Search: “models”

arXiv:2605.24436v1 Announce Type: cross Abstract: Selecting the most suitable algorithm for a given problem instance remains a challenging task, particularly in online or dynamic environments where pr

arXiv:2605.24406v1 Announce Type: new Abstract: Optimizing HVAC (Heating, Ventilation and Air Conditioning) can enhance a building's energy efficiency while providing comfort levels for its occupants.

arXiv:2605.26072v1 Announce Type: new Abstract: Efficient learning of user preferences is crucial for many modern decision making systems but typically requires costly labeled data. Active learning re

arXiv:2605.24343v1 Announce Type: new Abstract: Human-AI collaboration requires agents that can adapt to diverse partner behaviors and skill levels while remaining robust to unseen partners. Existing

arXiv:2506.04805v2 Announce Type: replace Abstract: Loss spikes commonly emerge during neural network training with the Adam optimizer across diverse architectures and scales, yet their underlying mec

arXiv:2602.13203v2 Announce Type: replace-cross Abstract: Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and d

arXiv:2605.24673v1 Announce Type: cross Abstract: We generalize finite-sample bounds for convex clustering to the setting where affinity weights appearing in the objective correspond to a general conn

arXiv:2605.24542v1 Announce Type: cross Abstract: This paper examines the erosion of Public Key Cryptography (PKC) security under adaptive adversarial optimisation driven by artificial intelligence. T

arXiv:2605.23905v1 Announce Type: cross Abstract: We show that AI-driven investment strategies are inherently self-defeating at scale. As AI adoption rises, three mutually reinforcing channels -- sign

arXiv:2605.24457v1 Announce Type: cross Abstract: Data streams in real-world industrial scenarios often contain transitional operating conditions that are uncovered during offline training, leading to

arXiv:2605.25489v1 Announce Type: new Abstract: Visual analytics (VA) workflows are inherently complex, involving data transformation, feature engineering, visual representation, and human interpretat

Automate a loan underwriting pipeline in just a few lines of code✨️ A typical loan file is a stack of pay stubs and brokerage statements, every one formatted differently, every number re-typed by hand

B9320 is a release of llama.cpp, a C/C++ implementation for enabling LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware locally and in the cloud. The project

Release b9333 of llama.cpp adds support for Apple device IDs in the Metal framework for GPU acceleration on macOS and iOS . The build includes compiled binaries for multiple platforms including macOS,

arXiv:2605.24611v1 Announce Type: new Abstract: Classical Hopfield networks are limited to static patterns due to symmetric weights, whereas asymmetric networks can encode temporal sequences via limit

arXiv:2605.24684v1 Announce Type: cross Abstract: Multimodal Attributed Graph Learning (MAGL) integrates intrinsic node attributes with structural topology via graph aggregation. However, as pretraine

arXiv:2602.05448v4 Announce Type: replace Abstract: Selecting the top m from n items via expensive k-wise comparisons is central to settings ranging from LLM-based document reranking to crowdsourced e

arXiv:2605.25826v1 Announce Type: cross Abstract: We develop a branched signature kernel solver for linear and nonlinear ordinary differential equations driven by a single observed trajectory of a pos

arXiv:2510.19731v3 Announce Type: replace-cross Abstract: HAPS are emerging as key enablers in the evolution of 6G wireless networks, bridging terrestrial and non-terrestrial infrastructures. Operatin

Building agents (like software) is a deeply iterative process --> this is why we try to supply as much easy to use tooling and infra as possible so builders can start on their agent improvement journe

arXiv:2605.24609v1 Announce Type: cross Abstract: Artificial intelligence is increasingly integrated into radiotherapy workflows, yet such pipelines remain vulnerable to out-of-distribution image data

arXiv:2605.24076v1 Announce Type: cross Abstract: Modern Artificial Intelligence achieves remarkable predictive power by optimizing statistical risk functionals over vast corpora. Yet a gap separates

arXiv:2605.26089v1 Announce Type: cross Abstract: We present Channel-wise Vector Quantization (CVQ), a novel image tokenization paradigm that replaces patch-wise tokens with channel-wise tokens. Unlik

