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VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence
The VLT foundation model integrates vision, language, and time-series data to improve industrial equipment monitoring and prognostic health management.
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JEEVHITAA -- An HCAI Ecosystem to Support Collective Care
The JEEVHITAA platform introduces a mobile system designed to facilitate coordinated, multi-actor information sharing and workflows within healthcare settings.
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Analytic Abduction: Causal Decomposition and Governed Commitment for Human--AI Coordination
A new framework for analytic abduction is proposed to improve human-AI coordination by governing how models commit to causal explanations.
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A Minimal Interpretable Architecture for Zero-Shot Reconstruction of Dynamical Systems
The paper presents a minimal architecture designed to interpret and reconstruct dynamical systems, aiming to identify the core requirements for in-context learning.
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Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting
Researchers have developed an LLM-assisted scripting tool that enables the visualization of massive petascale scientific datasets on standard consumer hardware.
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EdgeFaaS: A Function-based Framework for Edge Computing
EdgeFaaS is proposed as a new function-based framework designed to manage the heterogeneity and distribution of resources in edge computing environments.
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VOiLA: Vectorized Online Planning with Learned Diffusion Models for POMDP Agents
Researchers introduced a vectorized online planning approach using diffusion models to improve robot decision-making under uncertainty.
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遭连日舆论抨击后，Meta暂停AI图像生成功能
Meta has suspended an AI feature that allowed users to generate images using public Instagram photos following widespread criticism regarding its opt-out mechanism.
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InCarEmo: A Multimodal Dataset for In-Cabin Emotion Recognition and Driver State Monitoring
Researchers released a multimodal dataset combining visual and conversational data to improve emotion recognition and state monitoring for automotive safety systems.
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EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
This study explores the integration of transformer-based AI models with EEG data to control wheelchairs through motor imagery.
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Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
This paper proposes a new method for evaluating epistemic uncertainty in machine learning models by focusing on the identification of reducible error rather than traditional out-of-distribution detection.
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Digital Pantheon: Simulating and Auditing Coalition Formation with LLM Agents
A multi-agent simulation framework has been created to study political coalition formation while bypassing standard LLM neutrality biases.
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BrainPilot: Automating Brain Discovery with Agentic Research
The authors introduce an agentic AI system designed to automate complex research workflows within the field of neuroscience.
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SeeSE3: Emergence of 3D Space in Vision Features
This study investigates whether vision foundation models inherently learn 3D spatial representations by analyzing the relationship between visual features and Euclidean transformations.
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A framework for single and multi-agent human-AI curiosity ecosystems
This paper introduces a theoretical framework for modeling curiosity in single and multi-agent AI systems based on decision-making trade-offs.
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Does generative AI supersede supervised XMLC? A Benchmark Study on Automated Subject Indexing with German Scientific Literature
This study compares the performance of generative AI against specialized supervised classification methods for the automated indexing of scientific literature.
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Grow-Prune-Freeze Networks: Adaptive & Continual Learning Technique for Olfactory Navigation
The Grow-Prune-Freeze network architecture provides a method for agents to perform continual learning in dynamic environments, specifically applied to olfactory navigation tasks.
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Benchmarking Multimodal Large Language Models for Scientific Visualization Literacy
Researchers assessed six multimodal large language models on their ability to interpret and understand scientific visualizations.
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Subjective functions
This paper explores the theoretical framework of subjective functions as a mechanism to enable artificial agents to autonomously generate and pursue their own goals.
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Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models
This paper introduces a multidimensional framework designed to standardize the evaluation of explainability methods in machine learning models.
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Neuro-Symbolic Strong-AI Robots with Closed Knowledge Assumption: Learning and Deductions
The paper explores a neuro-symbolic approach to AGI, focusing on how robots can learn and perform deductions using a closed knowledge assumption.
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Spatial Normalization for Cross-Domain Retinal Layer Segmentation in Optical Coherence Tomography
This research introduces a spatial normalization technique to improve the accuracy of retinal layer segmentation in medical imaging across different domains.
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"Skill Issues'': Data-Centric Optimization of Lakehouse Agents
The authors examine methods for optimizing the skill sets and environmental configurations of coding agents to improve their performance on data lakehouse infrastructure.
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Volition Elicitation: Operational Semantics for People and Their Machines
The study introduces a formal framework for managing multi-agent transactions between humans and their personal devices based on user intent.