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Roujia Wang

Publications and source records attributed to Roujia Wang.

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The Morality Game: An online multiplayer platform to standardize, expedite, and expand research on cooperation

This paper presents the Morality Game, a platform designed to standardize and accelerate research on cooperation and morality through game theory-based experiments. The Morality Game functions as a video game for science, a hub for economic game research, an open-access data repository, and a tool for expediting the research process. It allows researchers to launch customized online multiplayer experiments with zero coding, using game trees to simulate moral dilemmas. The platform automates participant payments, data collection, and analysis, promoting replication and transparency. This paper details the platform's architecture, emphasizing its capabilities for standardizing research methods, unifying data, and enabling rapid aggregation and comparison of results. The Morality Game leverages dynamic, self-correcting game trees to generate well-controlled, abstract experiments that can represent any social scenario. Participants interact through a responsive user interface, making the experiments intuitive and engaging. Researchers can configure experiments through a user-friendly dashboard, specifying various parameters and utilizing pre-created or auto-generated game trees. The platform supports nested belief representation and incorporates artificial agents with customizable traits. Plans include integrating social networking features, enhancing emotional expression capabilities, and expanding the platform's reach to remote small-scale societies to test the ecological validity of findings. By evolving into an integrated ecosystem that supports the entire research lifecycle, the Morality Game aims to foster collaboration, enhance data accessibility, and ultimately increase cooperation. This paper outlines the platform's current features, architectural details, and future directions, demonstrating its potential to advance cooperation research.

q-bio.NC

SAGE-Music: Low-Latency Symbolic Music Generation via Attribute-Specialized Key-Value Head Sharing

Low-latency symbolic music generation is essential for real-time improvisation and human-AI co-creation. Existing transformer-based models, however, face a trade-off between inference speed and musical quality. Traditional acceleration techniques such as embedding pooling significantly degrade quality, while recently proposed Byte Pair Encoding (BPE) methods - though effective on single-track piano data - suffer large performance drops in multi-track settings, as revealed by our analysis. We propose Attribute-Specialized Key-Value Head Sharing (AS-KVHS), adapted to music's structured symbolic representation, achieving about 30% inference speedup with only a negligible (about 0.4%) quality drop in objective evaluations and slight improvements in subjective listening tests. Our main contributions are (1) the first systematic study of BPE's generalizability in multi-track symbolic music, and (2) the introduction of AS-KVHS for low-latency symbolic music generation. Beyond these, we also release SAGE-Music, an open-source benchmark that matches or surpasses state-of-the-art models in generation quality.

cs.SD