Causal Label Recovery in Payment Networks
arXiv:2605.29272v1 Announce Type: cross Abstract: Fraud detection models in payment networks train on chargeback labels that are systematically biased. Every label must survive three sequential gates:
Knowledge catalogue
arXiv:2605.29272v1 Announce Type: cross Abstract: Fraud detection models in payment networks train on chargeback labels that are systematically biased. Every label must survive three sequential gates:
arXiv:2605.29966v1 Announce Type: new Abstract: Marine lead (Pb) and its isotopes are critical tracers for ocean circulation and anthropogenic pollution, yet in-situ observations remain costly and spa
arXiv:2511.19316v2 Announce Type: replace-cross Abstract: Recent fine-tuning techniques for diffusion models enable them to reproduce specific image sets, such as particular faces or artistic styles,
arXiv:2605.29812v1 Announce Type: new Abstract: Video Moment Retrieval (VMR) targets to retrieve the specific moment corresponding to a sentence query from an untrimmed video. Although recent works ha
arXiv:2605.30148v1 Announce Type: cross Abstract: Evolution Strategies (ES) has recently emerged as a competitive alternative to reinforcement learning (RL) for large language model (LLM) fine-tuning,
arXiv:2605.29234v1 Announce Type: new Abstract: We study large-scale literature search from two complementary angles: improving the retrieval pipeline, and stress-testing the human reference list as a
arXiv:2605.29930v1 Announce Type: new Abstract: Mutual misunderstanding in contemporary society does not arise merely because people hold different opinions or values. Even under the same observations
arXiv:2605.28883v1 Announce Type: new Abstract: Tropical forests worldwide are under intense deforestation pressure driven by economic and political interests, and scientific evidence suggests this de
arXiv:2605.28640v1 Announce Type: new Abstract: Efficient inference is critical for long-context language models, where attention computation and KV-cache access dominate the cost. Recent work RAT+, i
arXiv:2602.15198v2 Announce Type: replace-cross Abstract: Multi-agent systems, where LLM agents communicate through free-form language, enable sophisticated coordination for solving complex cooperativ
arXiv:2605.27451v1 Announce Type: new Abstract: The 10th Affective & Behavior Analysis in-the-Wild (ABAW) Workshop and Competition, held at CVPR 2026, continues to advance research on modelling, analy
arXiv:2605.27433v1 Announce Type: cross Abstract: With the increasing complexity of collaboration among various social entities and user demands, the factors affecting the stable development of the da
arXiv:2605.27394v1 Announce Type: cross Abstract: Determining whether published scientific findings can successfully be replicated is a long-standing challenge in the empirical sciences. Existing appr
arXiv:2605.27865v1 Announce Type: new Abstract: Matching submissions with suitable reviewers at scale is a growing challenge for major venues, yet existing approaches either rely on coarse proxy signa
arXiv:2605.27456v1 Announce Type: new Abstract: Geometric deep learning organises neural architectures around the symmetries of their data domain, with the choice of symmetry group serving as a geomet
arXiv:2605.27827v1 Announce Type: new Abstract: AI governance frameworks increasingly emphasize fairness, transparency, accountability, and lifecycle risk management in high-stakes domains. However, m
arXiv:2605.27885v1 Announce Type: new Abstract: Various approaches have been proposed to adapt Vision-Language Models (VLMs) to specialized domains for Video Question Answering, including fine-tuning
arXiv:2605.28282v1 Announce Type: new Abstract: AI-assisted research compresses ideation, implementation, evaluation, and manuscript writing into a single interactive loop. This compression is useful,
arXiv:2605.27557v1 Announce Type: new Abstract: Card payment fraud detection is usually framed as a supervised classification problem. Although this approach has generated practical progress, improvem
arXiv:2605.28000v1 Announce Type: cross Abstract: Large language model agents are increasingly expected to perform operational work: calling APIs, manipulating files, assembling workflows, and acting
arXiv:2605.28102v1 Announce Type: new Abstract: Large language models trained with Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI exhibit persistent behavioral patterns that s
arXiv:2605.27740v1 Announce Type: new Abstract: Long-context inference in large language models (LLMs) is bottlenecked by the linear growth of the self-attention key-value (KV) cache. Top-k sparse att
arXiv:2605.26119v1 Announce Type: cross Abstract: Industrial Edge AI programs often begin with the model and only later confront the platform. That sequencing is attractive because it allows early dem
arXiv:2605.26166v1 Announce Type: cross Abstract: The rapid proliferation of Internet of Things (IoT) devices has created an urgent demand for adaptive, resource-efficient Intrusion Detection Systems
arXiv:2605.26332v1 Announce Type: cross Abstract: Machine unlearning aims to remove specific concepts from pretrained text-to-image diffusion models, yet several white- and black-box attacks have been
arXiv:2605.26508v1 Announce Type: cross Abstract: We propose a foundational runtime actuarial layer for autonomous AI agents in which every side-effect-bearing action carries a time-consistent, counte
arXiv:2605.26781v1 Announce Type: new Abstract: Advanced Large Multimodal Models (LMMs) have demonstrated impressive performance in K-12 reasoning tasks, exhibiting great promise as intelligent tutors
arXiv:2605.26908v1 Announce Type: new Abstract: Exploiting the indistinguishability of objects in a probabilistic graphical model such as a factor graph is key to lifted probabilistic inference algori
arXiv:2605.27014v1 Announce Type: cross Abstract: Large Language Models (LLMs) have transformed artificial intelligence from primarily generative systems into increasingly capable reasoning agents. Re
