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Researchers have developed an interpretable AI framework that assesses suicide risk in metro stations by analyzing surveillance video, tracking passenger behavior, spatial context, and temporal dynamics. The system, whic…

Researchers have introduced VideoOdyssey, a benchmark designed to evaluate ultra-long-context and omni-modal video understanding in AI models. The benchmark features videos averaging 109 minutes across 11 domains, requir…

A team of researchers improved a DETR-based fusion transformer for the MaCVi 2026 Vision-to-Chart data association challenge by adding a dedicated MLP, QueryMLP, to explicitly predict a buoy's waterline contact point in …

Researchers have developed the GazeBehavior Annotation Toolkit (GBAT), a deep-learning-based system that automates the synchronization and annotation of egocentric eye-tracking and video data from child-caregiver interac…

Researchers have developed CoMoGen, a controllable video generation framework that creates realistic motion dynamics and interactions from a single binary mask sequence and an input image. The system uses a lightweight M…

Researchers demonstrated that multi-level Floyd-Steinberg error-diffusion dithering, a lightweight input transformation, can disrupt adversarial attacks against vision foundation models while preserving semantic content.…

Flow Mismatching, a new unsupervised anomaly detection method, avoids reconstruction-based paradigms by treating flow matching as geometric dynamics and identifying anomalies where learned normal flow disagrees with the …

Researchers have developed IaMSB, an inconsistency-aware multimodal Schrödinger Bridge framework that improves deepfake localization by jointly estimating cross-modal consistency and performing interval-level temporal ev…

Researchers have developed CoReVAD, a training-free video anomaly detection framework that uses a single frozen Vision-Language Model to generate both anomaly scores and temporal descriptions without additional training …

Researchers at the German Cancer Research Center (DKFZ) developed a new "Verified Tracking" paradigm for tumor lesion tracking across serial CT scans, achieving first place in the MICCAI autoPET IV challenge. The method …

Researchers have developed LQ-rPPG, a label-quantized coarse-to-fine learning framework that improves remote photoplethysmography (rPPG) estimation from facial videos by reducing noise in training labels. The framework u…

Researchers have introduced DriveSpatial, a benchmark of 15,600 human-verified question-answer pairs across 20 tasks designed to evaluate spatiotemporal intelligence in vision-language models for autonomous driving. Test…

A new study evaluating large language models in the social deduction game Secret Hitler found that current architectures remain ineffective at complex, multi-turn manipulation and deception. Models like Llama 3.1 70B ach…

Researchers have developed Query-Adaptive Semantic Chunking (QASC), a method that integrates user queries into the document segmentation process for Retrieval-Augmented Generation systems. In tests on 100 technical docum…

Researchers have developed a knowledge-aware Text-to-SQL framework that improves the performance of large language models in converting natural language questions into executable database queries, particularly in low-res…

Researchers have developed a red-teaming framework to measure the political steerability of open-source large language models (LLMs), finding that these models are more willing to generate left-leaning social media conte…

Researchers introduced Reflection-Augmented Scaling (RAS), a method that uses execution error messages from databases to improve Cypher query generation through in-context learning. Testing across three Neo4j datasets an…

Researchers have developed LIFT, a new fine-tuning algorithm for diffusion language models that improves reasoning capabilities by aligning token learning difficulty with the information available at different diffusion …

Researchers developed a graph neural network that models alignment topology between reference documents and LLM outputs to detect hallucinations, achieving state-of-the-art results on four datasets. The method outperform…

Researchers tested seven large language models on their ability to infer employee expertise from 27,188 Slack messages, finding that Gemini 2.5 Flash achieved the lowest estimation error at 21.13% mean absolute error. Th…