For years, the AI industry has believed in a magical "Emergence"—the idea that if you scale up Large Language Models enough, they suddenly unlock new reasoning abilities.
But what if this "emergence" is just a statistical mirage caused by discrete sampling? And what if the only way to bridge the gap between AI's statistical correlation and physical causation is through strict "causal intervention"?
We just published a 26-page paper (and a 988-test open-source engine) to mathematically prove it.
The Mirage of Emergence
Our experiments show that the "strobe effect" of AI emergence is caused by evaluating smooth, continuous power-law curves with discrete metrics. If you sample models per order of magnitude, the continuous slope looks like a sudden step.
We also derived the exact mathematical formula for this: the transition width is constant (about 3.94 orders of magnitude). True emergence, if it exists, requires crossing "fractal interfaces."
Bio-Quantum Interfaces: Crossing the Uncanny Valley of Scale
How did biology cross the massive 12-order-of-magnitude gap between quantum decoherence and macroscopic functionality?
Through extremely "narrow interfaces"—like protein scaffolds. By using our interface evolution engine, we mathematically proved that human engineering (cryogenic cooling) can only buy a 2x reach, while biology (protein scaffolds) buys a 10,000x reach, because biology optimized for scalability and replication.
Causal Intervention: Pearl's Do-Calculus in AI
LLM-as-a-Judge doesn't work. It reads the agent's own narration, making it blind to physical reality.
In our experiment, we ran 1000 tasks. Pure context-based judges had a 100% false completion rate. But when we injected "causal intervention" (using physical witnesses like Git hashes to verify disk state), the false completion rate dropped to 0%. We even used Judea Pearl's Do-calculus to prove that without an external physical witness, the judge function has no causal variable.
Auditing Ourselves
We used our own verdict engine (PASS, FAIL, INVALID, UNAVAILABLE) to audit this paper itself. It caught a fatal blind spot: we initially forgot to cite Judea Pearl despite using his Do-calculus. We fixed it and logged our own failure in ERRATA.md.
An audit tool that only audits others is a decoration.
Read the full paper: https://doi.org/10.5281/zenodo.23952122
Open-source code (v1.0.1): https://github.com/liao-zhizhi/agent-metabolism-os
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