Finder: A Multimodal AI-Powered Search Framework for Pharmaceutical Data Retrieval
2026-03-18T08:52:17Z•ee886fa46c0265a0826a119f15721cd8ed43ffd02ff51f040a287c68d53b01aa
AILLMRAGbiasbiomedical-knowledge-graphexplainabilityinformation-exposurelatent-memorylocal-firstmemory-systemsmultimodal-searchrecommender-systemsreproducibilityretrievalroutingtemporal-factsvector-search
What happened
This arXiv feed collects new research (Mar 18, 2026) on AI systems for retrieval, memory, recommendation, and reproducibility. Highlights include: Finder, a scalable multimodal (text/image/audio/video) hybrid vector search framework for pharmaceutical data; studies on temporal fact conflicts in LLMs reconciling DYNAMICQA and MULAN; Answer Bubbles quantifying source-selection and confidence biases in generative search; open-source reproduction and explainability analysis of Corrective RAG (CRAG); MemX, a local-first long-term memory system with stability-focused retrieval; NextMem, a latent-f实际
Why it matters
A reviewed impact interpretation has not been published for this record.
Evidence and limitations
- Source ID
- arxiv_cs_ir
- Record identifier
- ee886fa46c0265a0826a119f15721cd8ed43ffd02ff51f040a287c68d53b01aa
- Enrichment time
- 2026-03-18T08:52:17Z
- AI-assisted enrichment
- Yes
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