Advancing the frontier
of machine intelligence.
Our research spans three core areas, each pushing the boundaries of what autonomous systems can perceive, reason about, and act upon.
Neural Architectures
Traditional neural networks process information in fixed, sequential layers. We are exploring directed acyclic graph (DAG) based architectures that allow information to flow through non-linear, branching pathways, enabling more flexible and efficient reasoning.
Our work focuses on topologies that support parallel processing streams, selective attention routing, and dynamic depth, where the network allocates more computation to harder problems and less to simpler ones.
Edge Inference
Deploying large models to resource-constrained environments remains one of the most significant challenges in applied AI. Our research in edge inference focuses on bringing powerful model capabilities to devices with limited compute, memory, and power budgets.
We investigate quantization strategies, model distillation, and hardware-aware optimization techniques that maintain model quality while dramatically reducing inference latency and resource consumption. The goal: sub-millisecond inference on commodity hardware.
Multi-Agent Orchestration Layers
Single-agent systems hit fundamental limits when tasks require diverse expertise, parallel execution, or long-horizon planning. Our research in multi-agent orchestration explores hierarchical frameworks where specialized agents coordinate to solve complex, multi-step problems.
We are developing protocols for agent-to-agent communication, recursive planning loops where agents decompose tasks and delegate to child agents, and authority hierarchies that ensure safe, aligned behavior across autonomous agent swarms. This includes tool use, memory management, and real-time coordination across heterogeneous model backends.
Interested in our research?
We are open to collaborations with researchers and institutions working on related problems.
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