Evaluating the Applicability of LLM Inference Optimizations on Apple Silicon
On-Device Intelligence: Foundation Models under Real-World Constraints (ODI) @ NeurIPS 2026
Advancing Mac-native
intelligence.
MacPaw Research is a research department at MacPaw, focused on LLM efficiency, AI memory, and human–computer interaction - turning findings into the Mac products people use every day. Papers, tech notes, and highlights, published as we go.
Research highlights
Recent publications
Explore all publicationsRigid 3D Object Alignment: Optimization vs. Feed-Forward Prediction
7th Workshop on Open-World 3D Scene Understanding and Representations (OpenSUN3D) @ ECCV 2026
GenRA: End-to-end Framework for LLM-Driven Animation of Rigged 3D Models
7th Workshop on Open-World 3D Scene Understanding and Representations (OpenSUN3D) @ ECCV 2026
Memory Belongs to the User: Portable Memory, an Open Standard Proposal for Cross-Vendor AI Memory
MacArena: Benchmarking Computer Use Agents on an Online macOS Environment
2nd AIWILD Workshop @ ICML 2026
Research partners
University collaborations driving co-authored work, internships, and our long-term talent pipeline.
- Cambridge, MA
MIT
Internships and joint research with CS students on efficient on-device inference and applied ML.
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- Kyiv
Kyiv-Mohyla Academy
Faculty of Informatics - joint applied-AI research, summer schools, and a steady stream of Kyiv-rooted ML engineers.
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- Lviv
Ukrainian Catholic University
Faculty of Applied Sciences - joint research, internships, and a steady talent pipeline.
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- Kyiv
KPI Igor Sikorsky Polytechnic
Home of the MacPaw AI Lab "Bilka Space" and a MacPaw-funded scholarship for applied-ML students.
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Open to collaboration
Build Mac‑native intelligence together
We partner with universities, research labs, and industry teams on frontier AI - on-device runtimes, LLM efficiency, AI memory, and human–computer interaction. If your work intersects with ours, we'd love to hear from you.
Email the team [email protected]