Publications
State-of-the-Art AI Memory Across All Scales
Our cognitive retrieval research, in collaboration with Exabase, powers the only memory system to hold the top score on both major benchmarks at every evaluated scale.
Memory Is an Unsolved Research Problem That Model Scaling Cannot Fix
Context windows are getting bigger. AI memory is not getting better. Why memory requires dedicated research, not larger models.
Cognitive Retrieval and the Architecture of Memory
Approaching long-term memory as a cognitive process
Research
Hyperplane Labs develops foundational infrastructure for AI systems that can understand, learn from, and operate within complex environments, unlocking machine-human collaboration.
Memory Systems
Current AI systems lack persistent, contextual memory. We research architectures that allow AI to maintain and reason over long-term context while preserving privacy and computational efficiency.
Secure Agent Runtimes
As AI agents become more autonomous, secure execution environments are critical. We develop runtime systems that enable AI agency while maintaining strict security boundaries and user control.
Human-Level Document Understanding and Perception
We research novel and robust multi-modal extraction and parsing systems for complex documents, turning unstructured, rich and varied data into structured, machine-readable outputs. Our focus is production-grade accuracy and human-level perception that enables reliable downstream AI applications while preserving privacy and handling real-world document complexity.
Reinforcement Learning Environments
AI agents require rich, persistent environments to learn complex behaviors. We research RL environments that enable agents to develop genuine understanding through interaction with realistic, long-horizon tasks in personal computing contexts.
Company World Model
AI research exploring how companies actually operate and developing a model that thinks like an entire company as a single autonomous actor.