As training scales grow, collective communication libraries (CCL) increasingly face anomalies arising from complex interactions among hardware, software, and en...
Generative Recommender (GR) inference places embedding hot caches (EMB) and KV caches in direct competition for limited GPU HBM: allocating more memory to one i...
The class of 2026 is the first generation to start and finish college with ChatGPT. They arrived on campus in the fall of 2022 just as AI was beginning to resha...
The usage of large language models (LLMs) has grown increasingly fragmented, with no single model dominating. Meanwhile, cloud providers offer a wide range of m...
Asha Sharma confirmed that Xbox will wind down Copilot on mobile and halt console development entirely. In a memo to staff on Tuesday, the new CEO outlined swee...
Tail latency dominates the performance of synchronous pretraining jobs when running at very large scales. We describe a three-pronged approach: (1) a new RDMA-b...
Understanding the neural mechanisms underlying visual computation has long been a central challenge in neuroscience. Recent alignment based approaches have impr...
Interactive multi-objective optimization systems face a budget allocation dilemma: one can spend resources on expensive objective evaluations or on eliciting de...
We introduce PALACE (Persistence Adaptive-Landmark Analytic Classification Engine), the data-adaptive companion to PLACE, paying a small cross-validation tier o...
Audio-Visual Intelligence (AVI) has emerged as a central frontier in artificial intelligence, bridging auditory and visual modalities to enable machines that ca...
Visual correspondence across image-to-image (2D-2D), image-to-point cloud (2D-3D), and point cloud-to-point cloud (3D-3D) geometric matching forms the foundatio...
Text-to-image generation has advanced rapidly with diffusion models, progressing from CLIP and T5 conditioning to unified systems where a single LLM backbone ha...
Clinical LLMs are often scaled by increasing model size, context length, retrieval complexity, or inference-time compute, with the implicit expectation that hig...
Deep search capabilities have become an indispensable competency for frontier Large Language Model (LLM) agents, yet their development remains dominated by indu...
We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an...
AI systems are entering critical domains like healthcare, finance, and defense, yet remain vulnerable to adversarial attacks. While AI red teaming is a primary ...
Reasoning-intensive retrieval aims to surface evidence that supports downstream reasoning rather than merely matching topical similarity. This capability is inc...
Sampling from unnormalized multimodal distributions with limited density evaluations remains a fundamental challenge in machine learning and natural sciences. S...
Language models excel at diagnostic assessments on currated medical case-studies and vignettes, performing on par with, or better than, clinical professionals. ...
A brain tumor is a medical disorder faced by individuals of all demographics. Medically, it is described as the spread of non-essential cells close to or throug...
High-precision CNC machining of free-form aerospace components requires bounded compensations informed by inspection, simulation, and process knowledge. Off-the...
Vision Transformers (ViTs) achieve state-of-the-art segmentation accuracy but require large training datasets because each layer has unique parameters that must...
Emergency department triage assigns patients an acuity score that determines treatment priority, and clinical evidence documents persistent gender disparities i...
Nowadays as convolution neural networks demonstrate its powerful problem-solving ability in the area of image processing, efforts have been made to reconstruct ...
Retrieval-augmented generation systems often assume that one fixed retrieval pipeline is sufficient across heterogeneous tasks, yet factoid question answering, ...
Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications. However...
Sampling from unnormalized densities is analogous to the generative modeling problem, but the target distribution is defined by a known energy function instead ...
'Enterprise AI: From Generation to Action How should AI act in the enterprise?
Large Language Models (LLMs) are prone to factual hallucinations, risking their reliability in real-world applications. Existing hallucination detectors mainly ...
AI-generated text is nowadays produced at scale across domains and heterogeneous generation pipelines, making robustness to distribution shift a central require...
Accurate school detection is essential for supporting education initiatives, including infrastructure planning and expanding internet connectivity to underserve...
Training machine learning interatomic potentials (MLIPs) for reactive chemistry is often bottlenecked by the high cost of quantum chemical labels and the scarci...
Several recent Transformer architectures expose later layers to representations computed in the earliest layers, motivated by the observation that low-level fea...
Coding agents often pass per-prompt safety review yet ship exploitable code when their tasks are decomposed into routine engineering tickets. The challenge is s...
Although recent LMMs have become much stronger at visual perception, they remain unreliable on problems that require multi-step reasoning over visual evidence. ...
Standard differential privacy imposes uniform privacy constraints across all features, overlooking the inherent distinction between sensitive and insensitive fe...
Reservoir characterization workflows increasingly rely on image-based and machine-learning/deep learning or even generative AI approaches, but openly available ...
Conceptual analysis -- proposing definitions and refining them through counterexamples -- is central to philosophical methodology. We study whether language mod...
Question: Does atomic fact-checking, which decomposes AI treatment recommendations into individually verifiable claims linked to source guideline documents, inc...
Large language models can be steered at inference time through prompting or activation interventions, but activation steering methods often underperform compare...
This paper presents a reproducible and process-aware pipeline for predictive monitoring of clinical pathways. The approach integrates data lifting, temporal rec...
This paper analyzes the strategic education process aimed at transitioning traditional software development squads into hybrid structures centered on collaborat...
We address day-ahead transmission topology planning and congestion management as a sequential, multi-objective optimization problem and develop two complementar...
Human involvement is critical in training and deploying AI systems in high-stakes defence and security contexts. However, real-time interaction is impractical i...
!Cover image for AI Button UX — Where to Put It, How to Label It, What to Show While Waitinghttps://media2.dev.to/dynamic/image/width=1000,height=420,fit=cover,...
Understanding how biological and artificial neural networks implement computation from connectivity is a central problem in neuroscience and machine learning. I...
Understanding how biological and artificial neural networks implement computation from connectivity is a central problem in neuroscience and machine learning. I...
Recurrent networks that store position, phase, or other continuous variables need state-space directions that remain neutral over long horizons. We give a symme...
