All entries

arXiv:2605.02241v1 Announce Type: cross Abstract: How reliably can a small language model estimate its own correctness? The answer determines whether local-to-cloud routing-escalating queries a cheap

arXiv:2605.01331v1 Announce Type: new Abstract: Image steganalysis, which aims at detecting secret information concealed within images, has become a critical countermeasure for assessing the security

arXiv:2605.01787v1 Announce Type: cross Abstract: Autonomous navigation and obstacle avoidance remain a core challenge of modern Unmanned Aerial Vehicles (UAVs). While traditional control methods stru

arXiv:2605.02356v1 Announce Type: new Abstract: We introduce the Z-Domain Neural Operator (ZNO), a causal neural operator whose layers are stable low-rank multiple-input multiple-output (MIMO) rationa

Marissa Newman / Bloomberg: Zyg, an AI startup by ironSource's founders to automate business functions, raised 60M led by Accel at a 500M valuation, two months after exiting stealth — Zyg, an artifici

arXiv:2605.00569v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for generating photorealistic renderings of a scene in real-time. However, the volumetr

A $1 Billion one person company may look like a video game. That’s the view Andrew Pignanelli and the team at Intelligence Co are taking. We sat down with him on @11AMdotclub ahead of today’s launch.

A challenge with AI regulation and vetting is how bad our benchmarks of AI model performance and risks are. There is no benchmark for risks and red-teaming requires experiments from dedicated speciali

arXiv:2605.00279v1 Announce Type: cross Abstract: AI-powered edge computing security is moving Intelligent Transportation Systems (ITS) from passive, rule-based protections to proactive, smart, zero-t

arXiv:2605.00508v1 Announce Type: new Abstract: We present a unique, multitask dataset comprising 143 drug and drug candidate molecules, each evaluated on in vitro, parallel artificial-membrane permea

arXiv:2603.02275v2 Announce Type: replace Abstract: Uniform Manifold Approximation and Projection (UMAP) is a widely used manifold learning technique for dimensionality reduction. This paper studies U

IBM Research reflects on ten years of quantum computing development and deployment through cloud-based platforms, documenting the evolution from early experimental systems to more practical quantum pr

arXiv:2508.11696v3 Announce Type: replace Abstract: A deep learning real-time smoking detection system for CCTV surveillance of fire exit areas is proposed due to critical safety requirements. The dat

arXiv:2605.00284v1 Announce Type: new Abstract: Dirac-Frenkel instantaneous residual minimization evolves nonlinear parametrizations of PDE solutions in time, but ill-conditioning can render the param

arXiv:2605.00307v1 Announce Type: cross Abstract: Grasp force estimation can help prevent robots from damaging delicate objects during manipulation and improve learning-based robotic control. Integrat

arXiv:2512.12108v5 Announce Type: replace Abstract: The development of machine learning models based on computed tomography (CT) imaging has been a major focus due to the promise that imaging holds fo

arXiv:2605.00457v1 Announce Type: cross Abstract: The coexistence of NR-U and Wi-Fi in unlicensed spectrum introduces a system-level resource coordination problem, where heterogeneous channel access m

Jacob Shamsian / Business Insider: A profile of William Savitt, Sam Altman's lead lawyer against Elon Musk, who represented Twitter against Musk in 2022 and helped OpenAI's for-profit transition — - W

Gary Marcus explores hypothetical scenarios and consequences if OpenAI were to fail or lose its competitive position in the AI industry. The post likely examines implications for AI development, the b

arXiv:2409.14204v4 Announce Type: replace-cross Abstract: Deep learning has shown significant value in medical image registration for motion correction, however, current techniques are either limited

arXiv:2605.00229v1 Announce Type: cross Abstract: We study the problem of training diffusion and flow generative models to sample from target distributions defined by an exponential tilting of a base

