Motivation is Something You Need

This work introduces a novel training paradigm that draws from affective neuroscience. Inspired by the interplay of emotions and cognition in the human brain and more specifically the SEEKING motivational state, we design a dual-model framework where a smaller base model is trained continuously, while a larger motivated model is activated intermittently during predefined "motivation conditions". The framework mimics the emotional state of high curiosity and anticipation of reward in which broader brain regions are recruited to enhance cognitive performance. Exploiting scalable architectures where larger models extend smaller ones, our method enables shared weight updates and selective expansion of network capacity during noteworthy training steps. Empirical evaluation on the image classification task demonstrates that, not only does the alternating training scheme efficiently and effectively enhance the base model compared to a traditional scheme, in some cases, the motivational model also surpasses its standalone counterpart despite seeing less data per epoch. This opens the possibility of simultaneously training two models tailored to different deployment constraints with competitive or superior performance while keeping training cost lower than when training the larger model.

Exploring Semantic Labeling Strategies for Third-Party Cybersecurity Risk Assessment Questionnaires

Third-Party Risk Assessment (TPRA) is a core cybersecurity practice for evaluating suppliers against standards such as ISO/IEC 27001 and NIST. TPRA questionnaires are typically drawn from large repositories of security and compliance questions, yet tailoring assessments to organizational needs remains a largely manual process. Existing retrieval approaches rely on keyword or surface-level similarity, which often fails to capture implicit assessment scope and control semantics. This paper explores strategies for organizing and retrieving TPRA cybersecurity questions using semantic labels that describe both control domains and assessment scope. We compare direct question-level labeling with a Large Language Model (LLM) against a hybrid semi-supervised semantic labeling (SSSL) pipeline that clusters questions in embedding space, labels a small representative subset using an LLM, and propagates labels to remaining questions using k-Nearest Neighbors; we also compare downstream retrieval based on direct question similarity versus retrieval in the label space. We find that semantic labels can improve retrieval alignment when labels are discriminative and consistent, and that SSSL can generalize labels from a small labeled subset to large repositories while substantially reducing LLM usage and cost.