DeepHiveMind original emblemDeepHiveMind.

RESEARCH LABS / 03

Focused teams.
Shared direction.

Working groups form around specific questions, deliverables and contributors. They are designed to stay small enough to work and open enough to learn.

01

RESEARCH COLLECTIVE

Understand the frontier.

Research questions, model evaluation, responsible AI methods, reproducible experiments and technical publications.

  • Literature reviews and research briefs
  • Model and benchmark evaluation
  • Safety, robustness and responsible AI
OUTPUTSResearch notesEvaluation reportsTechnical papers
02

BUILDER COLLECTIVE

Turn ideas into working systems.

Agentic AI, multi-agent systems, GenAI engineering, RAG, AI infrastructure and open-source development.

  • Reference architectures and prototypes
  • Reusable components and evaluations
  • Open-source contributions
OUTPUTSPrototypesCode repositoriesArchitecture notes
03

INDUSTRY INNOVATION LAB

Connect intelligence to industry.

Industrial AI, smart manufacturing, robotics, IIoT, digital twins and Industry 5.0.

  • Industry problem framing
  • Applied experiments and demos
  • Human-centred industrial innovation
OUTPUTSUse-case studiesDemonstratorsApplied research
04

KNOWLEDGE & LEARNING NETWORK

Make expertise transferable.

Expert sessions, technical roundtables, research exchange, mentorship and collaborative learning.

  • Peer-led deep dives
  • Roundtables and reading groups
  • Mentorship and learning pathways
OUTPUTSSession notesLearning resourcesShared playbooks

Working groups are a proposed operating structure for DeepHiveMind 2.0. Their activation, membership and deliverables should be announced only after a founding group confirms ownership.