3,694 items tracked: 32 reported by two or more outlets, the rest by a single source. Most-corroborated first.
arXiv:2610.14031v1 Announce Type: cross Abstract: Modern data pipelines increasingly combine human-created and human-curated data with AI-generated data for model training and evaluation.
arXiv:2610.14041v1 Announce Type: cross Abstract: Inverse Reinforcement Learning (IRL) is classically solved using Lagrangian optimisation, by jointly optimising a primal-dual problem with gradient descent to obtain the…
arXiv:2610.14042v1 Announce Type: cross Abstract: A longstanding goal for robot reinforcement learning (RL) is that robots learn from their own physical experience, as humans and animals do.
arXiv:2610.14059v1 Announce Type: cross Abstract: Rapid advances in AI have opened a new era for physics, enabling unprecedented capabilities in research and discovery.
arXiv:2610.14106v1 Announce Type: cross Abstract: Large language models can translate natural-language instructions into plausible manipulation plans, but robotic execution requires physically grounded decisions that res…
arXiv:2610.14151v1 Announce Type: cross Abstract: Reliable probability estimates are important for the clinical interpretation of machine-learning classifiers, yet radiomics--image fusion models for renal cell carcinoma…
arXiv:2610.14152v1 Announce Type: cross Abstract: Gate coefficients describe how a fusion layer weights intermediate features, but they do not necessarily reflect the magnitude of branch contributions at the classifier o…
arXiv:2610.14219v1 Announce Type: cross Abstract: Softmax attention in Transformers suffers from quadratic complexity in sequence length, making it impractical for long-context applications.
arXiv:2610.14248v1 Announce Type: cross Abstract: Diffusion models provide expressive trajectory priors for multi-robot motion planning (MRMP), but scaling them to large teams requires coordinating individual motions wit…
arXiv:2610.14279v1 Announce Type: cross Abstract: Learning long-range dependencies with recurrent neural networks (RNNs) is difficult because of the vanishing gradient problem.
arXiv:2610.14315v1 Announce Type: cross Abstract: Reconstructing accurate camera poses and floor-plan layouts from sparse, unposed, wide-baseline RGB panoramas remains a challenging open problem.
arXiv:2610.14324v1 Announce Type: cross Abstract: Foundation models like V-JEPA are increasingly deployed across a heterogeneous edge-cloud continuum spanning smart glasses, phones, robots, and servers.
arXiv:2610.14332v1 Announce Type: cross Abstract: Accurate sampling from continuous-time generative models can require many model evaluations.
arXiv:2610.14372v1 Announce Type: cross Abstract: Dexterous robot hands can receive useful motion intent from teleoperation or learned policies, but successful execution also depends on contact decisions that these comma…
arXiv:2610.14385v1 Announce Type: cross Abstract: This article offers an overview of recent developments in deep learning from a control vantage point.
arXiv:2610.14406v1 Announce Type: cross Abstract: It is widely appreciated that robotic policies require long-term memory to perform complex tasks.
arXiv:2610.14413v1 Announce Type: cross Abstract: Computational coarse-graining aims to replace a sequence of updates with a cheaper module while preserving task performance.
arXiv:2610.14439v1 Announce Type: cross Abstract: Large multimodal models (LMMs) need to understand how entities move and interact as a scene develops.
arXiv:2610.14448v1 Announce Type: cross Abstract: Asynchronous rollout improves the efficiency of reinforcement learning with verifiable rewards (RLVR) by reducing under-utilization during long-tail generation.
arXiv:2610.14465v1 Announce Type: cross Abstract: Structured knowledge retrieval acquires task-relevant evidence from structured knowledge sources for downstream tasks.
arXiv:2610.14473v1 Announce Type: cross Abstract: We propose a new framework for banner ad auctions, leveraging diffusion models to allow for multiple bidders to co-win a single banner ad opportunity, and in particular i…
arXiv:2610.14474v1 Announce Type: cross Abstract: Medication recommendations seek to deliver personalized drug combinations based on patients' clinical information, where ensuring transparent decision-making and personal…
arXiv:2610.14500v1 Announce Type: cross Abstract: Many widely used activation functions, including ReLU, GELU, Swish, and Sigmoid, produce predominantly positive outputs, resulting in a nonzero post-activation mean that…
arXiv:2610.14514v1 Announce Type: cross Abstract: Diffusion models pose significant risks regarding the generation of illicit content and copyright violations.
