Field Kit
A consulting OS with 40+ tools across 3 MCP servers. This is a live view into the knowledge base — trends, papers, regulations, frameworks, and brand intelligence.
Trend Radar
Latest Papers
Mental Health Professionals’ Views on Gaming to Inform Game-Based Interventions: Qualitative Cross-Sectional Study
Lauri Lukka, Veli-Matti Karhulahti, J Matias Palva
Mental Health Professionals’ Views on Gaming to Inform Game-Based Interventions: Qualitative Cross-Sectional Study
Gamified Shoulder Wheel: Enhancing Pediatric Engagement and Data Collection
David Infante-Sánchez, Aikaterini Bourazeri, Tatiana Cruz Lira +4
Shoulder wheels are commonly used in upper limb rehabilitation, yet their repetitive nature often results in low engagement, particularly among pediatric patients. This study aimed to develop a low-cost, gamified rehabilitation system by integrating a tablet-based serious game with a gyroscope-equipped shoulder wheel to guide exercise rhythm, track movement, and provide real-time visual feedback. Specifically, the study addressed two research questions: (RQ1) what are the benefits of integrating
Aging Collagen: Fostering Students’ Motivation and Understanding through Meaningful Gamification
Ashley Quan, Laura M. van der Lubbe, Henri E.K. Matimba
Although gamification has been used in education for many studies, the focus has primarily been on rewards-based gamification, with limited attention to meaningful gamification. This study represents the design and evaluation of a meaningfully gamified educational tool, Aging Collagen, aimed at enhancing intrinsic motivation and learning outcomes regarding collagen among lower secondary students. The design of Aging Collagen followed the guidelines from the RECIPE framework proposed by Nicholson
Convergent Validity of Game-Based Assessment: A Meta-Analysis
Fadillah, Rahmat Hidayat, Agung Santoso
Game-Based Assessments (GBAs) have emerged as innovative tools for measuring personality traits, particularly in recruitment and employee selection. This meta-analysis aims to evaluate the convergent validity of GBAs compared to traditional self-report personality measures, addressing ongoing concerns about their psychometric robustness. A total of 18 studies from 13 peer-reviewed articles (2002–2025) were systematically reviewed using data from Scopus, ProQuest, Wiley Online Library, and Scienc
Safe Walk: A Serious Game for Exploring Environmental Distractions Affecting Pedestrian Safety
Yoones A. Sekhavat, Joseph Mani, Seyed Vahid Mostafavi +3
Understanding the impact of distracting factors on pedestrian safety is crucial for reducing accidents. This study investigates how urban video advertisements influence pedestrian behavior amidst competing visual stimuli. We employed a true experimental design, utilizing eye-tracking technology within a simulated environment to assess participants' visual behavior in the presence of video advertisements. Participants were randomly assigned to one of three scenarios, and we compared attention dir
Latent Class Analysis of Gameplay Metrics from Youth Playing Robot ChampionsTM: Relations of Class Membership to Persistence and Intensity
Lawrence Scheier, William Hansen, Alex Stone +1
Purpose: Game metrics assessing in-game actions (e.g., keystrokes, challenges, and time on task) have become a staple component to understanding the attraction youth have towards a video game. Data from automatic log files can be used to assess how players interact with various aspects of the game and whether they comprehend the game mechanics. Despite a rapid burgeoning in the use of game metrics, few studies have used this type of objective instrumentation to examine whether users display uniq
Larp in Wartime: Palestine
Mo Holkar
This video was recorded during the 2025 Nordic Larp Talks, in Oslo. In 2024, the Palestinian larp organisation Bait Byout ran its Larp Factory training programme in social impact larp design and game design for 43 young adults aged 18-30 from the West Bank and Jerusalem. In this conversation with Johanna Koljonen, Tamara Nassar discusses […]
What Medieval Spirituality Taught Me About Intimacy in Larp
Mo Holkar
This video was recorded during the 2025 Nordic Larp Talks, in Oslo. What’s at stake in admitting that we are nothing but relationships? This talk explores how religious communities from the Middle Ages and their writings about spiritual intimacy can reimagine how we think about intimacy larp. The talk dips our into bodies in ecstasy, […]
Agents of Chaos
Natalie Shapira, Chris Wendler, Avery Yen +35 · arXiv preprint (cs.AI)
We report an exploratory red-teaming study of autonomous language-model–powered agents deployed in a live laboratory environment with persistent memory, email accounts, Discord access, file systems, and shell execution. Over a two-week period, twenty AI researchers interacted with the agents under benign and adversarial conditions. Focusing on failures emerging from the integration of language models with autonomy, tool use, and multi-party communication, we document eleven representative case s...
