17 August 2026

Smaller AI models gain reasoning ability through new memory techniques

• Researchers found that smaller models, including one with 150 million parameters (basic building blocks), can solve harder problems by using latent-space reasoning and memory, which lets them work through problems internally.
• A system called GPT-5.6 Sol demonstrated that compressing reasoning steps into memory acts as a capability multiplier, meaning it makes models substantially more capable without making them physically larger.
• This suggests model capability depends not just on size but also on how a model stores and uses information during thinking, opening a separate path to improvement.

How it was covered

Latent Spaceswyx & Alessio

Small models using latent-space reasoning and memory (like 150M model on ARC-AGI) and GPT-5.6 Sol with reasoning compaction show memory strategy as capability multiplier. The newsletter presents this as a separate scaling dimension beyond traditional model size.