Spectral Retrieval: Multi-Scale Sinc Convolution over Token Embeddings for Localized Retrieval in LLM Multi-Agent Systems
arXiv:2605.24764v1 Announce Type: cross Abstract: [Abridged] - Spectral Retrieval is a plug-in re-ranking stage that interpolates between per-token MaxSim and mean-pool retrieval through a multi-scale sinc convolution over token embeddings. In standard dense retrieval each document is one mean-pooled vector; when relevance localises into a short subspan, the signal averages into noise. Spectral Retrieval reuses per-token embeddings from a late-interaction index and convolves them with a normalised sinc kernel at multiple scales. At L=1 the kernel acts as the identity, recovering per-token MaxS
The paper introduces a method to improve localized retrieval within LLM multi-agent systems, aligning with the ongoing push for more precise and context-aware AI interactions.
This development enhances the ability of AI agents to retrieve highly specific information from large datasets, leading to more accurate analyses and decision-making for strategic applications.
Retrieval mechanisms for AI agents can now be significantly more localized and precise, reducing noise and improving the specificity of information used in complex AI operations.
- · AI Agent developers
- · Companies deploying LLM multi-agent systems
- · Data-intensive industries
- · Inefficient retrieval systems
- · General-purpose search algorithms
Multi-agent LLM systems gain improved contextual understanding through more precise information retrieval.
Enhanced retrieval precision leads to more reliable and trustworthy outputs from AI agents, accelerating their adoption in critical applications.
The increased effectiveness of AI agents could further automate complex workflows, changing the landscape of white-collar work and SaaS industries.
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Read at arXiv cs.CL