Posts

paper in Frontiers

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Differentiated memory and scientific cognition in AI research agents

Cuadros DF, Maiga A-A, Thatham S, Ortiz A, Powers-Fletcher M and Tang M (2026) Differentiated memory and scientific cognition in AI research agents. Front. Big Data 9:1916523. doi: 10.3389/fdata.2026.1916523

AI research agents increasingly support ideation, literature search, coding, experimental execution, analysis, and manuscript drafting across the scientific workflow. This progress advances automated discovery, but workflow automation is not scientific cognition. Scientific reasoning is cumulative and path-dependent: it depends on what a researcher has written, read, learned from critique, absorbed through experience, and used as habitual standards for judging novelty, rigor, feasibility, and significance. We propose Mnemo as a framework for modeling scientific cognition in AI research agents. First, scientific cognition may require differentiated memory, organized into distinct spaces for authored work, external reference, critique, experience, and judgment. Second, provenance should be treated not as passive metadata but as memory routing, because source origin helps determine cognitive function in reasoning. Third, new ideas may be better modeled as controlled collisions across memory spaces, filtered by judgment, rejection, and epistemic calibration, than as generic recombination from model priors. Mnemo motivates a research agenda for AI in science centered on routing fidelity, critique use, judgment alignment, rejection quality, and epistemic calibration.

paper in Environments Journal

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Toward Sub-Sewershed Spatio-Temporal Wastewater Surveillance: A Critical Review and a Candidate Multimodal Foundation-Model Framework

Cuadros, Diego F., Xi Chen, and Ming Tang. 2026. “Toward Sub-Sewershed Spatio-Temporal Wastewater Surveillance: A Critical Review and a Candidate Multimodal Foundation-Model Framework” Environments 13, no. 7: 382. https://doi.org/10.3390/environments13070382

Wastewater-based epidemiology (WBE) has matured into a population-level surveillance complement with operational precedent in poliovirus environmental surveillance, institutionalised systems for SARS-CoV-2, and expanding evidence across respiratory pathogens, substance-use markers, and antimicrobial-resistance targets at uneven maturity. The unresolved problem is specific: sub-sewershed spatio-temporal inference under sewer-network and observational aggregation. Intra-catchment heterogeneity, hydraulic dynamics, and equity-relevant population differences can all be obscured by aggregate-scale modelling. Current artificial intelligence/machine learning (AI/ML) methods in WBE can be organised into four threads: temporal forecasting, spatial–statistical localisation, sewer-network and hydraulic transport, and cross-site transfer. These methods solve useful parts of the surveillance problem at the scales they target, but they do not yet supply transferable latent sub-sewershed representations under downstream aggregation. This review proposes a candidate multimodal foundation-model framework that treats place and time as jointly learnable entities, integrates a graph backbone over the sewer-network topology, incorporates physics-informed constraints, and embeds equity-conscious downstream validation as a design requirement. The framework is intended to make sub-sewershed hypotheses explicit, testable, uncertainty-bounded, and accountable to environmental-justice-relevant external validation. Whether those hypotheses survive empirical testing remains an open question.

Figure 1. The aggregation problem at the sub-sewershed scale.

Poster in Urban Affairs Conference

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Poster: Co-Designing Data Governance Policies for a Neighborhood-Based Digital Health Intervention

Lauren Forbes, ..Ming Tang. Co-Designing Data Governance Policies for a Neighborhood-Based Digital Health Intervention. INTERNATIONAL CONFERENCE ON URBAN AFFAIRS 2026.

