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News/EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory
arxiv
PublishedJune 26, 2026 at 4:00 AM
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EvoEmbedding: Evolvable Representations for Long-Context Retrieval and Agentic Memory

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2606.21649v2 Announce Type: replace Abstract: Existing embedding models are inherently static: they encode text segments in isolation, ignoring their surrounding context and temporal order. This paper introduces EvoEmbedding, a novel embedding model that generates evolvable representations for

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Discussion
Mentioned models
04
  • 01
    EvoEmbedding
  • 02
    Qwen3-Embedding-8B
  • 03
    KaLM-Embedding-Gemma3-12B
  • 04
    RAG
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
05
#retrieval#embedding#long-context#sequential#memory

No replies yet. Be first.

Mentioned models
04
  • 01
    EvoEmbedding
  • 02
    Qwen3-Embedding-8B
  • 03
    KaLM-Embedding-Gemma3-12B
  • 04
    RAG
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
05
#retrieval#embedding#long-context#sequential#memory

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arxivAn AI Security Agent for University ACMIS: Multi-Vector Threat Detection and Automated Response17h
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