Revisiting Content-Based Music Recommendation: Efficient Feature Aggregation from Large-Scale Music Models
Yizhi Zhou, Jia-Qi Yang, De-Chuan Zhan +1
Music Recommendation Systems (MRSs) are a cornerstone of modern streaming platforms. Existing recommendation models, spanning both recall and ranking stages, predominantly rely on collaborative filtering, which fails to exploit the intrinsic characteristics of audio and consequently leads to suboptimal performance, particularly in cold-start scenarios. However, existing music recommendation datasets often lack rich multimodal information, such as raw audio signals and descriptive textual metadat
MATRAG: Multi-Agent Transparent Retrieval-Augmented Generation for Explainable Recommendations
Sushant Mehta
Large Language Model (LLM)-based recommendation systems have demonstrated remarkable capabilities in understanding user preferences and generating personalized suggestions. However, existing approaches face critical challenges in transparency, knowledge grounding, and the ability to provide coherent explanations that foster user trust. We introduce MATRAG (Multi-Agent Transparent Retrieval-Augmented Generation), a novel framework that combined multi-agent collaboration with knowledge graph-augme
AtomicRAG: Atom-Entity Graphs for Retrieval-Augmented Generation
Yanning Hou, Duanyang Yuan, Sihang Zhou +5
Recent GraphRAG methods integrate graph structures into text indexing and retrieval, using knowledge graph triples to connect text chunks, thereby improving retrieval coverage and precision. However, we observe that treating text chunks as the basic unit of knowledge representation rigidly groups multiple atomic facts together, limiting the flexibility and adaptability needed to support diverse retrieval scenarios. Additionally, triple-based entity linking is sensitive to relation-extraction err
CaST-POI: Candidate-Conditioned Spatiotemporal Modeling for Next POI Recommendation
Zhenyu Yu, Chunlei Meng, Yangchen Zeng +2
Next Point-of-Interest (POI) recommendation plays a crucial role in location-based services by predicting users' future mobility patterns. Existing methods typically compute a single user representation from historical trajectories and use it to score all candidate POIs uniformly. However, this candidate-agnostic paradigm overlooks that the relevance of historical visits inherently depends on which candidate is being evaluated. In this paper, we propose CaST-POI, a candidate-conditioned spatiote
ADS-POI: Agentic Spatiotemporal State Decomposition for Next Point-of-Interest Recommendation
Zhenyu Yu, Chunlei Meng, Yangchen Zeng +2
Next point-of-interest (POI) recommendation requires modeling user mobility as a spatiotemporal sequence, where different behavioral factors may evolve at different temporal and spatial scales. Most existing methods compress a user's history into a single latent representation, which tends to entangle heterogeneous signals such as routine mobility patterns, short-term intent, and temporal regularities. This entanglement limits the flexibility of state evolution and reduces the model's ability to
Clinical Reasoning AI for Oncology Treatment Planning: A Multi-Specialty Case-Based Evaluation
Philippe E. Spiess, Md Muntasir Zitu, Alison Walker +33
Background: More than 80% of U.S. cancer care is delivered in community settings, where survival remains worse than at academic centers. Clinicians must integrate genomics, staging, radiology, pathology, and changing guidelines, creating cognitive burden. We evaluated OncoBrain, an AI clinical reasoning platform for oncology treatment-plan generation, as an early step toward OGI. Methods: OncoBrain combines general-purpose LLMs with a cancer-specific graph retrieval-augmented generation layer,
The Shrinking Sweet Spot: How Algorithms, Institutions, and Social Priors Shape Musical Ecosystems
Fabio Lokwani Di Matteo, Pier Luigi Sacco
Why do some national music markets sustain a rich musical diversity whereas others converge on mostly formulaic output? The existing models of cultural consumption (superstar economics, rational addiction, Bayesian social learning) each capture part of the answer, but none can explain how exposure, social influence, institutional gatekeeping, and algorithmic curation interact to shape what listeners come to prefer. We address this gap by modeling musical taste as a learning process rather than a
Dialect vs Demographics: Quantifying LLM Bias from Implicit Linguistic Signals vs. Explicit User Profiles
Irti Haq, Belén Saldías
As state-of-the-art Large Language Models (LLMs) have become ubiquitous, ensuring equitable performance across diverse demographics is critical. However, it remains unclear whether these disparities arise from the explicitly stated identity itself or from the way identity is signaled. In real-world interactions, users' identity is often conveyed implicitly through a complex combination of various socio-linguistic factors. This study disentangles these signals by employing a factorial design with
Advances in Art: Orthogonal Disruption and the Beauty in Schematics
Sergio Alvarez-Telena, Marta Diez-Fernandez
This paper introduces Orthogonal Art, a proposed artistic discipline that emerges in dialectical response to artificial intelligence rather than in service of it. Unlike AI-augmented creative practices, Orthogonal Art is structurally defined by occupying the generative and conceptual spaces that current AI systems cannot access. As a founding instantiation of this framework, the paper presents a novel artistic practice in which technical schematics serve as the primary medium. A significant seco
Knowledge Base
Frameworks by Domain
Brands Tracked
Powered by 3 MCP servers · Supabase + pgvector · Refreshed every minute
Full Case Study →