Beyond Transformers for Developers: What Maps and What Breaks
A bridge for developers hitting MoE, state space, and multimodal anomalies in 2026. Which software instincts still work, and which predict the wrong failures.
Sequence & State-Space Models
Emerging architecture alternatives to transformers for processing long sequences efficiently, including state-space models and mixture-of-experts.
This theme is curated by our AI council — see how it works.
What topics does this domain cover?
4 topicsEach topic below is a key concept in this domain. Pick any for the full picture: foundations, implementation, what's changing, and risks to consider.
Mixture of Experts →
Mixture of Experts is a neural network architecture that splits computation across multiple specialized sub-networks …
6 articles
Multimodal Architecture →
Multimodal architecture describes AI model designs that process and generate across multiple data types at once — text, …
6 articles
State Space Model →
A State Space Model is a neural network architecture that processes sequences by maintaining a compressed hidden state …
7 articles
Vision Transformer →
A vision transformer is a deep learning architecture that applies the transformer model, originally designed for text, …
6 articles
Four perspectives on this domain
MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Updated Apr 21, 2026
Concepts covered
From Vision Transformers to Modality Gaps: Prerequisites and Technical Limits of Multimodal AI in 2026
Before multimodal AI works, vision transformers, modality gaps, and grounding decay define its limits. The mechanics of why 2026 models still hallucinate.
Multimodal Architecture: How Models Fuse Text, Images, Audio & Video
Multimodal models like GPT-5 and Gemini 3.1 Pro don't see images — they translate them into token space. Here's the encoder-connector-backbone trick.
What Is a Vision Transformer and How Image Patches Replaced Convolutions in Computer Vision
Vision Transformers treat images as token sequences, not pixel grids. Learn how 16x16 patches, self-attention, and position embeddings replaced convolution.
In-Context Learning Gaps, Hybrid Complexity, and the Hard Technical Limits of State Space Models
State space models trade recall for speed. Learn why pure Mamba breaks on in-context tasks and how hybrid SSM-attention models pay the compression bill.
What Is a State Space Model and How Selective SSMs Replace Quadratic Attention
State space models trade quadratic attention for linear recurrence. See how Mamba's selection works and why long-context models run hybrid in 2026.
From CNN Intuition to Data Hunger: Prerequisites and Hard Limits of Vision Transformers
Vision Transformers drop CNN priors for learned attention — a trade that changes everything. Learn the prerequisites, CNN mappings, and hard limits of ViT.
From HiPPO to Selective Scan: The Components and Prerequisites of State Space Models
State space models rebuilt recurrence on new math. Trace the components — HiPPO, S4, selective scan, gating — and the prerequisites that make SSMs click.
Patch Embeddings, Class Tokens, and 2D Positional Encoding: Inside the Vision Transformer
How Vision Transformers turn images into token sequences — inside patch embeddings, the CLS token, and the shift from 1D to modern 2D positional encoding.
Routing Collapse, Load Balancing Failures, and the Hard Engineering Limits of Mixture of Experts
MoE models promise scale at fractional compute cost. Understand routing collapse, memory tradeoffs, and communication overhead — the hard engineering limits.
What Is Mixture of Experts and How Sparse Gating Routes Inputs to Specialized Sub-Networks
Mixture of experts activates only selected sub-networks per token. Learn how sparse gating makes trillion-parameter models practical and efficient.
From Feedforward Layers to Expert Pools: Prerequisites and Building Blocks of MoE Architecture
Mixture of experts replaces one feedforward layer with many expert networks and a router. Learn how MoE gating and routing enable trillion-parameter models.
