Integrity report for Fair-Aurora: Comparing Fairness Strategies for Reinforcement Learning-Based Congestion Control in Multi-Flow Environments

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.19909 · pith:2026:YLOZMCVOGMDGXNR6TYKDYZCRCX

0Critical

0Advisory

6Detectors run

2026-05-21Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-21 05:01:37.188673+00:00

doi_compliance completed v1.0.0 · findings 0 · 2026-05-21 04:53:43.611729+00:00

claim_evidence completed v1.0.0 · findings 0 · 2026-05-21 03:42:20.303207+00:00

citation_quote_validity completed v0.1.0 · findings 0 · 2026-05-20 09:50:07.004927+00:00

cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-20 06:52:25.819346+00:00

ai_meta_artifact skipped v1.0.0 · findings 0 · 2026-05-20 02:33:28.044155+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/YLOZMCVO/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.