Amazon researchers developed a new AI architecture that cuts inference time by 30% by selecting only task-relevant neurons, similar to how the brain uses specialized regions for specific tasks. This breakthrough approach addresses one of the biggest challenges facing large AI models: the computational expense and latency associated with activating every neuron for every request,…
Embedding models act as bridges between different data modalities by encoding diverse multimodal information into a shared dense representation space. There have been advancements in embedding models in recent years, driven by progress in large foundation models. However, existing multimodal embedding models are trained on datasets such as MMEB and M-BEIR, with most focus only…
Medical image segmentation is at the heart of modern healthcare AI, enabling crucial tasks such as disease detection, progression monitoring, and personalized treatment planning. In disciplines like dermatology, radiology, and cardiology, the need for precise segmentation—assigning a class to every pixel in a medical image—is acute. Yet, the main obstacle remains: the scarcity of large, expertly…
Language model users often ask questions without enough detail, making it hard to understand what they want. For example, a question like “What book should I read next?” depends heavily on personal taste. At the same time, “How do antibiotics work?” should be answered differently depending on the user’s background knowledge. Current evaluation methods often…
Large Reasoning Models (LRMs) have rapidly advanced, exhibiting impressive performance in complex problem-solving tasks across domains like mathematics, coding, and scientific reasoning. However, current evaluation approaches primarily focus on single-question testing, which reveals significant limitations. This article introduces REST (Reasoning Evaluation through Simultaneous Testing) — a novel multi-problem stress-testing framework designed to push LRMs beyond isolated problem-solving…
The discipline of epigraphy, focused on studying texts inscribed on durable materials like stone and metal, provides critical firsthand evidence for understanding the Roman world. The field faces numerous challenges including fragmentary inscriptions, uncertain dating, diverse geographical provenance, widespread use of abbreviations, and a large and rapidly growing corpus of over 176,000 Latin inscriptions, with…
Advancements in artificial intelligence are rapidly closing the gap between digital reasoning and real-world interaction. At the forefront of this progress is embodied AI—the field focused on enabling robots to perceive, reason, and act effectively in physical environments. As industries look to automate complex spatial and temporal tasks—from household assistance to logistics—having AI systems that…
LLMs have demonstrated exceptional performance across multiple tasks by utilizing few-shot inference, also known as in-context learning (ICL). The main problem lies in selecting the most representative demonstrations from large training datasets. Early methods selected demonstrations based on relevance using similarity scores between each example and the input question. Current methods suggest using additional selection…
Existing long-CoT reasoning models have achieved state-of-the-art performance in mathematical reasoning by generating reasoning trajectories with iterative self-verification and refinement. However, open-source long-CoT models depend only on natural language reasoning traces, making them computationally expensive and prone to errors without verification mechanisms. Although tool-aided reasoning provides greater efficiency and reliability for large-scale numerical computations through…
Introduction Wearable devices are transforming health monitoring by enabling continuous collection of physiological and behavioral signals such as heart rate, activity, temperature, and skin conductance. However, the real-world data that these devices generate is highly prone to missingness due to sensor failures, device removal, charging, motion artifacts, battery-saving modes, and other interruptions. This presents a…