A new study reveals that large language models (LLMs) in ubiquitous systems exhibit "Authority Inversion," where they trust user-provided natural-language claims over conflicting numerical sensor data, leading to near-ze…
Researchers have developed DRIVE, a dual-level skill modeling framework that separates web agent knowledge into natural language reasoning skills for transferable task logic and programmatic interaction skills for execut…
A new study finds that multi-turn reasoning systems fail primarily through "satisfiable drift"—where the model silently violates prior commitments while maintaining a logically consistent internal state—rather than throu…
Researchers developed MEMOR-E, a mobile quadruped robot with a tablet interface that assists Alzheimer's patients and caregivers with medication reminders, routine guidance, and memory-oriented interactions. The system u…
A new study models the interaction between users and AI recommendation systems, finding that the optimal number of product suggestions depends on the cost of user communication versus search effort. Researchers analyzed …
Researchers have developed PAT, an adaptive tensor parallelism method that dynamically reconfigures GPU resource allocation during the generation stage of synchronous RLHF training to address the bottleneck caused by res…
Researchers developed GazeWorld, a medical imaging model that learns from radiologists' eye-tracking data by treating their fixation sequences as trajectories through an image. The model autoregressively predicts latent …
Researchers have released Nano World Models, a minimalist codebase for future video prediction that uses diffusion forcing to enable controlled studies of world-modeling components. The open-source platform provides a un…
A systematic review of 75 studies published between 2017 and 2025 found that the effectiveness of self-supervised learning in medical imaging depends on aligning the pretext task with the downstream clinical objective, w…
Researchers have developed a brain-to-image system that decodes visual stimuli from EEG signals, achieving a mean Top-1 retrieval accuracy of 86.30% and a CLIP score of 0.903 for image reconstruction. The system uses mul…
Researchers have introduced IVR-R1, a reinforcement learning framework that iteratively realigns visual reasoning trajectories to correct errors in multimodal large language models. The method uses a reward-driven screen…
Researchers have developed Diff-Instruct with Diffused Reward (DIDR), a data-free trajectory-level alignment framework that propagates reward-tilted clean-image distributions across all noise levels to improve one-step t…
Researchers compared deep learning models against linear interpolation for reconstructing missing spectral bands in PlanetScope SuperDove satellite imagery across four lakes with algal bloom histories. The study found th…
Researchers introduced ActQuant, a sub-4-bit quantization framework for Vision-Language-Action (VLA) models that reduces computational demands for edge deployment. The method achieved 95% performance retention at 3 bits-…
Researchers developed a deep learning framework called DL-TMPFC that automatically calculates the TIMI Myocardial Perfusion Frame Count from coronary angiography to diagnose coronary microvascular dysfunction (CMVD). In …
Researchers have developed SkySeg, a collaborative framework enabling multiple unmanned aerial vehicles (UAVs) to perform real-time semantic segmentation onboard despite hardware constraints and environmental data shifts…
A new dissertation proposes three novel architectures to improve machine intelligence across vision-language tasks, including image captioning, visual dialog, and interactive instruction following. The GRIT model achieve…
Researchers have identified that hallucinations in large vision-language models (LVLMs) stem from route competition, where textual pathways override visual evidence during token decision-making. To address this, the team…
Researchers have developed a gaze-aligned artificial intelligence framework that predicts perceived cycling safety by integrating human eye-tracking data into image analysis. The model, called EG-PCS, uses vision transfo…
Researchers have developed EMMA, a physics-informed multimodal framework that recovers all identifiable dynamical parameters of a system directly from raw video, audio, and image-based time-series observations. The frame…