Avey Architecture: Scalable Neural Model

• Avey Architecture is a neural model that decouples input sequence length from the processing window, enabling efficient management of long-range dependencies.
• Its ranker module computes cosine similarities to select the top‑k relevant splits, ensuring only contextually significant tokens are processed.
• Empirical tests reveal that Avey outperforms Transformer models on extended sequence tasks while reducing per-token latency with linear inference scaling.

Avey Architecture constitutes a foundational neural architecture distinguished by its complete separation from both attention mechanisms and recurrent structures. Developed to efficiently process arbitrarily long sequences while mitigating the quadratic complexity and fixed context window limits of standard Transformer models, Avey leverages a two-component system designed to rank and contextualize relevant sequence tokens. Experimental evidence demonstrates that Avey not only matches but, in long-range dependency tasks, surpasses Transformer-based models under identical context window constraints and parameter regimes (Hammoud et al., 12 Jun 2025). Its applicability spans diverse NLP settings where robust sequence modeling and latency performance are critical.

1. Fundamental Components and Processing Pipeline

Avey Architecture is defined by the interaction of two principal modules: the Ranker and the Autoregressive Neural Processor.

• Ranker:

The ranker decomposes the input token sequence into fixed-size contiguous splits. For each "current" split, it computes all pairwise cosine similarities with previous splits using the MaxSim operator, which identifies the highest similarity for each token, then aggregates these to yield split-wise relevance scores. The top‑$k$ relevant splits are selected and normalized (score divided by the highest among the selected) to form a contextual memory window. This mechanism allows tokens contextualized at arbitrary positions, effectively decoupling context width from actual sequence length.

• Autoregressive Neural Processor:

This processor comprises three subunits: 1. Enricher: Each token embedding $X$ is projected through a learned matrix $U$ and bias $b$, then activated:

$Z = \sigma(X U + b)$

The resulting embedding $Z$ is partitioned into a "head" $Z_h$—directly bypassed to the fusion module—and a "tail" $Z_t$—sent for neural contextualization. 2. Contextualizer: On $Z_t$, embedding-wise operations and dynamic parameterization enable gating and feature transformation:

$$c(Z_t) = Z_{t_l} \odot \sigma \left((V \odot \mathcal{N}(Z_{t_r}) \mathcal{N}(Z_{t_r})^T) Z_{t_r} + b' \right)$$

where $Z_{t_l}$, $Z_{t_r}$ are the left/right halves of $Z_t$, $V$ is a learnable matrix, $\odot$ indicates element-wise multiplication, $\mathcal{N}$ denotes normalization, and $b'$ is a bias. 3. Fuser: The fusion layer concatenates the bypassed head and contextualized tail, contracting via another linear projection:

$f(Z) = [Z_h \Vert c(Z_t)] O$

maintaining the original embedding dimensionality.

The pipeline enables Avey to focus only on the most contextually relevant splits, achieving high efficiency and modeling power regardless of sequence length.

2. Decoupling of Sequence Length and Context Width

The architecture’s defining innovation is its strict separation between the length of the input sequence $N$ and the context width $C$ (number of tokens processed by the neural processor in each step). Unlike attention-based models, where context and sequence are tightly bound and scaling becomes quadratic in $N$, Avey's ranker selects top‑$k$ splits from an unbounded pool, modulating the actual data seen by the neural processor on each iteration.

• Implication:

This configuration enables Avey to recall and incorporate information from any point in arbitrarily long documents at constant per-token inference cost, with total inference complexity linear in $N$. It also supports robust extrapolation: models trained on 512-token windows generalize successfully to 64k-token sequences.

3. Empirical Performance and Benchmark Results

Experimental results reveal Avey’s performance profile compared to Transformer++ and recurrent alternatives (Mamba, RWKV-7):

• Short-range Benchmarks:

On ARC, HellaSwag, PIQA, OBQA, SIQA, and Winogrande, Avey achieves competitive or superior accuracy in small and medium parameter settings, though slightly underperforms in some large-scale regimes.

• Long-range Dependency:

On S-NIAH (Needle-in-a-Haystack, Sequence-Needle-in-a-Haystack) tasks, Avey substantially exceeds Transformer++ and rivals in ability to locate and utilize tokens located far outside its trained context window. At 64k-token haystacks, Avey’s recall far outpaces its competitors.

• Latency:

Time-to-first-token (TTFT) measurements confirm Avey’s lower latency per token and overall linear scaling, in contrast to Transformer’s quadratic cost.

4. Mathematical Formulation and Algorithmic Details

Avey’s algorithmic structure is characterized by a sequence of deterministic, learnable projections and dynamic gating steps:

Subunit	Mathematical Formula / Operation	Description
Enricher	$Z = \sigma(X U + b)$	Position-wise feedforward projection, splits embedding
Contextualizer	$c(Z_t) = Z_{t_l} \odot \sigma(\cdots)$	Embedding-wise, dynamic, cross-token contextualization
Fuser	$f(Z) = [Z_h \Vert c(Z_t)] O$	Concatenates enriched head and gated tail, projects to output

The ranker’s MaxSim selection is based on the sum of each token’s maximal cosine similarity with candidate splits:

$$s = \sum_{i=1}^{C} \max_{j \in \text{past splits}} \cos(x_i, x_j)$$

Top‑$k$ splits by $s$ are selected, normalized, and used for context.

5. Applications and Use Cases

Avey demonstrates utility across domains requiring very long context understanding, beyond NLP:

• Conversational AI: Modeling extended dialogue histories.
• Document Summarization: Handling legal, scientific, or technical texts with extensive dependencies.
• Edge Deployments: Lower TTFT and reduced inference latency for real-time systems.
• Long-range Modeling: Tasks where “needle-in-haystack” token search is critical.

The architecture’s strong extrapolation and selective contextualization suggest its adaptability to a range of real-world scenarios where traditional fixed-context or quadratic scaling models are limiting.

6. Limitations and Future Directions

Identified constraints in the current instantiation include:

• Training Complexity: Training complexity remains quadratic ($\mathcal{O}(N^2 d)$) due to ranker pairwise comparisons. Linear complexity is achieved only during inference.
• Modal Extension: To date, only autoregressive language modeling has been evaluated; no implementations for bidirectional context, vision, or speech modalities have been reported.
• Implementation Efficiency: The current system lags behind optimized Transformer codebases in raw throughput; further engineering is required to match or surpass baseline efficiency.
• Research Directions: Future work will explore bidirectional pre-training, adaption to broader modalities, reduction of quadratic training overhead, and strategies for balancing context window sizes with model capacity.

A plausible implication is that Avey’s paradigm may stimulate development of architectures that combine selective, rank-based contextualization with neural modularity, potentially bridging gaps between scalability, adaptability, and long-range dependency modeling.

7. Position within Neural Architecture Paradigms

Avey Architecture signifies a distinct shift in foundational neural modeling, diverging from both attention-dominant (e.g., Transformer family) and strictly recurrent forms. By structurally decoupling sequence length and context processing, and by utilizing explicit ranking and dynamic enrichment, Avey offers competitive performance for both standard and challenging NLP benchmarks, particularly excelling in latency and long-sequence generalization (Hammoud et al., 12 Jun 2025). The architecture’s novelty lies not in incremental improvement over attention or recurrence, but in establishing an alternative pathway for scalable, context-sensitive neural computation suitable for high-demand sequence processing tasks.