Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation
2026-08-05T08:52:10Z•9ac92232d735921a5ec23e507699f55faaf110aff75126e4c1454331c7f2d7ab
LLM-agentsadversarial-attacksarXivbandit-recommendationcollaborative-filteringdeep-researchdefensive-securityinformation-retrievalmodel-robustnessmulti-agent-systemsout-of-distributionposition-biasprompt-injection-riskrecommender-systemsrerankingretrieval
What happened
The document is an arXiv computer-science information-retrieval feed containing new research on recommender systems, deep-research agents, retrieval and reranking, multi-agent collaborative-filtering security, bandit recommendation, OOD recommendation, and long-sequence modeling. One paper specifically studies attacks and defenses against multi-agent collaborative-filtering systems, including dissemination and extraction attacks whose effectiveness varies with system connectivity. The remaining papers primarily present performance, compression, retrieval-quality, robustness, or deployment-e 관련
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ir
- Record identifier
- 9ac92232d735921a5ec23e507699f55faaf110aff75126e4c1454331c7f2d7ab
- Enrichment time
- 2026-08-05T08:52:10Z
- AI-assisted enrichment
- Yes
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