ShredBench: Evaluating the Semantic Reasoning Capabilities of Multimodal LLMs in Document Reconstruction

Multimodal Large Language Models (MLLMs) have achieved remarkable performance in Visually Rich Document Understanding (VRDU) tasks, but their capabilities are mainly evaluated on pristine, well-structured document images. We consider content restoration from shredded fragments, a challenging VRDU setting that requires integrating visual pattern recognition with semantic reasoning under significant content discontinuities. To facilitate systematic evaluation of complex VRDU tasks, we introduce ShredBench, a benchmark supported by an automated generation pipeline that renders fragmented documents directly from Markdown. The proposed pipeline ensures evaluation validity by allowing the flexible integration of latest or unseen textual sources to prevent training data contamination. ShredBench assesses four scenarios (English, Chinese, Code, Table) with three fragmentation granularities (8, 12, 16 pieces). Empirical evaluations on state-of-the-art MLLMs reveal a significant performance gap: The method is effective on intact documents; however, once the document is shredded, restoration becomes a significant challenge, with NED dropping sharply as fragmentation increases. Our findings highlight that current MLLMs lack the fine-grained cross-modal reasoning required to bridge visual discontinuities, identifying a critical gap in robust VRDU research.

Exploring Embodied Emotional Communication: A Human-oriented Review of Mediated Social Touch

This paper offers a structured understanding of mediated social touch (MST) using a human-oriented approach, through an extensive review of literature spanning tactile interfaces, emotional information, mapping mechanisms, and the dynamics of human-human and human-robot interactions. By investigating the existing and exploratory mapping strategies of the 37 selected MST cases, we established the emotional expression space of MSTs that accommodated a diverse spectrum of emotions by integrating the categorical and Valence-arousal models, showcasing how emotional cues can be translated into tactile signals. Based on the expressive capacity of MSTs, a practical design space was structured encompassing factors such as the body locations, device form, tactile modalities, and parameters. We also proposed various design strategies for MSTs including workflow, evaluation methods, and ethical and cultural considerations, as well as several future research directions. MSTs' potential is reflected not only in conveying emotional information but also in fostering empathy, comfort, and connection in both human-human and human-robot interactions. This paper aims to serve as a comprehensive reference for design researchers and practitioners, which helps expand the scope of emotional communication of MSTs, facilitating the exploration of diverse applications of affective haptics, and enhancing the naturalness and sociability of haptic interaction.