Search: “models”

arXiv:2604.24191v1 Announce Type: new Abstract: Omnimodal understanding entails a massive, highly redundant search space of cross-modal interactions, demanding focused and deliberative reasoning. Curr

arXiv:2602.19035v2 Announce Type: replace Abstract: We introduce OpenVO, a novel framework for Open-world Visual Odometry (VO) with temporal awareness under limited input conditions. OpenVO effectivel

This article discusses the practical implementation of AI systems in government agencies to detect and prevent fraudulent activities, likely covering deployment challenges, best practices, and real-wo

OpenAI outlines its commitment to implementing safety measures and responsible practices in the development and deployment of AI systems to protect users and communities. The statement likely covers O

arXiv:2604.24071v1 Announce Type: new Abstract: The increasing scale and variability of peer review in scholarly venues has created an urgent need for systematic, interpretable, and extensible tools t

arXiv:2604.23574v1 Announce Type: new Abstract: Existing image-to-video generation methods often produce physically implausible motions and lack precise control over object dynamics. While prior appro

arXiv:2604.24586v1 Announce Type: new Abstract: Single-image point cloud reconstruction must infer complete 3D geometry, including occluded parts, from a single RGB image. While diffusion-based recons

arXiv:2604.23922v1 Announce Type: cross Abstract: In this paper, we introduce the Quasi-Quadratic Gradient (QQG), a novel search direction designed to accelerate the BFGS method within the quasi-Newto

arXiv:2604.22911v1 Announce Type: new Abstract: Humanoid robots operating in unstructured environments must recover from unexpected disturbances-a capability that remains challenging for end-to-end co

arXiv:2604.24334v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation (RAG) chunking methods often create excessive redundancy, increasing storage costs and slowing retrieval. This s

arXiv:2604.22967v1 Announce Type: cross Abstract: Trust Region Bayesian Optimization (TuRBO) is an effective strategy for alleviating the curse of dimensionality in high-dimensional black-box optimiza

arXiv:2604.23875v1 Announce Type: cross Abstract: Noisy labels are a pervasive challenge in medical image classification, where annotation errors arise from inter-observer variability and diagnostic a

arXiv:2603.04603v2 Announce Type: replace-cross Abstract: We present a risk-aware formalism for evaluating system trajectories in the presence of uncertain interactions between the system and its envi

arXiv:2604.24163v1 Announce Type: new Abstract: Robustness is a long-overlooked problem in deepfake detection. However, detection performance is nearly worthless in the real world if it suffers under

arXiv:2604.23648v1 Announce Type: new Abstract: Convex free regions provide a structured and optimization-friendly representation of collision-free space for robot navigation in unknown and cluttered

arXiv:2603.13502v2 Announce Type: replace Abstract: Wirelessly-connected robotic systems empower robots with real-time intelligence by leveraging remote computing resources for decision-making. Howeve

arXiv:2603.20738v2 Announce Type: replace Abstract: Cross-subject EEG-to-image retrieval for visual decoding is challenged by subject shift and hubness in the embedding space, which distort similarity

arXiv:2604.24313v1 Announce Type: cross Abstract: Training large-scale deep neural networks effectively and stably is essential for applying deep learning across various fields. However, conventional

arXiv:2509.00747v2 Announce Type: replace-cross Abstract: Learning with physical systems is an emerging paradigm that seeks to harness the intrinsic nonlinear dynamics of physical substrates for learn

arXiv:2604.24498v1 Announce Type: new Abstract: Modern self-supervised representation learning methods often relies on empirical heuristics that are not theoretically grounded. In this study we propos

arXiv:2604.23046v1 Announce Type: new Abstract: We argue that current definitions of machine unlearning are underspecified for second-order optimizers. We compare first-order and second-order learners

arXiv:2506.18493v2 Announce Type: replace Abstract: Customizing image generation remains a core challenge in controllable image synthesis. For single-concept generation, maintaining both identity pres

// Skill Retrieval Augmentation for Agentic AI // Great read for AI devs. (bookmark it) It's on finding efficient ways to incorporate skills for agents. The work introduces Skill Retrieval Augmentatio

arXiv:2512.03028v3 Announce Type: replace-cross Abstract: Data-driven motion priors that can guide agents toward producing naturalistic behaviors play a pivotal role in creating life-like virtual char

