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2,089 papers
Semantic collapse, the progressive narrowing of what AI systems generate, is studied in MOLTBOOK, a social network of interacting AI agents that human users configure and steer, and interface and policy interventions that could support improved human input are discussed.
In the United States, management of cybercrime-related consumer complaints increasingly falls on state and city governments given de-staffing of federal agencies. AI, and in particular, large language models (LLMs), shows promise for detecting cybercrime in text complaints, but often via specialized models that local g...
This study examines how title characteristics, particularly title word count and intended message, influence people's pattern identification in single-class line charts.
Visual data communication in digital media is increasingly characterized by short attention spans and snapshot-based viewing, often employing line charts to convey trends and patterns. Among all visual elements, titles are crucial elements that can shape how viewers interpret visual information and form chart takeaways...
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This work proposes Lexplorer, a flexible interface for exploring, navigating, and analyzing legal documents, based on a taxonomy capturing user intents, that enables context-sensitive interactions with evolving collections of interconnected legal texts, facilitating Adaptive Meaning Construction in law.
LLM-assisted VR instruction is characterized as a domain-grounded mediation process among expert expression, executable operations, and learner-facing representations among expert expression, executable operations, and learner-facing representations.
This work analyzes ChatGPT and Gemini conversations, using repeated prompting and clarification following misunderstanding as behavioural indicators of cognitive difficulty, and finds that response characteristics such as length, readability and lexical diversity do not have fixed relationships with conversational diff...
While DS-enhanced visualisations effectively support lower-order tasks (finding data points and understanding insights), they don’t necessarily aid the correct completion of higher-order tasks (application, analysis, evaluation and creation).
The MM/AI workshop calls for a critical reassessment of how the authors understand and study mental models in human-AI interaction research, and aims to foster theoretical and methodological exchange on mental models in human-AI interaction, identify open challenges, and develop directions for future research.
A scenario-knowledge-driven pipeline is proposed: a single scenario knowledge document, human-authored and version-controlled, configures sensing, constrains LLM reasoning, and shapes a graded intervention proposal that appropriateness of these interventions, the pipeline's restraint on sessions without struggle, and t...
The results show that providing tailored examples contributes to better writing performance and user experience.
A shared vocabulary, a design space, and evidence that measured monitoring and task performance are separable design targets are contributed, suggesting that measured monitoring and task performance are separable design targets.
AI agents can generate code at a scale that’s hard for humans to review line-by-line. PDD Kit helps ensure the code is doing what the developer intended by creating formal specifications and testing against them. It converts developer intent into a Verus specification, proves AI-generated code meets it for every input, and uses tests where proofs aren't practical. Developers review intent, not every line. The post Formally verifying agent-written full-stack web apps with PDD Kit appeared first o…
Data Mining & Modeling
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
During the School of Humanities, Arts, and Social Sciences’ 75th Anniversary Symposium, President Sally Kornbluth said “MIT has never needed your particular insight and guidance more than we do now.”
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
“X-Hinges” helps design and 3D print items like lamps and gloves with an interactive twist: automatically embedded sensors that perceive and adaptively respond to human contact.
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