Semin Jin | Searcharxiv

arXiv2026

Beyond Semantic Similarity: Open Challenges for Embedding-Based Creative Process Analysis Across AI Design Tools

AI-based creativity support tools (CSTs) are evaluated through domain-specific metrics, limiting cross-domain comparison of creative processes. Embedding-based protocol analysis offers a potential domain-agnostic analytical layer. However, we argue that fixed embedding similarity can misrepresent creative dynamics: it may not detect creative pivots that occur within superficially similar language, treating shifts in the problem being addressed as continued elaboration. We identify three open challenges stemming from this gap: aligning similarity measures with creative significance, segmenting and representing multimodal design traces, and evaluating agentic systems where embedding-based metrics enter the generation loop and shape agent behavior. We propose context-aware interventions using large language models as a direction for making trace analysis sensitive to session-specific creative dynamics.

cs.HC↗

arXiv2026

Behavior-Aware Anthropometric Scene Generation for Human-Usable 3D Layouts

Well-designed indoor scenes should prioritize how people can act within a space rather than merely what objects to place. However, existing 3D scene generation methods emphasize visual and semantic plausibility, while insufficiently addressing whether people can comfortably walk, sit, or manipulate objects. To bridge this gap, we present a Behavior-Aware Anthropometric Scene Generation framework. Our approach leverages vision-language models (VLMs) to analyze object-behavior relationships, translating spatial requirements into parametric layout constraints adapted to user-specific anthropometric data. We conducted comparative studies with state-of-the-art models using geometric metrics and a user perception study (N=16). We further conducted in-depth human-scale studies (individuals, N=20; groups, N=18). The results showed improvements in task completion time, trajectory efficiency, and human-object manipulation space. This study contributes a framework that bridges VLM-based interaction reasoning with anthropometric constraints, validated through both technical metrics and real-scale human usability studies.

cs.HC↗