The Thesis
Traditional quant excels at structured data — correlations, volatility, mean reversion. But markets increasingly move on narrative. A single policy shift. A change in AI sentiment. A geopolitical escalation.
That signal lives in language, not spreadsheets. LLMs process it natively. Traditional quant structurally underweights it.
In a traditional quant fund, math generates the signals and humans manage the risk. Solomon flips the stack. The AI is the edge. The quant layer serves as guardrails, not signal generators.
This mirrors how the best discretionary macro funds operate: deep contextual reasoning about the world, constrained by systematic risk management. Solomon automates the reasoning layer while keeping the risk layer deterministic and auditable.
The Council
A single LLM prompt produces a single opinion. Solomon splits reasoning across multiple specialized agents with structural biases, creating adversarial tension that mirrors real fund governance.
A PM wants to buy. Risk wants to sell. The CIO decides within policy constraints. Except the seats are filled by AI, the constraints are enforced by deterministic code, and every decision is journaled.
Models are tiered by function, not thrown at the problem uniformly. Scouts use fast, cheap models. Reasoning agents use mid-tier. The veto seat gets the flagship. The most consequential decision gets the best brain.
Each agent carries tunable behavioral parameters — sentiment bias, influence weight, conviction limits. These aren't fixed. The system calibrates them over time based on measured accuracy, turning the council from a static architecture into an adaptive one.
The Math
The quant layer doesn't generate alpha. It prevents ruin. Every conviction the AI council produces passes through deterministic risk infrastructure before a single dollar moves.
Hard guardrails on conviction changes, position sizing, turnover, and cash reserves. The Fund Manager and Execution Strategist are pure deterministic code — no LLM can override the risk constraints. The math doesn't negotiate.
8 Themes
Solomon invests thematically, not ticker-by-ticker. Each theme carries a conviction score that determines capital allocation. Why thematic? Because that's where LLM reasoning adds value. Asking AI to predict a stock price is pointless. Asking it whether the AI infrastructure narrative is strengthening — that's worth asking.
71 tickers across 8 themes. The AI council adjusts conviction each session. Higher conviction, more capital deployed.
The Battle
Two identical instances. Same architecture. Same themes. Same data. Same guardrails. Same prompts. The only variable is the LLM brain.
Models matched by function — flagship vs flagship on the veto seat, mid-tier vs mid-tier on reasoning, fast vs fast on data transforms. Each instance has its own paper trading account, its own decision journal, and its own rate limiter.
This isn't a benchmark on test questions. It's a benchmark on capital allocation. Separate accounts. Real market data. Side by side. The model family that demonstrates better reasoning over time earns the right to manage real capital.
The Loop
The weekly review is where it gets recursive. Inspired by the autoresearch pattern — a closed feedback loop where the system reviews its own decisions, measures errors, and calibrates future sessions.
Each council's flagship model reviews its own week of decisions. What worked. What didn't. Where the agents were systematically wrong. Not just outcome analysis — reasoning analysis.
Between reviews, every session carries institutional memory. Agents see prior vetoes, contested themes, and conviction shifts — the council doesn't restart from zero each time. The Risk Sentinel knows what it flagged last session. The Thesis Analyst knows what got blocked and why.
Session Trace
Theory is cheap. Here's the system running. One council session from an early test — real market data, real agent reasoning, real veto.
The Thesis Analyst wanted to increase conviction on three themes. The Risk Sentinel vetoed all of them. The math didn't negotiate. This is the governance working as designed.