Knowledge Base - OBSESSLABS | OBSESSLABS

Post-Transformer Architectures

While the Transformer architecture has dominated for years, the next phase of LLM development is looking toward more efficient state-space models (SSMs) and hybrid architectures that offer infinite context windows and faster inference.

1. Beyond the Context Window

Current models struggle with extremely long documents. Future LLMs will likely utilize "active memory" that works more like a human brain, selectively forgetting irrelevant details while maintaining core context across millions of tokens.

2. Small Langauge Models (SLMs)

The "bigger is better" era is slowing down. We are seeing a surge in specialized models like Phi-3 or Mistral-7B that can outperform GPT-4 on specific code or math tasks while running locally on a mobile device or laptop.

STRATEGIC TIP: Don't build your infrastructure around a single massive model. Build a "Model Router" that selects the cheapest, smallest model capable of solving each specific sub-task.

3. Multimodal Convergence

LLMs are becoming LMMs (Large Multimodal Models). Native integration of vision, audio, and video at the kernel level—rather than via external wrappers—will allow for deeper spatial reasoning and intuitive human-computer interaction.