Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.12146.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T22:34:34.776239Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T13:46:37.078741Z
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 abf61ba1-7759-4af5-abc8-febd0e8059b3 · inbound
Language Models for Code Optimization: Survey, Challenges and Future Directions cites this paper.
Language Models for Code Optimization: Survey, Challenges and Future Directions Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models
Reference 113
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T22:34:34.776239Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fbe7f28d-5e17-4f10-9b44-c22702fbd875 · inbound
A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards cites this paper.
A Blueprint for AI-Driven Software Quality: Integrating LLMs with Established Standards Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models
Reference 135
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T13:46:37.082679Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation a4cf8450-6b35-482e-bce8-1995e1338d68 · inbound
CETBench: A Novel Dataset constructed via Transformations over Programs for Benchmarking LLMs for Code-Equivalence Checking cites this paper.
CETBench: A Novel Dataset constructed via Transformations over Programs for Benchmarking LLMs for Code-Equivalence Checking Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:57:22.088513Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d717d99e-8e17-4e16-93c8-aaa5045b0c37 · inbound
Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors cites this paper.
Accelerating Latency-Critical Applications with AI-Powered Semi-Automatic Fine-Grained Parallelization on SMT Processors Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T13:12:44.187916Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a18fbc48-b4b3-4956-a603-9faeb4fcec1d · inbound
PerfCoder: Large Language Models for Interpretable Code Performance Optimization cites this paper.
PerfCoder: Large Language Models for Interpretable Code Performance Optimization Should AI Optimize Your Code? A Comparative Study of Classical Optimizing Compilers Versus Current Large Language Models
Reference 33
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T22:43:38.193503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.