The Wiola Architecture for Efficient Small Language Models
Wiola is a fully original small language model architecture built from first principles, unrelated to existing families like GPT, LLaMA, Mistral, or Falcon. It introduces five novel components: Spiral Rotary Positional Encoding (SRPE), Gated Cross-Layer Attention (GCLA), Adaptive Token Merging (ATM), Dual Stream Feed-Forward (DSFF), and WiolaRMSNorm. Released in four sizes (120M to 1.5B parameters), it is fully compatible with HuggingFace Transformers.
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[Submitted on 1 Jul 2026]
Title:The Wiola Architecture for Efficient Small Language Models
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Abstract:We present Wiola, a fully original Small Language Model (SLM) architecture built from first principles, sharing no structural lineage with any existing model family including GPT, LLaMA, Mistral, or Falcon. Wiola introduces five independently novel components: (i) Spiral Rotary Positional Encoding (SRPE), which embeds token positions on a three-dimensional helical manifold combining absolute, relative, and hierarchical positional signals; (ii) Gated Cross-Layer Attention (GCLA), providing each decoder layer with soft cross-attention access to compressed summaries of two preceding layers for inter-layer coherence; (iii) Adaptive Token Merging (ATM), which dynamically merges se mantically redundant adjacent tokens in middle network layers to reduce attention complexity without information loss; (iv) Dual Stream Feed-Forward (DSFF), replacing the conventional MLP with two parallel streams fused by a learned per-dimension gate; and (v) WiolaRMSNorm, a modified normalisation introducing a per-dimension learned offset vector that prevents representation collapse. We provide complete mathematical derivations, architectural block diagrams, complexity analyses, and systematic comparisons against GPT-2, LLaMA-2, and Mistral. Wiola is released in four sizes (120M, 360M, 700M, and 1.5B parameters) and is fully compatible with the HuggingFace Transformers ecosystem, with all 22 architectural unit tests passing.
Comments: 7 Pages
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.01394 [cs.AI]
(or arXiv:2607.01394v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.01394
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aryuemaan Chowdhury [view email] [v1] Wed, 1 Jul 2026 18:52:56 UTC (3,002 KB)
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