arXiv:1909.08210v4 Announce Type: replace Abstract: A restricted Boltzmann machine (RBM) is a two-layer neural network with shared weights and has been extensively studied for dimensionality reduction
arXiv:2605.26661v1 Announce Type: cross Abstract: Out-of-distribution (OOD) detection has emerged as a popular technique to enhance the reliability of machine learning models by identifying unexpected
arXiv:2510.09606v2 Announce Type: replace Abstract: With the current surge in spatial reasoning explorations, researchers have made significant progress in understanding indoor scenes, but still strug
arXiv:2605.25735v1 Announce Type: new Abstract: Problem formulation translating customer needs and constraints into a minimum set of independent first-level functional requirements, is arguably the mo
arXiv:2605.25549v1 Announce Type: cross Abstract: High-quality expert chain-of-thought (CoT) data is one of the core bottlenecks in large language model (LLM) post-training. Existing data production m
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
arXiv:2605.25290v1 Announce Type: cross Abstract: Online experiments in ads, recommendation, and member-experience systems are often planned before the dominant interference mechanism is known. A trea
arXiv:2605.25120v1 Announce Type: cross Abstract: Radiology reports remain the primary mechanism by which imaging findings are communicated to clinical teams. However, much of the structured informati
arXiv:2605.24728v1 Announce Type: new Abstract: Foundation models can increasingly describe, reconstruct, and generate 3D objects, assemblies, scenes, and environments, but visually plausible spatial
arXiv:2605.23952v1 Announce Type: new Abstract: Artificial agents now generate behavior rich enough to invite trust, surprise, and concern, yet our evaluation tools still privilege capability scores o
arXiv:2605.23917v1 Announce Type: new Abstract: Modern scientific discovery is bottlenecked not by data scarcity, but by the inability to synthesize fragmented knowledge into actionable hypotheses. Th
arXiv:2605.25271v1 Announce Type: cross Abstract: We study the symmetric polynomial prod_{alphain A_{n,d}}igl(1+alpha_1 x_1+dots+alpha_n x_nigr) where A_{n,d}:={alphainZ_{ge 0}^n:|alpha|=d}, which is
arXiv:2605.22978v1 Announce Type: new Abstract: Katharevousa Greek remains poorly served by contemporary NLP pipelines despite its importance for legal, administrative, and parliamentary archives. We
arXiv:2605.23448v1 Announce Type: cross Abstract: This work examines an imbalance in artificial intelligence (AI) security research: the field tends to produce more work on attacking AI systems than o
arXiv:2605.23569v1 Announce Type: new Abstract: Dynamic Programming (DP) and Constraint Programming (CP) are well-established paradigms for solving combinatorial optimization problems. Usually, these
Dropped this morning by the Vatican: Magnifica Humanitas of His Holiness Pope Leo XIV on Safeguarding the Human Person in the Time of Artificial Intelligence. This is a very interesting document. It's
arXiv:2605.23540v1 Announce Type: new Abstract: Dimensionality Reduction (DR) methods are widely used to visualize high-dimensional data. One key task in DR-based analysis is discovering neighborhoods
Researchers from the University of Gothenburg invented a fake disease called 'bixonimania,' a fictional skin condition supposedly caused by screen time. Multiple AI chatbots including Google's Gemini,
arXiv:2605.21552v1 Announce Type: new Abstract: Confidence calibration for classification models is vital in safety-critical decision-making scenarios and has received extensive attention. General con
Highlighting the new WebGPU backend in llama.cpp/ggml The work to bring full-fledged WebGPU support in llama.cpp started about an year and a half ago. It has been lead by @reeselevine and team at USCS
arXiv:2605.20303v1 Announce Type: new Abstract: Airfoil shape design is a fundamental task in aerospace engineering, with a direct impact on flight stability and fuel consumption. Deep learning has re
arXiv:2603.15842v2 Announce Type: replace Abstract: Modern machine learning systems increasingly rely on sensitive data, creating significant privacy, security, and regulatory risks that existing priv
arXiv:2603.23890v2 Announce Type: replace-cross Abstract: As the modern microservice architecture for cloud applications grows in popularity, cloud services are becoming increasingly complex and more
arXiv:2604.01449v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) systems are increasingly integrated into healthcare and pharmacy workflows, supporting tasks such as medication r
arXiv:2605.20173v1 Announce Type: new Abstract: Production LLM agents combine stochastic model outputs with deterministic software systems, yet the boundary between the two is rarely treated as a firs
arXiv:2002.09053v3 Announce Type: replace Abstract: Pedestrian detection benefits from deep learning technology and gains rapid development in recent years. Most of detectors follow general object det
Can we transform the Hugging Face Hub🤗—with its enormous sea of artifacts—into a self-evolving discovery machine? WE CAN. Introducing ArtifactLinker! 🚀🔥 We are fundamentally rethinking how we interact
arXiv:2605.19887v1 Announce Type: cross Abstract: Current Physical AI (PAI) relies heavily on closed-loop visual-servoing pipelines, whose perception and planning stages may become computationally int
arXiv:2605.19321v1 Announce Type: cross Abstract: Large Language Model (LLM) alignment remains vulnerable to jailbreak attacks that elicit unsafe responses, motivating pre-model and post-model guards.
arXiv:2407.20595v4 Announce Type: replace-cross Abstract: Deciding whether two pieces of text share an author is made difficult by topical confound: two writers covering the same topic often look more
arXiv:2407.13193v4 Announce Type: replace Abstract: Large language models (LLMs) have achieved strong empirical performance in various fields, benefiting from their huge amount of parameters that stor