We are partnering with Yerevan State University's Physical-Technical Laboratory and Department of Radiophysics and Telecommunications to build a digital engineering platform for virtual system modeling and diagnostics. The platform will create digital twins of real experimental systems that scientists can query in plain language for fast, accurate answers, powered by machine-learning models trained on thousands of physics simulations. The project is funded by the Higher Education and Science Committee of RA MESCS, with co-financing from the Enterprise Incubator Foundation.
Our team joined METAMAT2026 in Ajaccio, Corsica, France. David Aznaurov presented ongoing work with Imperial College London on machine-learning methods for mechanical structure generation and elastic wave control, alongside five days of exchange on acoustic, mechanical, and thermal metamaterials.
M:3L has received access to NVIDIA H100 GPUs through the AI Virtual Institute, a program of Armenia's Ministry of High-Tech Industry. This support expands our capacity to design elastic metamaterials and wave-cloaking structures. The research combines differentiable FEM, neural-field optimisation, and generative design to create manufacturable microstructures for broadband Rayleigh-wave cloaking and chiral metamaterials.
M:3L Lab is partnering with hackathon.ngo to host BitGn PAC. BitGn PAC is a global challenge focused on building autonomous, trustworthy AI agents. The challenge brings together researchers and engineers to push the boundaries of agentic AI systems with safety, reliability, and trust at the core.
M:3L co-authored Zoo3D, accepted at CVPR 2026, an A*-ranked conference. The work introduces the first training-free, open-vocabulary 3D object detector: it clusters 2D instance masks across views into class-agnostic 3D boxes, then labels them through best-view selection and view-consensus visual-language matching. Zoo3D supports point clouds as well as posed or unposed RGB images, while its zero-shot and self-supervised variants deliver leading results on ScanNet200 and ARKitScenes.
M:3L co-authored TUN3D, accepted at ICRA 2026, an A*-ranked conference. It is the first system to jointly estimate room layouts and detect 3D objects in real scans from multi-view images without ground-truth camera poses or depth supervision. A lightweight sparse-convolutional backbone feeds dedicated detection and parametric wall-layout heads, enabling one model to work with point clouds, posed images, or fully unposed RGB captures and substantially advancing holistic indoor-scene understanding.
M:3L co-authored cadrille, accepted as an Oral at ICLR 2026, an A*-ranked conference. The model turns point clouds, multi-view images, or text descriptions into editable Python-based CAD programs within a single vision-language architecture. It combines supervised fine-tuning on large-scale procedurally generated designs with online reinforcement learning driven by programmatic feedback, improving geometric accuracy and code validity and achieving leading results across DeepCAD, Fusion360, CC3D, and Omni-CAD benchmarks.
M:3L is developing a foundational event-embedding model that jointly captures event meaning, temporal progression, and outcome/value signals. The work targets time-sensitive retrieval and agent memory across domains, supporting anomaly and churn detection, next-action and value prediction, causal and counterfactual analysis, and grounded conversational investigation through a Semantic Vector MCP. This work was conducted within the scope of the Faculty Research Funding Program 2025 of the Enterprise Incubator Foundation (EIF).