Evaluating Financial Sentiment in the Age of AI
arXiv:2609.20198v1 Announce Type: new Abstract: Financial sentiment measures are widely used in empirical finance, but it remains unclear whether general-purpose large language models (LLMs) improve o
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arXiv:2609.20198v1 Announce Type: new Abstract: Financial sentiment measures are widely used in empirical finance, but it remains unclear whether general-purpose large language models (LLMs) improve o
arXiv:2609.19740v1 Announce Type: new Abstract: Recent innovations in deep learning have significantly enhanced the diagnosis of medical images, although they are based on the use of centralized data
arXiv:2609.19445v1 Announce Type: cross Abstract: The rapid expansion of multimodal models has surfaced formidable bottlenecks in computation, memory, and deployment, catalyzing the rise of Efficient
arXiv:2609.19209v1 Announce Type: new Abstract: Generative query suggestion aims to enhance user engagement by anticipating user intents and recommending relevant follow-up queries. A central challeng
arXiv:2609.20776v1 Announce Type: cross Abstract: Action chunking is widely used for action generation and execution in Vision-Language-Action (VLA) policies, yet existing approaches commonly use a fi
arXiv:2609.19631v1 Announce Type: new Abstract: Accurate instance-level and functional understanding of urban buildings in large-scale point clouds is essential for digital city modeling and urban ana
arXiv:2509.03405v2 Announce Type: replace Abstract: Language models (LMs) increasingly drive real-world applications that require world knowledge. However, the internal processes through which models
arXiv:2609.19832v1 Announce Type: new Abstract: Relational table learning has gained increasing attention with the widespread use of relational databases. Existing methods typically rely on deep GNN o
arXiv:2602.01928v3 Announce Type: replace-cross Abstract: Privacy preservation is critical in many high-stakes domains such as medicine and finance, where sensitive data must be analyzed without compr
arXiv:2609.19872v1 Announce Type: new Abstract: 3D assembly is fundamental to modern manufacturing and digital content creation. In this paper, we present PART, a unified transformer-based framework f
arXiv:2609.19463v1 Announce Type: new Abstract: We present ParticleSplat, a self-supervised object-centric learning method that decomposes scenes into a set of latent ''particles'' representing semant
arXiv:2609.19871v1 Announce Type: new Abstract: Time series forecasting has seen signicant advancements with the emergence of new deep learning models. However, forecasting time series in applications
arXiv:2609.19510v1 Announce Type: new Abstract: A fundamental assumption in robotic perception is that the sensor's field of view (FoV) is fixed relative to the robot body. Motion-decoupled sensors, s
arXiv:2609.19460v1 Announce Type: new Abstract: Semantic exploration in confined environments requires both environment mapping and detailed observation of target objects. For ground robots, limited s
arXiv:2511.20844v2 Announce Type: replace-cross Abstract: Training deep networks with noisy labels leads to poor generalization and degraded accuracy due to overfitting to label noise. Existing approa
arXiv:2609.19865v1 Announce Type: new Abstract: Representation learning from medical code sequences in electronic health records and medical claims data has been successful in various clinical applica
arXiv:2609.19243v1 Announce Type: cross Abstract: Spectral co-clustering is a useful tool for discovering latent structure in word-document matrices, but its reliance on singular value decomposition (
arXiv:2609.20589v1 Announce Type: new Abstract: Current dense visual SLAM systems rely almost exclusively on 8-bit tonemapped Low Dynamic Range (LDR) inputs, limiting their robustness in extreme light
arXiv:2609.20677v1 Announce Type: new Abstract: The intersection of open-source processor architectures and machine learning is driving the demand for customizable, efficient, and accessible hardware.
