AI Post Transformers · Interactive Visualization
Atlas: Test-Time Memory for Long Contexts
Atlas asks whether a model with fixed recurrent memory can learn to write better during inference and rival long-context Transformers. The page below keeps the text light and pushes the tradeoff into diagrams: exact token access versus bounded state, single-token writes versus windowed writes, and promising gains versus an information bottleneck that never fully disappears.
Reference Trail
Academic citations below link to arXiv when an ID is known. The transcript text provided here does not add any new numeric arXiv IDs beyond the known source paper.
Data note The charts use realistic illustrative values shaped by the episode narrative, not copied benchmark tables.