Erin Woo / The Information: Source: Google signed a deal allowing the US DOD to use Google's AI for “any lawful government purpose”; Google says the agreement amends an existing contract — Google and

Nellie Peyton / Reuters: South Africa withdraws its first draft national AI policy after revelations that it contained fictitious sources that appeared to have been AI-generated — South Africa has wit

arXiv:2604.22892v1 Announce Type: new Abstract: Feature selection in high-dimensional genomic data (d gg n) demands methods that are simultaneously accurate, sparse, and stable. Existing approaches ei

arXiv:2604.23309v1 Announce Type: new Abstract: Remote sensing image change captioning (RSICC) aims to describe the difference between two remote sensing images. While recent methods have explored vid

arXiv:2309.06020v3 Announce Type: replace-cross Abstract: Technical debt refers to the consequences of sub-optimal decisions made during software development that prioritize short-term benefits over l

Google LLC and Anthropic PBC are two of the most powerful forces shaping enterprise AI. As agentic deployments demand platforms that are both capable and safe to build on, the Google-Anthropic partner

this is a great read on thinking through how to simplify agent orchestration to it’s core interestingly converges on an architecture very similar to babyagi 2, which stored functions (agent tools) in

Thoughtful book on LLMs and linguistics — by someone who actually knows linguistics. Interested in the LLM vs. linguistic theory debate? My just out book will tell you (almost) all about it. https://m

arXiv:2604.23844v1 Announce Type: new Abstract: Cross-Lingual Text Simplification (CLTS) aims to make content more accessible across languages by simultaneously addressing both linguistic complexity a

arXiv:2507.04503v2 Announce Type: replace Abstract: Accurate localization using visual information is a critical yet challenging task, especially in urban environments where nearby buildings and const

arXiv:2604.23141v1 Announce Type: cross Abstract: Emerging AR-LLM-based Social Engineering attack (e.g., SEAR) is at the edge of posing great threats to real-world social life. In such AR-LLM-SE attac

arXiv:2604.23102v1 Announce Type: new Abstract: Standard evaluations of Bayesian deep learning methods assume that metric estimates are reliable, but we show this assumption fails under data scarcity.

arXiv:2604.23953v1 Announce Type: cross Abstract: Blind omnidirectional image quality assessment (BOIQA) presents a great challenge to the visual quality assessment community, due to different storage

arXiv:2604.23193v1 Announce Type: cross Abstract: Perturbing a deterministic n-dimensional matrix with small Gaussian noise is a cornerstone of smoothed analysis of algorithms [Spielman and Teng, JACM

arXiv:2604.23593v1 Announce Type: new Abstract: The volume of scientific submissions continues to climb, outpacing the capacity of qualified human referees and stretching editorial timelines. At the s

arXiv:2603.24231v2 Announce Type: replace Abstract: Stance detection is nearly always formulated as classifying text into Favor, Against, or Neutral. This convention was inherited from debate analysis

arXiv:2604.23351v1 Announce Type: cross Abstract: Whether intermediate reasoning is computationally useful or merely explanatory depends on whether chain-of-thought (CoT) tokens contain task-relevant

arXiv:2604.23528v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide a promising machine learning framework for solving partial differential equations, but their training o

arXiv:2511.13211v2 Announce Type: replace Abstract: Despite recent advancements in 3D-text cross-modal alignment, existing state-of-the-art methods still struggle to align fine-grained textual semanti

arXiv:2604.21961v1 Announce Type: cross Abstract: Optimization problems are fundamental in diverse fields, such as engineering, economics, and scientific computing. However, current algorithms are mos

arXiv:2604.22256v1 Announce Type: cross Abstract: Goal recognition aims to infer an agent's goal from observations of its behaviour. In realistic settings, recognition can benefit from exploiting hier

arXiv:2604.22535v1 Announce Type: new Abstract: Objective: To propose and retrospectively validate an integrated framework addressing three barriers to clinical translation of readmission prediction: