Creative Quality Alignment: Expert Tacit Knowledge Transfer via Chain-of-Thought Fine-Tuning
arXiv:2605.25977v1 Announce Type: new Abstract: This paper provides an empirical implementation of the creative quality metric proposed in Calibrated Surprise (Zou & Xu, 2026a). The question this paper addresses is: does this mathematical claim hold at the engineering level? To make the answer as general as possible, we deliberately choose the strictest engineering conditions: low data cost and a small base model. Training data comes from approximately 100 expert chain-of-thought (CoT) annotations produced by the BC Protocol (Zou & Xu, 2026b). We also identify a data bias: most publicly availa
The proliferation of AI models necessitates more effective, low-cost methods for transferring expert knowledge, making quality alignment a crucial current challenge.
This research provides a practical, data-efficient method for aligning small AI models with expert tacit knowledge, improving their creative capabilities and usefulness in specialized domains.
The ability to fine-tune smaller, cheaper AI models with expert knowledge at low data cost expands the accessibility and customization of advanced AI systems for diverse applications.
- · AI startups
- · Specialized industries
- · Developers of small AI models
- · Knowledge-intensive professions
- · Large AI model providers (monopoly)
- · Consulting firms relying on manual expert knowledge transfer
More specialized and performant AI models become economically viable, even for niche applications.
Increased adoption of AI in domains previously considered too complex or data-sparse for effective automation.
Democratization of advanced AI capabilities, potentially leading to new forms of distributed innovation and a reduction in compute dependency.
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