Sachit Mahajan, Dirk Helbing

This paper critically examines the growing use of big data algorithms and AI in science, society, and public policy. While these tools are often introduced with the goal of increasing efficiency, the results do not always lead to greater empowerment or fairness for individuals or communities. Persistent issues such as bias, measurement error, and over-reliance on prediction can undermine success and produce outcomes that are neither fair nor transparent, especially when automated decisions replace human judgment. Beyond technical limitations, the widespread use of data-driven methods also shapes the distribution of power, influences public trust, and raises questions about the health of techno-socioeconomic institutions. We argue that the pursuit of optimality cannot succeed without careful evaluation of ethical risks and societal side effects. Responsible innovation demands open standards, ongoing scrutiny, and a focus on human values alongside technical performance. Our goal is to encourage a more balanced approach to big data-one that recognizes both its potentials and its limits, and one that aims for genuine social benefits rather than just efficiency alone.

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Binglu Wang, Zhengnan Ma, Dashun Wang, Brian Uzzi

This study examines a fundamental yet overlooked function of peer review: its role in exposing reviewers to new and unexpected ideas. Leveraging a natural experiment involving over half a million peer review invitations covering both accepted and rejected manuscripts, and integrating high-scale bibliographic and editorial records for 37,279 submitting authors, we find that exposure to a manuscript’s core ideas significantly influences the future referencing behavior and knowledge of reviewer invitees who decline the review invite. Specifically, declining reviewer invitees who could view concise summaries of the manuscript’s core ideas not only increase their citations to the manuscript itself but also demonstrate expanded breadth, depth, diversity, and prominence of citations to the submitting author’s broader body of work. Overall, these results suggest peer review substantially influences the spread of scientific knowledge. Ironically, while the massive scale of peer review, entailing millions of reviews annually, often drives policy debates about its costs and burdens, our findings demonstrate that precisely because of this scale, peer review serves as a powerful yet previously unrecognized engine for idea diffusion, which is central to scientific advances and scholarly communication.

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Shuowen Li, Kexin Wang, Minglu Fang, Danqi Huang, Ali Asadipour, Haipeng Mi, Yitong Sun

We present a semantic feedback framework that enables natural language to guide the evolution of artificial life systems. Integrating a prompt-to-parameter encoder, a CMA-ES optimizer, and CLIP-based evaluation, the system allows user intent to modulate both visual outcomes and underlying behavioral rules. Implemented in an interactive ecosystem simulation, the framework supports prompt refinement, multi-agent interaction, and emergent rule synthesis. User studies show improved semantic alignment over manual tuning and demonstrate the system’s potential as a platform for participatory generative design and open-ended evolution.

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Hyun-Woo Lee, Gerardo Iñiguez, Hang-Hyun Jo, Hye Jin Park

Ranking systems are widely used to simplify and interpret complex data across diverse domains, from economic indicators and sports scores to online content popularity. While previous studies including the Zipf’s law have focused on the static, aggregated properties of ranks, in recent years researchers have begun to uncover generic features in their temporal dynamics. In this work, we introduce and study a series of system-level indices that quantify the compositional changes in ranking lists over time, and also characterize the temporal ranking trajectories of individual items’ ranking dynamics. We apply our method to analyze ranking dynamics of movies from the over-the-top services, including Netflix, as well as that of music items in Spotify charts. We find that newly released movies or music items influence most the system-level compositional changes of ranking lists; the highest ranks of items are strongly correlated with their lifetimes in the lists more than their first and last ranks. Our findings offer a novel lens to understand collective ranking dynamics and provide a basis for comparing fluctuation patterns across various ordered systems.

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Wei Wang, Niklas Hagemann,  Alejandro Gonzalez-Garcia,  Carlo Ratti, Daniela Rus

Self-reconfigurable aquatic robots offer promising potential for a wide range of marine applica-tions, including building temporary infrastructure, environmental monitoring, and on-demand transportation. However, achieving autonomous water-based self-reconfiguration, even in two di-mensions on the water surface, remains challenging, due to complex nonlinear hydrodynamics, disturbances from self-motion and neighboring robots, as well as external environmental factors. Here, we present the FloatForm platform, a group of miniature modular robotic boats, capable of self-assembling into physically connected structures, self-reconfiguring, and collectively traveling as larger assemblies via a hybrid coordination framework. Each robot unit is equipped with onboard sensing, motion control, and the ability to coordinate and physically latch with its neigh-bors. We demonstrate the feasibility of parallel self-reconfiguration, where distributed controllers on each robot handle coordination tasks such as aggregating into desired shapes and avoiding col-lisions, while a minimalist central planner oversees the overall success of each task and fixes im-perfections. This work advances the design, control, and coordination of modular robotic systems in aquatic environments, paving the way for flexible, robust and scalable applications on the water.

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