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2,089 papers
The need for more user-centered studies to measure LLMs'ability to help users while preserving privacy, and for improving alignment between LLMs and users in estimating perceived privacy and utility, is discussed.
It is shown that translating simulation traces to a sparse representation of "high-level"structural patterns leads to more effective interpretation by LLMs, and an unsupervised learning scheme to perform this translation, or annotation, via program synthesis is proposed.
GazeFS improves Focus target centering and empirical residual contraction while leaving temporal smoothness as a separate objective and introduces GazeFS, which maps a variable-length gaze-head history to the next target-center direction and a short-horizon Search/Focus estimate without target information at inference.
A structured framework of behavioral indicators that may signal progression toward potentially catastrophic threats from artificial intelligence systems is presented, inspired by established methodologies in cybersecurity and national security.
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It is argued that federation is not in itself a remedy for extractive AI, because outcomes depend on who governs the data and the model and who has agency over the practices that shape them, and four design principles for a creative data commons are proposed.
The TRANSparent Framework for Interactive, Verifiable, Editable Representation (Transfiver) is introduced, an architecture for human-AI co-inference through a shared editable state that separates shared parameters from the persistent state, which evolves during deployment without parameter retraining.
DECODE (Developer Edits of Code Dataset), a dataset of 53.6K real-world in-IDE code edits of AI-generated code in Python, TypeScript, and JavaScript, is introduced, and finetuning on DECODE enables open-source 3B models to perform code edit prediction tasks significantly better than frontier LLMs.
In a controlled study, 20 university students completed five common daily tasks using OpenClaw, a general-purpose AI agent, across tasks chosen to vary in privacy, stakes, and reversibility, and found delegation regret appeared consistently when the agent executed actions without preview, even when the output was rated...
It is demonstrated that Big Five inventories do not measure a construct equivalent to human personality in LLMs, highlighting the need for evaluation frameworks that are specifically designed and validated for LLMs, rather than transferring human psychological constructs without first establishing their validity.
This study applies Archetypal Analysis to standardized acceptance ratings across eight representative AI application contexts in games to provide an exploratory empirical vocabulary for segmenting game AI audiences and offers preliminary design implications for more context-sensitive and player-sensitive AI integration...
An open-source dataset collected from 279 students at a public Australian university, generated through students' use of FLoRA, provides a valuable resource for advancing understanding of GenAI's role in educational IPS and informing the design of adaptive, inclusive AI-powered learning tools.
This work investigates how listener perception of AI-generated versus human-composed music affects emotional resonance and regulation and challenges the assumption that preference alone defines system success, highlighting design implications for affective and wellness technologies that foreground authenticity, transpa...
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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