Adaptive Multi-Agent Architectures
Multi-Agent SystemsA framework for dynamically reconfigurable agent swarms — enabling task-specialised topologies to emerge from continuous coordination gradients rather than static orchestration.
working paperLaboratory · Focus areas
Research, engineered
with rigor.
Our research program is organised around four primitives. Each compounds on the last — converging toward a coherent substrate for autonomous computation.
Focus areas
Multi-Agent Systems
Decision Intelligence
Scalable Infrastructure
Continual Learning
Causal Reasoning
Compositional Planning
Self-Reflective Models
01 — Selected research
Working papers &
frameworks.
Internal working documents distilled from ongoing experiments. Full manuscripts available under collaboration agreements.
Context-Aware AI Systems
Decision IntelligenceWe formalise a class of agents that reason over layered environmental context — integrating episodic memory, operational state, and goal invariants into a unified decision substrate.
working paperScalable Runtimes for Autonomous Agents
InfrastructureA runtime specification for elastic agent populations — cold-start constraints, deterministic replay, and resource-aware scheduling across heterogeneous compute fabric.
working paperRequest access — manuscripts shared under collaboration agreements only.
02 — Methodology
Claims must survive
controlled replay.
I.
Hypothesise
Architectural claims are stated formally, with falsifiable predicates and measurable constraints.
II.
Instrument
We build purpose-made simulators and runtimes; every claim must survive controlled replay.
III.
Harden
Findings are hardened into production-grade primitives only after cross-domain ablation.
Four primitives ✳ One coherent research thesis
Collaborate
Research is a conversation.
We co-develop autonomous systems with a narrow set of aligned partners. Full manuscripts and replay environments are shared under collaboration agreements.