Research
Research on AI memory and databases.
It started with machines that have human-like memory, built with AI and knowledge graphs. It now covers databases too, multi-model ones in particular. The papers, code, and data are all public.
Research
Papers and the thesis.
Machines With Human-Like Memory
A PhD research project on explicit memory architectures for AI, spanning benchmarks, handcrafted agents, learned policies, and temporal knowledge-graph memory.
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RoomKG Meta-Policies
A neuro-symbolic meta-policy that learns which symbolic memory heuristic to apply for answering, exploring, and forgetting, while keeping execution symbolic.
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Co-Learning
Reusing prior human-robot collaboration patterns as knowledge-graph episodic memory to improve early teamwork in USAR.
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KG Memory Transfer
A learned keep-drop transfer policy for long-term knowledge-graph memory under partial observability.
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RoomKG Baselines
A neurosymbolic benchmark for temporal knowledge-graph memory under partial observability.
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ArcadeDB
A multi-model database engine that keeps documents, graphs, vectors, and time series in one transactional engine, plus the Python package that runs that engine inside your process, benchmarked against the comparators in each category.
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Explicit Memory
A reinforcement-learning agent with explicit short-term, episodic, and semantic memory in RoomEnv-v1.
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Human-Like Memory Systems
A paper and codebase introducing RoomEnv-v0 and heuristic agents with explicit episodic and semantic memory.
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Reproduce it, or work on it with us.
The papers, code, and data behind each result are public. Open an issue when you find a mistake, or reproduce a result on your own hardware and tell us what you measured.
Research collaborations are welcome, on memory for agents, knowledge graphs, or databases.