Capability Conditioned Scaffolding for Professional Human LLM Collaboration
Quick Answer
The paper introduces Capability Conditioned Scaffolding, a framework that enhances AI collaboration by adapting to user expertise levels across domains.
Quick Take
It addresses Professional Domain Drift by implementing structured capability profiles, showing effective intervention behaviors in mixed domain risk zones during pilot evaluations on subsets. This approach aims to improve reliability in professional human-AI interactions beyond mere stylistic adjustments.
Key Points
- Introduces a framework for adapting AI outputs based on user expertise levels.
- Addresses Professional Domain Drift by recognizing varying evaluation capacities.
- Pilot evaluations show effective intervention behaviors across MMLU subsets.
- Demonstrates categorical inversion and selective activation in mixed domains.
- Aims for reliable human-AI collaboration beyond stylistic personalization.
Paper Resources
📖 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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