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2,089 papers
Overall, varifocal viewing yielded significantly higher performance than the fixed-focal baseline across key interaction metrics, although the magnitude and even the direction of the benefit varied across individuals.
This work developed a methodological workflow using Item Response Theory to evaluate VLM and human rater proficiency against expert-established ground truth, suggesting that top-performing VLMs can approximate ground-truth ratings at levels comparable to human raters.
It is suggested that local preference recognition requires community-specific training signal, not just better prompting, and this work supports future research on community-aware reward modeling, feed curation, and positive moderation.
The results demonstrate that personality pairing can improve human-AI collaboration and performance and motivate future research on the complex implications of AI personalization for human-AI collaboration, teamwork, and performance.
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Results show that EEG exhibits a consistent visual and emotional structure that can support dynamic video generation, whereas current reconstruction primarily reflects coarse stimulus-level information rather than fine-grained, trial-specific decoding.
NoteFlow is a notebook extension that leverages charts as “sight glasses” to provide a consistent and continuous tracing of data flow and trace the global evolution of selected charts to continuously observe how particular data attributes evolve throughout the EDA process.
This work presents an architecture that separates intent interpretation (LLM) from execution (deterministic domain tools) from explanation (LLM), connected by the Model Context Protocol (MCP) and grounded by domain ontologies that constrain planning to valid analysis chains.
It is shown that passive EEG, fused online with behavioral evidence, can meaningfully extend the number of targets users detect and engage beyond their unaided action bandwidth, and that OLIVE Pareto-dominates prior test-time adaptation frameworks, achieving the highest convergence rate at comparable convergence speed.
Co-Annotator is presented, which distills expert gaze and dictation into two guidance components: a gaze-aligned Vision Transformer producing fixation-aligned areas of interest (AOIs), and an ontology-bounded vision-language model (VLM) that pre-fills editable biomarker summaries for retinal optical coherence tomograph...
The findings suggest that ADBA offers solid advantages to the traditional PINs, and successfully addresses the tradeoffs between efficiency, memorability, and security under the usage scenarios considered in the study.
FISICA is a body-assessment and recommendation service running in production that measures the avatar with the same function used on the subject and solves until the two agree, on a sampling-invariant spinal metric.
GuardianAgent, a policy-conditioned anonymization framework that couples structured risk assessment with verified adaptive rewriting, achieves the strongest privacy-utility trade-off among published baselines and is the only method to reach more than 0.90 privacy in all three domains, remaining robust under a backbone...
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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