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2,089 papers
Alignment Games, a framework for making task-relevant differences visible and repairable during human-AI interaction, is introduced and design principles for supporting task-sufficient conceptual alignment at runtime are derived.
This work proposes a Bayesian framework that treats clarification as an active learning problem over grounded Signal Temporal Logic task specifications and uses LLMs to initialize candidate formal specifications and translate informative contrasts into natural-language clarification questions, while Bayesian optimizati...
Practical assistive and rehabilitative brain--computer interfaces require subject-independent motor-imagery EEG (MI-EEG) decoders that generalize to new users under limited target-user data and constrained compute. However, held-out-subject performance can be overstated when test-subject information influences preproce...
EEG-Fusion is presented, a failure-informed decision-level fusion framework that treats source-free MI decoding as label-free reliability estimation over heterogeneous experts and suggests that label-free reliability estimation can reduce subject-level failure modes in source-free MI-EEG deployment.
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E-CONAN diagnostics dataset is introduced, a freely available dataset manually-annotated with coarse-grained and fine-grained categories based on the authors' proposed Arabic hierarchy, which helps NLU designers better understand their models by doing error analysis and in-depth investigation.
Data videos communicate data insights through dynamic charts, voice narration, and synchronized animations, and have become a widely adopted form of data storytelling. However, producing them requires expertise in data analysis, narrative design, and video editing. Static visualization tools lack narrative and animatio...
This work argues that their continual coordination under competing demands constitutes an important and underexplored target for modern robot learning and proposes the ethological behavioral substrate as a conceptual lens for studying this form of competence in artificial agents.
As coding assistants become increasingly autonomous, developers run multiple sessions in parallel, shifting the challenge from code generation alone to coordinating and monitoring concurrent agent work. Through a formative study (N=14), we identified PILOT: five supervisory practices for Planning, Isolating, Logging, O...
DashAct is introduced, to the authors' knowledge the first benchmark to diagnose failures at a fine-grained level within the same dashboard task, and measures the minimum support an agent needs to recover rather than scoring isolated skills.
These findings show that Vis designers actively use LLMs for both creative and technical aspects of the visualization process, and opens up opportunities for research combining LLM-mediated work with Vis tools that incorporate data visualization guidance, constraints, and best practices.
PainterBench, a benchmark that ports the incomplete-drawing task to the agentic setting, is introduced and ViDrA-adapted, an automated scorer that predicts human creativity ratings of agent drawings, is presented.
CLAIRE (Clinical Language and Agentic Intelligence for Reasoning and Entry), a hybrid workflow that separates field-state discovery, source-to-field mapping, deterministic validation, bounded correction, escalation, and audit tracing, is developed and evaluated.
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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