Song-Ze Yu | Search arXiv

arXiv2026

Anamnesis: An Open-Source Platform for Large-Scale Backstory-Conditioned Survey Simulation

We present Anamnesis, an interactive system for demographically controllable survey simulation using large language models. Open-source and designed for non-technical users/researchers, Anamnesis enables the prototyping and stress-testing of survey instruments on virtual populations rather than real human subjects. The platform operationalizes the recently introduced Anthology and Alterity frameworks, which use structured narrative backstories to condition model responses, within a unified web interface. It supports open-ended generation, probabilistic demographic resampling, and multimodal (image and audio) surveys. We evaluate the system through two case studies: (1) replicating segments of Pew Research Center's American Trends Panel (ATP) on political typology and biomedical issues and (2) emulating human preference in the New Yorker Caption Contest. In both cases, Anamnesis produces opinion distributions that more closely match real-world survey data than standard persona-prompting baselines, offering a transparent, reproducible, and open-source alternative to proprietary simulation services.

cs.CL↗

arXiv2026

MuSP-Bench: Advanced Multimodal Benchmarking of Music Understanding across Score and Performance

Musicians commonly communicate music through scores and performances. Scores encode musical intent, while performances realize it in sound. To investigate whether models can meaningfully engage with both modalities, we introduce MuSP-Bench, a human-authored benchmark of 490 questions targeting understanding across Musical Scores and Performances. The benchmark distinguishes itself by spanning score-based, performance-based, interpretive, and long-horizon reasoning across classical piano and orchestral works. We evaluate frontier multimodal large language models under multiple input conditions. Our results show that these models struggle substantially to understand scores, while facing even greater challenges when reasoning about performance audio. The benchmark is available at https://musp.vaclis.net/.

cs.MM↗