Mission & Vision

The Proving Ground

The Tribunal is an orchestration framework for autonomous multi-agent decisions. Financial markets are the proving ground — not because finance is the end goal, but because markets are unforgiving. There is no partial credit. The outcome either confirms the decision or it doesn’t.

High-frequency, unambiguous feedback makes markets the ideal environment to stress-test a decision architecture. Every cycle produces thousands of labeled training examples with market-verified outcomes. No human annotator involved.

What We Are Actually Building

The product is not a trading strategy. The product is the orchestration layer — the set of mechanisms that let specialized agents deliberate, vote, override, and learn from each other over time.

The core properties of that layer:

Role separation, not coordination. Knights never see each other’s votes. The King sees all of them, plus the portfolio state, plus the risk envelope. Each role has authority over exactly its domain and nothing else.

Earned seats. No agent holds a permanent position. Performance gates determine who advances. The market decides who stays. An agent that cannot demonstrate genuine signal diversity from its counterparts is a liability, not an asset.

Institutional memory. The Canon survives model replacement. When a fine-tuned model is superseded by the next generation, the verified decisions it made — the patterns that proved out — are absorbed into the training corpus for its successor. The institution learns even as individual models are retired.

Natural selection over hand-tuning. The long-term architecture replaces manual promotion decisions with a challenger system: candidates run against incumbents on held-out regime data. The market, through the Prophet’s ledger, determines the winner.

The Canon

The Canon is the written form of institutional memory. It holds verified strategic knowledge in a form that does not depend on any particular set of model weights.

When a model forgets — and language models do forget, especially under continuous fine-tuning — the Canon is the record it returns to. The Prophet reads it. The King is briefed from it. The Scribe maintains it.

The Canon grows only when the Python truth layer agrees. An LLM observation cannot write to the Canon unilaterally. Computed outcome is law; advisory interpretation is context. This prevents the system from encoding confident mistakes into institutional memory.

The Vision: Ancestral Tribunals

The current system is Phase 2 of a longer arc. The long-term vision is Ancestral Tribunals — each generation of the tribunal trained on the verified decisions of its predecessors.

The weights carry accumulated wisdom. The model trained on Cycle 20 data is not just better than the base model — it has inherited the institutional judgment of every cycle before it. That judgment compounds across generations rather than resetting with each retraining.

The end state is a self-governing system: Knights earn promotion to Kings through demonstrated performance. Kings earn replacement or succession through the same process. The tribunal runs in threes all the way down — every layer of the hierarchy replicates the structure of the layer below it.

No single model decides. No single cycle is the answer. The architecture is the answer.

Current Phase

The Tribunal is in Phase 2: three trained knights (Momentum, Flow, Trend) voting independently, a King synthesizing under uncertainty, and a Prophet in silent observation — recording but not advising.

The immediate objectives:

Demonstrate genuine vote diversity across knight roles (not prompt-differentiated clones)
Build the Prophet’s observation corpus to a size where macro context becomes meaningful for King training
Establish sovereign accuracy as the King’s maturity metric — the system is working when the King’s overrides outperform knight consensus

The public surface shows orchestration, operational maturity, and inference readiness. The strategy stays protected.