Capability Conditioned Scaffolding for Professional Human LLM Collaboration
Quick Take
The study proposes Capability Conditioned Scaffolding to enhance AI collaboration by addressing user evaluation capacity across expertise domains.
Key Points
- Introduces a framework for partitioning expertise into strong, mixed, and weak domains.
- Demonstrates consistent intervention behavior based on structured capability profiles.
- Supports reliable human-AI collaboration beyond mere stylistic personalization.
📖 Reader Mode
~2 min readAbstract:Large language model personalization typically adapts outputs to user preferences and style but does not account for differences in user evaluation capacity across domains of expertise. This limitation can encourage Professional Domain Drift, where users rely on AI generated reasoning in domains they cannot reliably evaluate. We introduce Capability Conditioned Scaffolding, a typed framework that partitions expertise into strong, mixed, and weak domains and conditions intervention behavior on structured capability profiles. A pilot evaluation across multiple MMLU subsets and four LLM substrates shows consistent profile conditioned intervention behavior, including categorical inversion under profile swapping and selective activation in mixed domain risk zones. These findings suggest that capability aware scaffolding can support more reliable professional human AI collaboration beyond stylistic personalization.
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.15404 [cs.CL] |
| (or arXiv:2605.15404v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.15404 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sen Yang [view email]
[v1]
Thu, 14 May 2026 20:42:03 UTC (559 KB)
— Originally published at arxiv.org
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