes for General Tensor Permutation VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T11:06:23.674395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c51f0bf-dd47-401c-9297-27cb36f31aa0 · inbound

SimdBench: Benchmarking Large Language Models for SIMD-Intrinsic Code Generation cites this paper.

SimdBench: Benchmarking Large Language Models for SIMD-Intrinsic Code Generation VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-06T15:43:40.145438Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d184e5bc-57fa-4d2b-b5dc-a2d42f29442f · inbound

AI Coding Agents Need Better Compiler Remarks cites this paper.

AI Coding Agents Need Better Compiler Remarks VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:10:21.960767Z

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.

Observation b049e5ee-9a39-49d9-9d07-e008e578e79e · inbound

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code cites this paper.

AutoVecCoder: Teaching LLMs to Generate Explicitly Vectorized Code VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

Reference 98

Resolution: verified exact; arxiv_id, observed 2026-05-20T11:43:14.939416Z

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.

Observation 0ae02696-7068-4354-a99e-3ead4db28670 · inbound

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

AutoPass: Evidence-Guided LLM Agents for Compiler Performance Tuning VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-07-04T05:19:34.877769Z

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.

Observation f14dfd45-1518-4945-a6b1-fdd6c6a42114 · 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? VecTrans: Enhancing Compiler Auto-Vectorization through LLM-Assisted Code Transformations

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-05T04:38:11.125800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.