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@alibaba_cloud: One PolarDB, one MemOS—persistent, never-blackout memory for AI. PolarDB & MemTensor launch a one-stop AI memory soluti…

X AI KOLs Timeline · yesterday Cached

Alibaba Cloud launches a one-stop AI memory solution combining PolarDB, MemOS, and MemTensor, offering relational, vector, and graph retrieval in a single PolarDB-PG instance, reducing P99 latency by up to 89.2%.

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#vector-retrieval

@Tsinghua_Uni: Breaking records across all four key benchmarks! Innovative Tsinghua's @AIRTHU1201 and collaborators have unveiled Gala…

X AI KOLs Timeline · 4d ago Cached

Tsinghua University and AIRTHU introduce GalaxyVS, a method that transforms protein–ligand docking into ultra-large-scale parallel vector retrieval, setting a new global benchmark for high-throughput drug discovery.

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#vector-retrieval

jina-embeddings-v5-omni: Text-Geometry-Preserving Multimodal Embeddings via Frozen-Tower Composition

arXiv cs.CL · 2026-05-12 Cached

This paper introduces jina-embeddings-v5-omni, a suite of multimodal embedding models that extend text embeddings to image, audio, and video using frozen-tower composition. The method trains only 0.35% of the total weights, maintaining text geometry while achieving competitive state-of-the-art performance with significantly lower computational cost.

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