AI support of collaborative interactions entails mediating potential misalignment between interlocutor beliefs.Common preference alignment methods like DPO excel in static settings, but struggle in dynamic collaborative tasks where the explicit signals of interlocutor beliefs are sparse and skewed.We propose the Frictional Agent Alignment Framework (FAAF), to generate precise, context-aware "friction" that prompts for deliberation and re-examination of existing evidence.FAAF's two-player objective decouples from data skew: a frictive-state policy identifies belief misalignments, while an intervention policy crafts collaborator-preferred responses.We derive an analytical solution to this objective, enabling training a single policy via a simple supervised loss.Experiments on three benchmarks show FAAF outperforms competitors in producing concise, interpretable friction and in OOD generalization.By aligning LLMs to act as adaptive "thought partners"-not passive responders-FAAF advances scalable, dynamic human-AI collaboration.Our code and data can be found at https: //github.com/csu-signal/FAAF_ACL.

Monitoring soil health at scale requires tools that are both scientifically robust and computationally efficient. Accurate and efficient estimation of soil chemical properties is essential for sustainable agricultural practices and environmental management. Traditional laboratory-based soil testing methods are labor-intensive, time-consuming, and often impractical for large-scale analysis. Although mid-infrared (MIR) spectroscopy offers a rapid and cost-effective alternative, conventional modeling techniques suffer from inconsistent predictive performance across diverse soil properties. To address this gap, we introduce FTIRNet, a novel multi-task Transformer-based architecture designed to predict multiple soil chemical properties from Fourier-transform infrared (FTIR) spectral data. FTIRNet employs a shared encoder block with task-specific Transformer encoders, enabling the model to learn both general and task-focused spectral representations. These multi-task Transformer encoders incorporate science-informed task relation learning through specialized fusion layers that capture known biogeochemical interdependencies among soil properties. Across a broad set of soil properties, FTIRNet demonstrates consistently superior predictive accuracy compared to traditional approaches.