Yinglei Ma

Sen Yang, Yinglei Ma

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.

Yinglei Ma, Fei Xiao

BJT-based 2D temperature-sensor arrays are factory-calibrated to +/-0.1 degC, but post-deployment thermal and mechanical stresses drift their per-sensor gain-offset parameters by an order of magnitude, and in-lab recalibration is impractical. We present RASC (Region-Aware Self-Calibration), a five-stage algorithm that decomposes the global ill-posed problem into local cluster-level problems, runs robust alternating estimation (trimmed-mean field reconstruction + Huber IRLS) inside each cluster, and reconciles overlapping estimates by linear consensus on the cluster-overlap graph with provable exponential convergence. On 7,632 frames from a deployed 16x16 array exhibiting ~5x factory-spec non-uniformity, RASC cuts the locally-non-smooth fixed-pattern residual by 71+/-5% (10-fold CV), restoring +/-0.1 degC accuracy while perturbing the calibrated field by only 0.041 degC RMSE; reduction concentrates at the edges (78% vs 55% interior). In simulations on 8x8 to 32x32 arrays, RASC matches an oracle centralized EKF within 0.10 degC with ~4x lower bandwidth.