Digital health innovations (DHI) present important opportunities to improve individual health outcomes and community well-being; however, they are typically developed by private sector innovators who prioritize the novelty of innovation and data-driven value creation over achieving health equity. Resultantly, Black lived experiences and perceptions of digital innovations are regularly overlooked in digital innovation development. This exclusion, exacerbated by the digital divide and place-based structural barriers, perpetuates racialized health inequities and widespread institutional mistrust among Black communities. In our study, we address these complex challenges through the question, “What are the digital data governance preferences of Black communities that should inform DHI development”? This question is the first of a broader study that seeks to generate a gamified DHI using “digital twinning” (3D, GIS based city modeling with virtual reality) for health outcomes improvement by integrating these community-driven data governance policies and fostering institution trust. We use mixed methods consisting of a longitudinal survey, listening sessions and focus groups, demonstration sessions, co-design hubs, and user-contributed Ecological Momentary Assessment (EMA) data to answer our questions and to test community perceptions of and experiences using the gamified DHI. By the time of this presentation, we anticipate having findings from the first survey, listening sessions, and focus groups along with preliminary community-driven data governance policies and a draft game design architecture. This interdisciplinary study represents a novel opportunity to not only improve health outcomes within marginalized communities, but also to repair institutional mistrust and foster digital literacy and agency through the co-design of digital data governance policies. 

More info on the project is available at THRED: Technology for Health, Resilience, Equity , and Decision-Making.

Poster in Creativity & Cognition

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VR as a Creative Medium for Immersive Storytelling: An Autobiographic Design Exploration

Bozhi Peng, Heekyoung Jung, Ming Tang, and Yoshiko Burke. 2026. VR as a Creative Medium for Immersive Storytelling: An Autobiographic Design Exploration. In Proceedings of the  Creativity & Cognition 2026 conference (C&C ’26,  London. UK. 2026). Association for Computing Machinery, New York, NY, USA, 1363–1370.  https://doi.org/10.1145/3803784.3816833

This project explores virtual reality (VR) as a creative medium for expressing personal memories through space, movement, and interaction, while evoking shared experiences. Using autobiographic and research through design approaches, three VR scenes were developed through iterative prototyping and first-person inquiry to investigate immersive storytelling. Guided by the framework of immersion, presence, and interactivity, the project examines how design decisions shape immersive experience in VR through the alignment of perception, embodied movement, and responsive interaction. The work demonstrates how personal memory can function as a generative resource for experiential storytelling rather than representational narration. It also reflects on the potential and limitations of autobiographic design as a method of creative inquiry in VR and identifies future directions for engaging collective and cultural memory through audience participation.

Besides the proceeding book, the publication is also included in the ACM Digital Library. 

 

Paper: VR Training to De-escalate Patient Aggressive Behavior

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Journal Paper: Virtual Reality Training to De-escalate Patient Aggressive Behavior: A Pilot Study

Daraiseh, N. M., Tang, M., Macaluso, M., Aeschbury, M., Bachtel, A., Nikolaenko, M., … Vaughn, A. (2025). Virtual Reality Training to De-escalate Patient Aggressive Behavior: A Pilot Study. International Journal of Human–Computer Interaction, 1–16. https://doi.org/10.1080/10447318.2025.2576635

Abstract
Despite intensive crisis de-escalation training, psychiatric staff continue to face high injury rates from aggressive patient interactions (APIs). New approaches are needed to enhance the application of effective strategies in managing APIs. This study explored the efficacy and feasibility of VR training for psychiatric staff in recognizing and selecting appropriate de-escalation interventions. A quasi-experimental design with psychiatric staff (N = 33) tested the effectiveness and feasibility of VR training depicting a common API interaction. Effectiveness was assessed through pre-post comparisons of the Confidence in Coping with Patient Aggression (CCPA) survey, correct answer percentages, response times, and attempt success rates. Feasibility was indicated by mean scores above ‘neutral’ on usability, presence, and learner satisfaction surveys. Results showed significant improvements in response times and confidence (p<.0001), with over 75% of participants rating the training positively. VR training is effective and feasible for enhancing de-escalation skills, offering a promising approach for psychiatric facilities.

More information on the project Therapeutic Crisis Intervention Simulation. P1,P2