A year ago Gabe and I started Torchbearer because the gap I kept seeing was not 'people who get it' but 'people who get it and have somewhere to put their effort.' That is what this is. Substack just

arXiv:2605.00551v1 Announce Type: new Abstract: AI agents that interact with graphical user interfaces (GUIs) require effective observation representations for reliable grounding. The accessibility tr

arXiv:2605.00650v1 Announce Type: new Abstract: Fine-tuning LLMs is necessary for various dedicated downstream tasks, but classic backpropagation-based fine-tuning methods require substantial GPU memo

arXiv:2507.18064v2 Announce Type: replace Abstract: Most existing low-light image enhancement (LLIE) methods rely on pre-trained model priors, low-light inputs, or both, while neglecting the semantic

arXiv:2603.22908v3 Announce Type: replace Abstract: Assuming that neither source data nor source model parameters are accessible, black-box domain adaptation (BBDA) represents a highly practical yet c

arXiv:2605.00443v1 Announce Type: cross Abstract: The advancement of generalized deepfake disruption is constrained by the interruption imbalance, a fundamental bottleneck inherent to the generation o

arXiv:2605.00233v1 Announce Type: new Abstract: Egocentric pose estimation for Augmented Reality (AR) and assistive devices requires not just accurate predictions but guaranteed uncertainty regions. C

arXiv:2510.03096v2 Announce Type: replace Abstract: We propose an adaptive node feature selection approach for graph neural networks (GNNs) that identifies and removes unnecessary features during trai

arXiv:2605.00171v1 Announce Type: cross Abstract: In this paper, we study norm-based regularization methods for neural networks. We compare existing penalization approaches and introduce two regulariz

arXiv:2605.00696v1 Announce Type: cross Abstract: We study adaptive querying for learning user-dependent quantities of interest, such as responses to held-out items and psychometric indicators, within

arXiv:2605.00374v1 Announce Type: new Abstract: Edge classification, a crucial task for graph applications, remains relatively under-explored compared to link prediction. Current methods often overloo

arXiv:2510.10913v3 Announce Type: replace Abstract: Recent progress in large language models (LLMs) has enabled them to communicate their confidence in natural language, improving transparency and rel

arXiv:2605.00604v1 Announce Type: new Abstract: Sparse MoE routing fails at domain transitions, where the current token belongs to one distribution and the next to another. In a controlled experiment

arXiv:2605.00663v1 Announce Type: cross Abstract: Affordance grounding requires identifying where and how an agent should interact in open-world scenes, where actionable regions are often small, occlu

arXiv:2605.00410v1 Announce Type: new Abstract: A multi-agent pipeline with N agents typically issues N LLM calls per run. Merging agents into fewer calls (compound execution) promises token savings,

arXiv:2603.25719v2 Announce Type: replace-cross Abstract: We present an empirical study of how far general-purpose coding agents -- without hardware-specific training -- can optimize hardware designs

Amazon SageMaker AI now offers an agentic experience that changes this. Developers describe their use case using natural language, and the AI coding agent streamlines the entire journey, from use case

arXiv:2605.00334v1 Announce Type: cross Abstract: Production agentic systems make many model calls per user request, and most of those calls are short, structured, and routine. This raises a practical

arXiv:2605.00383v1 Announce Type: new Abstract: The delivery of traditional substance education has remained problematic due to challenges in scalability, personalization, and the currency of informat

The rapid rise of agentic development has radically transformed the software engineering landscape, compelling enterprises to embrace a multi-model AI ecosystem. The pace of change in software develop

Agents are now the primary consumers of knowledge infrastructure. Tomorrow @jennapederson, Arjun Patel, and @RoieSchwabco are going live here to talk about what that means for your stack, share this w

arXiv:2605.00539v1 Announce Type: new Abstract: Quantization is a key method for reducing the GPU memory requirement of training large language models (LLMs). Yet, current approaches are ineffective f

Cerebras Systems Inc., a maker of supersized artificial intelligence chips, today disclosed the financial terms of its upcoming public offering. The company hopes to raise as much as 3.5 billion by se

Most discussions about artificial intelligence security are focused on what models might do wrong. The more urgent issue is what our detection systems still cannot see — and side-channel attacks are m

AI morality should be grounded in durable principles like being maximally truth seeking, enabling human agency, freedom of thought + humility under uncertainty Instead of inheriting the moral fashion