arXiv:2610.14530v1 Announce Type: cross Abstract: Multi-robot systems (MRS) offer significant advantages over single-robot platforms by enabling information sharing and coordination for different tasks in complex environ…
arXiv:2610.14550v1 Announce Type: cross Abstract: Dynamic Product Advertising (DPA) retrieval systems are a core component of modern e-commerce advertising; however, their evaluation remains challenging.
arXiv:2610.14552v1 Announce Type: cross Abstract: Protocol specifications rarely justify executable behavior in one place: a transition may inherit its source state from a remote control block, name its events only by nu…
arXiv:2610.14615v1 Announce Type: cross Abstract: We study autoregressive token generation through the notion of sequential samplers: A circuit that samples the next symbol only given the previous ones and possibly a sma…
arXiv:2610.14624v1 Announce Type: cross Abstract: Interpreting Earth system conditions is central to understanding environmental risks and informing decisions across weather- and climate-sensitive sectors.
arXiv:2610.14654v1 Announce Type: cross Abstract: Autoregressive video generators synthesize long videos through chunk-wise rollouts, yet they are typically trained to predict only the immediate future.
arXiv:2610.14691v1 Announce Type: cross Abstract: Recent advances in 3D hand-object interaction (HOI) perception have opened new opportunities for learning dexterous robotic skills from human videos.
arXiv:2610.14697v1 Announce Type: cross Abstract: Speech-conditioned generation maps speech signals to target audio outputs.
arXiv:2610.14770v1 Announce Type: cross Abstract: Multimodal contrastive learning typically relies on manually specified scoring functions, such as cosine similarity, to quantify the similarity between representations fr…
arXiv:2610.14815v1 Announce Type: cross Abstract: Industrial IoT sensors produce continuous, high-volume data streams that predictive maintenance needs to analyse in real time.
arXiv:2610.14823v1 Announce Type: cross Abstract: Artificial intelligence is increasingly used to support product management tasks.
arXiv:2610.14826v1 Announce Type: cross Abstract: Recent alignment-free Text-to-Speech (TTS) models achieve high synthesis quality by removing explicit alignment modules and simplifying training.
arXiv:2610.14942v1 Announce Type: cross Abstract: Bilevel data selection is a fundamental task with numerous applications in machine learning and statistics.
arXiv:2610.14953v1 Announce Type: cross Abstract: Contrastive reinforcement learning (CRL) learns goal-conditioned values by associating state--action pairs with the states subsequently reached, assuming that no unobserv…
arXiv:2610.14982v1 Announce Type: cross Abstract: Recurrent algorithmic reasoners are trained with per-step hints on small graphs and run for many more steps on large ones.
arXiv:2610.14984v1 Announce Type: cross Abstract: Generative AI makes options cheap and shifts the bottleneck of creative work from generating options to screening them.
arXiv:2610.14988v1 Announce Type: cross Abstract: The average-reward setting for reinforcement learning is challenging because its Bellman operator is known to contract in span only when the underlying Markov chain is mi…
arXiv:2610.15032v1 Announce Type: cross Abstract: Industrial anomaly detection benchmarks have advanced rapidly, yet they remain largely static and therefore underrepresent deployment settings in which inspection systems…
arXiv:2610.15056v1 Announce Type: cross Abstract: Large language models can post-edit ASR by using linguistic context to compare N-best hypotheses and rewrite the transcript when needed, often improving WER without retra…
arXiv:2610.15061v1 Announce Type: cross Abstract: Accurate and efficient analysis of dental radiographs has become increasingly important for disease diagnosis and clinical decision making.
arXiv:2610.15082v1 Announce Type: cross Abstract: As text-to-speech (TTS) systems continue to advance, provenance tracking and security verification of synthesized speech have become increasingly important.
arXiv:2610.15091v1 Announce Type: cross Abstract: Argument structures represent how individual arguments interact within a discourse.
arXiv:2610.15092v1 Announce Type: cross Abstract: Quantization plays a crucial role in efficiently deploying deep learning models on resource-constrained devices.
arXiv:2610.15117v1 Announce Type: cross Abstract: To evaluate whether a compact bidirectional state-space autoencoder improves joint classification of species, cell type, and disease state from single-nucleus RNA-sequenc…
arXiv:2610.15128v1 Announce Type: cross Abstract: Modern AI systems increasingly combine large language models (LLMs) with human experts, using the LLM to handle routine queries while deferring uncertain cases for expert…
arXiv:2610.15236v1 Announce Type: cross Abstract: Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI).