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 retrieval. It is tailored for long-context scenarios, where information is dynamic, sequential, and requires continuous state tracking. Our design is simple: EvoEmbedding maintains a continuously updated latent memory as it sequentially processes inputs, and uses it alongside the raw content to joi
The rapid advancement of large language models and autonomous agents necessitates more sophisticated and dynamic memory systems to handle long and evolving contexts.
A breakthrough in evolvable representations directly improves the effectiveness and capability of AI agents, enabling complex, sustained interactions and operations.
Retrieval systems and agentic memory will transition from static, isolated indexing to dynamic, continuously updated latent representations, significantly enhancing their contextual awareness.
- · AI Agent Developers
- · Companies Building Long-Context AI Applications
- · Foundation Model Researchers
- · Companies Relying Solely on Static Embedding Models
- · Traditional Information Retrieval Systems
AI agents will exhibit improved long-term coherence and efficiency in complex tasks requiring continuous state tracking.
New applications for AI agents will emerge that were previously impossible due to limitations in contextual memory and information retrieval.
The enhanced capabilities of AI agents could accelerate automation in white-collar workflows, potentially leading to unforeseen economic and societal shifts.
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Read at arXiv cs.CL