Category
1,707 papers
The AI Companion Vulnerability-Response Taxonomy is introduced, a grounded, paired taxonomy of user vulnerability and chatbot response designed for analyzing extended companion chatbot interactions and reveals distinct response profiles of AI companions in conversations with vulnerable users.
A novel multimodal framework that integrates egocentric vision, pupillometry, eye-gaze tracking, posture detection, heart activity, and the inferencing capabilities of large language models (LLMs) to create a proactive and context-aware ecosystem that dynamically adapts to users' psychophysiological states while analyz...
LatentGandr is a visual analytics technique that facilitates latent space exploration by extracting locally linear dimensions from embeddings in high-dimensional latent spaces by analyzing the topology and local curvature of the embeddings, and automatically identifies local neighborhoods and computes their principal c...
Empirical evidence is provided for understanding the "double-edged sword" effect of LLM-NPCs on player experience, and the importance of scenario-sensitive and user-sensitive design in intelligent NPC systems is highlighted.
Reach audiences
Advertise in front of researchers, engineers, and readers.
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.
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.
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
A Blog post by H company on Hugging Face
The PKG Center for Social Impact expands Code.Tulsa experiential learning program.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
