Single-LLM self-reflection has a structural blind spot — the model tends to confirm its own output. QuantGPT enforces a hard rule in factor-mine SKILL Phase 0.5: Claude must consult DeepSeek before designing a new factor family. Not a suggestion, a hard rule. This isn’t redundancy — it’s the antidote to structural bias.
When the system operator changes from a human to an LLM Agent, design principles need fundamental rethinking. Humans need GUIs and documentation. Agents need semantically clear tools and constraints that throw errors.
While everyone is building Agent frameworks, I chose a different path: build infrastructure for Agents, not the Agent itself. Not because I can’t build Agents, but because the Agent layer is a consumable — infrastructure is an asset.
Current AI Agent frameworks obsess over building complex loop chains: Planner → Executor → Reflector → Re-planner. I chose the opposite: tools are stateless pure functions, and the LLM decides the call sequence itself. Not because loop chains aren’t cool — but because they put decision authority in the wrong place.
QuantGPT’s core is an 870+ line hand-written recursive descent parser supporting 80+ operators, automatic cross-sectional/time-series grouping, and dual-mode compilation. Not because I didn’t know eval() is simpler — but because what eval() can’t do happens to be what matters most.
QuantGPT uses threading.local to enforce a runtime guard: all backtest calls must go through the API boundary. Direct function calls raise an exception. Not because the function is dangerous — but because a system without boundaries can’t be audited.
Most backtest systems treat anti-overfit as an optional add-on check — run the backtest, then test for overfitting if you feel like it. QuantGPT builds it into the scoring system and evolution engine: anti-overfit results directly affect factor scores, and the evolution engine reads anti-overfit metrics to decide its next strategy. Factors that haven’t proven robustness don’t even qualify for iteration.
Letting AI drive research workflows doesn’t mean letting AI decide how the system runs. I made a key separation: AI is just the operator, the execution engine is the law. This decision came from a failure.
Most systems are designed to run first, then audited as an afterthought. I inverted the order — first define what questions the system must answer when things go wrong, then work backwards to what each layer must record. This inversion reshaped the entire architecture.
Multi-agent is the hot paradigm in AI engineering. I chose single agent + state machine for my AI-driven quant research system. Not because multi-agent is too hard, but because the problem structure doesn’t match.