MOSS: A Production Framework for Monitoring and Diagnosing Semantic Failures in Search and Recommendation Systems
arXiv 2610.26902v1•6977dd7a899d91043ad07f9bc1c13114b46a12492c449bc69ae86625d8da2de5
Paper metadata
- arXiv ID
- 2610.26902
- Version
- v1
- Category
- cs.AI, cs.IR
- Authors
- Alina Nesen, Prathyusha Kanmanth Reddy, David Relyea, Rohit Avinash Mujumdar, Samaneh Karimi, Bo Shen, Jordan Hashemi
- Publication date
- 2026-10-21T04:00:00Z
- Source identifier
- 2610.26902v1
- Public record ID
- record:sha256:6977dd7a899d91043ad07f9bc1c13114b46a12492c449bc69ae86625d8da2de5
The PDF link points to arxiv.org. Baitaphish does not expose a private stored PDF.
This is source-provided metadata, not an enriched summary or an impact assessment. Follow the canonical source link for the published material.
Evidence and limitations
- Source ID
- arxiv_research
- Record identifier
- 6977dd7a899d91043ad07f9bc1c13114b46a12492c449bc69ae86625d8da2de5
- Record type
- Source metadata
This record may overlap with other records. Source metadata can be incomplete or change. Validate consequential decisions against the linked source and your own environment.