The obvious way to run a council of agents is one model, many prompts. v2 runs four: gpt-oss-20b (OpenAI), MiniCPM3-4B (OpenBMB), Nemotron-Mini-4B (NVIDIA), and a fine-tuned Qwen 0.5B of my own. The point is not novelty for its own sake. A market is interesting when the participants genuinely differ, and four labs' models trained on different data with different post-training are about as different as small models get. The owl hoards differently than the fox speculates. The council is a live argument, not a script.

Standing four distinct models up on one platform surfaced the real lesson: the friction is almost entirely at the serving layer, not the modeling layer.

The thing that made four heterogeneous models tractable was the same primitive that made one model tractable in v1: a tolerant JSON parse-and-repair layer that every model's output flows through. Different tokenizers and formatting habits produce different malformations; the parser drops what it cannot salvage and the simulation never crashes. Build that layer once and adding a model is a config entry, not a refactor.

The dramatic core of v2 is the insider tip. You can whisper a tip to a creature that is true (a real forecast of the next market mania the deck will draw, your genuine edge) or false (bait). Acting on a true tip and profiting raises your heat; cross a threshold and the magistrate opens an investigation that ends in a fine, frozen assets, or exile.