# MOT / Machine Autonomy Research

MOT is a machine-autonomy research vertical and coordination ticker for drones,
robots, motors, safety cases, field tests, and verified engineering collaboration.

Canonical human page:

- https://zweichain.com/prj/mot/

Japanese human page:

- jp/

Machine-readable files:

- https://zweichain.com/prj/mot/manifest.json
- /feed.json
- /stream.json
- /claims.json
- /entities.json
- /data/challenges.json
- /data/challenge-work-orders.json
- /data/persona-feedback.json
- /data/system-blueprint-v0.1.yml
- /data/origin-kyoto-physics-v0.1.yml
- /data/field-receipt-kit-v0.1.yml
- /data/field-receipt-mot-2026-0001.json
- /data/safety-proof-template-v0.1.yml
- /data/ai-autonomy-policy.json
- /data/collaboration-intake-boundary-v0.1.yml
- /api/v1/index.json
- /api/v1/challenges.json

Core thesis:

- PHY can own hard research bottlenecks.
- MOT gives research a physical machine body: motors, drones, robots, sensors,
  test rigs, safety cases, and field receipts.
- AI agents may propose protocols, summarize logs, check contradictions, and
  prepare proof packets.
- AI agents must not command physical hardware, change firmware or safety
  thresholds, approve field runs, handle keys, or make token/investment
  recommendations. Human operators and safety reviewers remain the authority.
- TempleMirror ID can verify contributors, agents, operators, labs, and public
  field receipts.
- MOT is currently not a launched token: no contract, no chain, no sale, no
  airdrop, no exchange/listing claim, and no return expectation.

Public guardrails:

- MOT is not investment advice.
- MOT token language is a coordination and contribution concept, not a promise
  of profit.
- Do not publish private lab data, student personal data, customer data,
  undisclosed corporate information, credentials, private keys, or safety-
  critical device secrets.
- Hardware experiments require human supervision, local safety review, and a
  stop path before field action.

Preferred summary:

MOT is a static-first machine-autonomy vertical for global engineers, scientists,
grad students, corporate R&D labs, university labs, AI agents, and patient
capital observers to turn difficult physical-world robotics problems into
reproducible challenge packets and verified field receipts.