arXiv:2510.19328v2 Announce Type: replace Abstract: Ensuring that predicted probabilities align with observed frequencies is critical in high-stakes domains such as clinical decision support, autonomo

arXiv:2605.24422v1 Announce Type: cross Abstract: Stochastic Dominance (SD) theory provides a rigorous framework for selecting superior assets tailored to the asset allocation needs of investors with

arXiv:2605.24294v1 Announce Type: cross Abstract: Android malware detectors often degrade after deployment because of concept drift, while full retraining at each maintenance step is costly. We propos

arXiv:2605.25354v1 Announce Type: new Abstract: While LLMs excel at reasoning over prompts using static pretrained knowledge, they struggle significantly with context learning-the ability to dynamical

arXiv:2602.08499v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is an effective paradigm for improving the reasoning capabilities of large language mode

arXiv:2605.24253v1 Announce Type: cross Abstract: Digital pathology archives increasingly contain multiple whole-slide images (WSIs) per case, capturing spatially distinct tumour regions and reflectin

arXiv:2605.25518v1 Announce Type: cross Abstract: Breast ultrasound imaging is an important noninvasive method for early breast cancer diagnosis, but automatic benign/malignant classification remains

arXiv:2605.25062v1 Announce Type: cross Abstract: The dominant artificial intelligence paradigm trains neural architectures via gradient descent against proxy objectives and reinforcement learning fro

arXiv:2605.24164v1 Announce Type: new Abstract: We describe our submission to the CLPsych~2026 Shared Task on capturing and characterizing mental health changes through social media timeline dynamics.

arXiv:2605.25749v1 Announce Type: cross Abstract: In multi-stage recommender systems, reranking optimizes overall utility by capturing intra-list contextual dependencies, yet its central challenge lie

Application security platform company Detectify AB today launched the Detectify MCP Server, a new integration layer that plugs the company’s security testing engines into artificial intelligence-drive

arXiv:2602.05961v2 Announce Type: replace Abstract: Sampling from a distribution p(x) propto e^{-E(x)} known up to a normalising constant is an important and challenging problem in statistics. Recent

arXiv:2601.21670v3 Announce Type: replace-cross Abstract: Multimodal fusion is often treated as an optimization-balancing problem, where training signals are adjusted to prevent one modality from domi

arXiv:2605.23910v1 Announce Type: cross Abstract: Information fusion is used widely to improve document classification by the integration of multiple data sources (multimodal) or representations (mult

arXiv:2509.12196v2 Announce Type: replace-cross Abstract: Standard attention mechanisms in transformers employ static token representations that remain unchanged across all pair-wise computations in e

arXiv:2605.24767v1 Announce Type: new Abstract: Accurate and reliable navigation is essential for autonomous ground vehicle operations. Standard INS/GNSS fusion relies on GNSS position updates, which

arXiv:2512.24075v5 Announce Type: replace Abstract: Early lane-change intention prediction is essential for autonomous driving and ADAS, but it remains challenging because lane-changing behavior depen

arXiv:2605.24996v1 Announce Type: new Abstract: Cognitive distortions, distorted patterns of thinking, have been increasingly studied in computational mental health research. Although they are related

arXiv:2605.24057v1 Announce Type: cross Abstract: Neural networks acquire structured representations at specific moments during training, yet identifying these transitions typically relies on retrospe

arXiv:2605.24386v1 Announce Type: cross Abstract: Fermi-Dirac machines were proposed recently as an approach to solving semidefinite optimization problems on quantum computers. Here, we reinterpret th

arXiv:2502.01184v2 Announce Type: replace-cross Abstract: Molecular representation learning methods typically tokenize molecules as individual atoms or use rigid, rule-based fragment decompositions, l

arXiv:2605.23955v1 Announce Type: new Abstract: Deploying machine learning in regulated financial environments -- credit risk, fraud detection, and anti-money laundering -- exposes critical vulnerabil

arXiv:2605.25646v1 Announce Type: new Abstract: Autonomous ground robots operating in large-scale outdoor environments require both robust long-range navigation and fine-grained ''last-mile'' explorat