Symbolic-Vector Attention Fusion for Collective Intelligence
When autonomous agents observe different domains of a shared environment, each signal they exchange mixes relevant and irrelevant dimensions. No existing mechanism lets the receiver evaluate which dimensions to absorb. We introduce Symbolic-Vector Attention Fusion (SVAF), the content-evaluation half of a two-level coupling engine for collective intelligence. SVAF decomposes each inter-agent signal into 7 typed semantic fields, evaluates each through a learned fusion gate, and produces a remix --
Record details
Published: 5 April 2026
Source: arXiv
Category: Research
Topics: Agents & autonomy · Environment
Retrieved: 14 July 2026
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ethics.ai (5 April 2026), “Symbolic-Vector Attention Fusion for Collective Intelligence,” evidence record 6358, https://ethics.ai/record/6358 (originally published by arXiv).
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