M:3L research
M:3L develops autonomous, tool-using single-agent and multi-agent AI systems for scientific, engineering, and enterprise workflows.
The laboratory combines large language models with retrieval-augmented generation, semantic search, persistent memory, and tools exposed through Model Context Protocol (MCP) servers and APIs. These systems support grounded investigation, enterprise automation, and orchestration of simulation and optimization workflows.
Engineering and scientific agents are a central direction. M:3L studies workflows in which agents can coordinate CAD or CAE tools, numerical simulation, finite element models, optimization routines, model validation, and iterative design while retaining explicit feedback and verification steps.
M:3L also partners with hackathon.ngo on BitGn PAC, a global challenge focused on autonomous and trustworthy AI agents with safety, reliability, and trust at the core.
M:3L treats agentic engineering as the orchestration of specialized tools and models rather than as a text-only assistant. A workflow may connect CAD and CAE systems, numerical or FEM simulation, optimization algorithms, engineering constraints, and model-validation steps.
This is an active convergence of the laboratory's capabilities. It should not be interpreted as a claim that every component is already deployed as one finished production platform.
These are relevant application domains, not claims that every application is deployed.
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 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).