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New PRISM architecture uses phase interference for better language representation

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced PRISM, a novel complex-valued neural network architecture that utilizes semantic phase locking and interference to better represent language data. Unlike standard Transformers that conflate semantic importance with activation magnitude, PRISM enforces a unit-norm constraint and employs gated harmonic convolutions. This design encourages the model to use subtractive interference in the frequency domain to suppress noise, rather than magnitude-based gating. Experiments suggest that phase-based spectral interference is a viable computational mechanism for sequence modeling, leading to improved parameter efficiency and representation quality. AI

IMPACT Introduces a novel computational mechanism for sequence modeling that could improve efficiency and representation quality.

RANK_REASON Academic paper detailing a new neural network architecture. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New PRISM architecture uses phase interference for better language representation

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Academic paper detailing a new neural network architecture. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Alper Y{\i}ld{\i}r{\i}m, \.Ibrahim Y\"uceda\u{g} ·

    Language as a Wave Phenomenon: Semantic Phase Locking and Interference in Neural Networks

    arXiv:2512.01208v5 Announce Type: replace-cross Abstract: In standard Transformer architectures, semantic importance is often conflated with activation magnitude, obscuring the geometric structure of latent representations. To disentangle these factors, we introduce PRISM, a comp…

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