!https://cdn.arstechnica.net/wp-content/uploads/2026/05/aiwarm1.webp Across models and tasks, the model trained to be “warmer” ended up having a higher error ra...
If you've written C, used Linux, or shipped software in the last 30 years, you already understand how AI agents work. You just don't know it yet. The Problem N...
The AI scaffolding layer is collapsing. LlamaIndex's CEO explains what survives The scaffolding layer that developers once needed to ship LLM applications—inde...
Flow matching (FM) trains a time-dependent vector field that transports samples from a simple prior to a complex data distribution. However, for high-dimensiona...
We introduce HyCOP, a modular framework that learns parametric PDE solution operators by composing simple modules (advection, diffusion, learned closures, bound...
Large language models (LLMs) often achieve strong performance on reasoning benchmarks, but final-answer accuracy alone does not show whether they faithfully exe...
While autoregressive Large Vision-Language Models (LVLMs) demonstrate remarkable proficiency in multimodal tasks, they face a 'Visual Signal Dilution' phenomeno...
In this paper, we present Generative Language-Image Pre-training (GenLIP), a minimalist generative pretraining framework for Vision Transformers (ViTs) designed...
Large language models are increasingly deployed as autonomous coding agents and have achieved remarkably strong performance on software engineering benchmarks. ...
Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are not det...
Gaze estimation methods commonly use facial appearances to predict the direction of a person gaze. However, previous studies show three major challenges with co...
Humans solve problems by executing targeted plans, yet large language models (LLMs) remain unreliable for structured workflow execution. We propose RunAgent, a ...
Background: Patient-facing medical chatbots based on retrieval-augmented generation (RAG) are increasingly promoted to deliver accessible, grounded health infor...
With the development of deep learning, medical image processing has been widely used to assist clinical research. This paper focuses on the denoising problem of...
Key-Value (KV) cache has become a de facto component of modern Large Vision-Language Models (LVLMs) for inference. While it enhances decoding efficiency in Larg...
!Ansh Guptahttps://media2.dev.to/dynamic/image/width=50,height=50,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fu...
While representation and similarity learning have improved the sample efficiency of Reinforcement Learning (RL), they are rarely used to shape policy updates di...
Reliable spatial analysis in GIScience requires preserving coordinate semantics, topology, units, and geographic plausibility. Current LLM-based GIS systems gen...
3D world generation is essential for applications such as immersive content creation or autonomous driving simulation. Recent advances in 3D world generation ha...
In biomechanical systems, observable performance is often used as a proxy for underlying system organization. However, this assumption implicitly presumes a cor...
A speaker encoder used in multilingual voice cloning should treat the same speaker identically regardless of which script the audio was uttered in. Off-the-shel...
The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpfulness, co...
The transformer is the most popular neural architecture for language modeling. The cornerstone of the transformer is its global attention mechanism, which lets ...
Urban perception describes how people subjectively evaluate urban environments, shaping how cities are experienced and understood. Existing computational approa...
We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-bandit fee...
This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the DeepONet f...
!Cover image for My Journey with AI & Fashion MNISThttps://media2.dev.to/dynamic/image/width=1000,height=420,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fde...
Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scaling. Res...
Monte Carlo Tree Search (MCTS) scales poorly in cooperative multi-agent domains because expansion must consider an exponentially large set of joint actions, sev...
The U.S. Department of War has announced deals with seven of the world’s leading frontier artificial‑intelligence companies for operational use. According to th...
Background OpenAI CEO Sam Altman is currently embroiled in some courtroom dramahttps://mashable.com/article/musk-openai-trial-testimony, but the engineers back...
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Edge detection refers to identifying points in a digital image where intensity changes sharply, indicating object boundaries or structural features. Corners are...
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call...
Agentic AI architectures augment LLMs with external tools, unlocking strong capabilities. However, tool use is not always beneficial; some calls may be redundan...
Google has announced the general availability of Gemini Embedding 2, a unified model that maps text, images, video, audio, and documents into a single semantic...
!US Defense Secretary Pete Hegseth testifies before Congress.https://www.engadget.com/img/gallery/amazon-web-services-microsoft-and-nvidia-will-provide-ai-tech-...
Single-point supervised infrared small target detection (IRSTD) drastically reduces dense annotation costs. Current state-of-the-art (SOTA) methods achieve high...
Large language models (LLMs) are increasingly applied in financial scenarios. However, they may produce harmful outputs, including facilitating illegal activiti...
Large language model (LLM) agents require long-term user memory for consistent personalization, but limited context windows hinder tracking evolving preferences...
We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within tight que...
Distributed blackbox consensus optimization is a fundamental problem in multi-agent systems, where agents must improve a global objective using only local objec...
Sequence learning reduces to similarity-based retrieval over a temporally indexed representation space, a constraint on any sequence model, not a property of a ...
Let me set a scene. You deploy an AI agent to handle your customer‑data pipeline. It calls APIs, queries databases, writes files, even spawns subtasks. It’s fas...
Sparse MoE routing fails at domain transitions, where the current token belongs to one distribution and the next to another. In a controlled experiment (4 exper...
The hidden multiplier nobody budgets for When we moved from single‑turn chatbots to agentic workflows in early 2026, the first thing that broke wasn’t the code...
Quantization is a key method for reducing the GPU memory requirement of training large language models (LLMs). Yet, current approaches are ineffective for 4-bit...
Scaling laws for Large Language Models (LLMs) establish that model quality improves with computational scale, yet edge deployment imposes strict constraints on ...
