CR-Stream · model merge real C engine
Collective merge demo
Separate workers train private model files. A content merge creates one collective model.
The collective derives a new answer that neither worker can produce alone, with no retraining and no raw data exchange.
Live sequence
real C engine1
Train worker AB on AExamples induce the grandparent rule.
2
Add B to worker ABSame model learns Ralph without forgetting.
3
Train worker C on CC knows a private bridge: Homer -> Maggie.
4
Merge the model filesCollective = worker AB + worker C.
5
Ask every modelOnly the collective should derive Maggie.
Ready.
worker AB
worker C
collective
The cross-worker fact
grandparent(abe, ?)worker AB has Abe -> Homer and learns the family rule.
worker C has Homer -> Maggie but cannot answer Abe questions alone.
collective joins facts across both model files after merge.
Expected proof: Abe is Homer's parent, and Homer is Maggie's parent, so Abe is Maggie's grandparent.
Answers
derived vs storedRun the story, then this area shows each model's answer and proof.
1. Decentralized training network
Independent workers train shards locally, submit model files, and can be credited when their shard improves the collective model.
worker shards -> merge queue
creditable contributions -> collective model
creditable contributions -> collective model
2. Community-owned model
A community can teach a shared model its lore, facts, memes, and behavior over time without forcing every contribution through one central dataset.
member models -> shared character
memes, lore, domain facts -> collective memory
memes, lore, domain facts -> collective memory
3. Corporate training
Offices keep local data local. Periodic merge lets headquarters or peer offices answer questions whose proof crosses teams.
office A + office B + office C
periodic merge -> one auditable model
periodic merge -> one auditable model