OPERA: Online Data Pruning for Efficient Retrieval Model Adaptation
arXiv 2603.17205•b8ea1662453434ff0e21046605440ca572ab85094d06b86d4de5cf93430389fb
GLIDENEOOPERAPJBThought 1 (T1)VLM2Recbenchmarkscatalog groundingcitation recommendationcontinual learningdata pruningdense retrievaldynamic pruningfederated learninggenerative retrievalinductive evaluationmodality collapsemodel adaptationpodcast recommendationproduction deploymentreasoning-intensive retrievalrecommendation systemsreinforcement learning (GRPO)semantic IDsvision-language models
Paper metadata
- arXiv ID
- 2603.17205
- Version
- Not specified by this published record
- Category
- Computer Science — Information Retrieval (cs.IR)
The PDF link points to arxiv.org. Baitaphish does not expose a private stored PDF.
Evidence and limitations
- Source ID
- arxiv_cs_ir
- Record identifier
- b8ea1662453434ff0e21046605440ca572ab85094d06b86d4de5cf93430389fb
- Enrichment time
- 2026-03-19T08:52:17Z
- AI-assisted enrichment
- Yes
This record may overlap with other records. Its enrichment can be incomplete or wrong, and machine assistance was used. Validate consequential decisions against the linked source and your own environment.