Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation

2026-08-05T08:52:10Z9ac92232d735921a5ec23e507699f55faaf110aff75126e4c1454331c7f2d7ab
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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Record · Knowledge-Geometry Decoupling: Refreshable Pretrained Transfer for Streaming Recommendation · Baitaphish