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Integrity report for RLFTSim: Realistic and Controllable Multi-Agent Traffic Simulation via Reinforcement Learning Fine-Tuning

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

arXiv:2605.19033 · pith:2026:SXF7B6M6QURY5A2PDQA5CUPQM2

0Critical
0Advisory
5Detectors run
2026-05-27Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

ai_meta_artifact completed v1.0.0 · findings 0 · 2026-05-27 05:41:17.221024+00:00
doi_title_agreement completed v1.0.0 · findings 0 · 2026-05-26 19:03:03.409102+00:00
claim_evidence completed v1.0.0 · findings 0 · 2026-05-25 12:03:48.418341+00:00
doi_compliance completed v1.0.0 · findings 0 · 2026-05-25 10:07:36.340247+00:00
cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-20 20:52:41.222183+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/SXF7B6M6/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.