arXiv:2609.20235v1 Announce Type: new Abstract: Indoor mobile robots require open-vocabulary scene understanding that grounds natural-language queries in a consistent 3D map. Many existing systems ult
arXiv:2609.19615v1 Announce Type: cross Abstract: Modern applications generate massive volumes of raw telemetry data, but translating those noisy, heterogeneous event streams into actionable business
arXiv:2601.19406v2 Announce Type: replace-cross Abstract: Real-robot demonstrations are prohibitively expensive, while simulation data and real-world human demonstrations are both scalable but each le
arXiv:2609.20565v1 Announce Type: new Abstract: Recent advancements in large language models have revolutionized the field of psychological counseling, especially in the context of Cognitive Behaviora
arXiv:2609.19153v1 Announce Type: new Abstract: Empirical legal scholarship increasingly treats judicial text as data, and much of it still runs on sparse, interpretable pipelines -- TF-IDF features a
Today we are announcing the new AgentCore runtime, a capability of Amazon Bedrock AgentCore built for the speed, flexibility, and cost efficiency that production agents demand. It reclaims memory as s
arXiv:2609.20449v1 Announce Type: new Abstract: The economic value of inference depends on how capacity and task information are distributed across stages of AI production. We study these organization
arXiv:2609.20414v1 Announce Type: cross Abstract: Tactile signals provide direct contact and force measurements that are essential for understanding physical interactions and enabling dexterous roboti
arXiv:2609.20680v1 Announce Type: cross Abstract: Scalable machine learning in challenging underwater environments is strongly limited by the lack of labeled real-world training data. This data is oft
arXiv:2609.20808v1 Announce Type: new Abstract: Hostile rhetoric toward social groups can normalize exclusion and justify mistreatment, as well as contribute to rising polarization and political viole
arXiv:2609.20114v1 Announce Type: new Abstract: Collecting real-world navigation data for mobile robots typically requires platform-specific teleoperation, making large-scale collection expensive and
arXiv:2609.20582v1 Announce Type: new Abstract: Human manipulation videos provide rich motion and interaction cues for acquiring robot skills without robot demonstrations. Video generation models synt
arXiv:2603.12808v2 Announce Type: replace Abstract: Artificial intelligence in molecular science must move beyond pattern recognition toward chemically valid and interpretable reasoning. We present a
arXiv:2609.18243v1 Announce Type: cross Abstract: Vision-Language-Action models and World-Action Models have advanced language-conditioned robotic manipulation, yet often leave metric relations among
Astra for Law: Frontier intelligence built for your practice. A new offering powered by GPT-6 Astra with tools, settings, and context to support the expertise and judgment of lawyers and legal technol
Astra for Law will initially be offered to selected firms through Trusted Access in ChatGPT and Codex, with API access coming soon. We’ll maintain the legal configuration so builders can focus on thei
arXiv:2609.18473v1 Announce Type: new Abstract: We present CADSplat, a framework that reconstructs photorealistic, geometrically accurate digital twins from sparse (<15 views), wide-baseline posed ima
arXiv:2609.18016v1 Announce Type: new Abstract: World-action models (WAMs) have emerged as a promising paradigm for robot manipulation by jointly modeling future visual dynamics and robot actions. How
arXiv:2505.24427v2 Announce Type: replace Abstract: Accurate modeling of subjective phenomena such as emotion expression requires data annotated with authors' intentions. Commonly such data is collect
arXiv:2609.18385v1 Announce Type: new Abstract: Emotions are an essential aspect of human communication, particularly on social media, where authors frequently combine text and images to convey their
arXiv:2512.07179v2 Announce Type: replace Abstract: Intelligent Tutoring Systems (ITS) provide personalized learning paths by diagnosing learners' proficiency. Knowledge Tracing (KT) models play a cen
arXiv:2603.16130v5 Announce Type: replace Abstract: Overexposure caused by strong daylight and oncoming headlights frequently overwhelms visible sensors, resulting in critical information loss in visu
arXiv:2609.17755v1 Announce Type: cross Abstract: The analysis of US political language is usually based on the written form (e.g. presidential addresses) or posts broadcasted on various social networ
arXiv:2609.18900v1 Announce Type: new Abstract: Prototyping and evaluating human-robot teaming (HRT) scenarios in the real-world is costly. Virtual simulation of HRT scenarios has been adopted as an a
arXiv:2505.15918v3 Announce Type: replace-cross Abstract: In this work, we evaluate the potential of Large Language Models (LLMs) in building Bayesian Networks (BNs) by approximating domain expert pri
arXiv:2609.18455v1 Announce Type: new Abstract: Ropes, cables, and other deformable linear objects appear in tasks from untangling to cable routing and suturing, yet controlling their shape remains a
arXiv:2609.18737v1 Announce Type: new Abstract: Underwater 3D reconstruction supports applications ranging from marine ecosystem monitoring and subsea inspection to underwater archaeology, education,
arXiv:2510.25218v2 Announce Type: replace-cross Abstract: AI is changing work and decision making faster than many institutions can adapt their operating practices. We argue that this adaptation gap m
arXiv:2609.18127v1 Announce Type: new Abstract: Many real-world processes exhibit long-range dependence, where the current state depends on a slowly decaying trace of past states rather than on the mo
arXiv:2609.17888v1 Announce Type: cross Abstract: We study the problem of demonstration selection, which involves selecting a subset of examples for prepending to a query to a language model. This pro
arXiv:2609.18129v1 Announce Type: cross Abstract: Leaf counting is an important task in plant phenotyping for monitoring plant growth and estimating crop yield. Most existing methods rely on RGB image
arXiv:2510.03768v2 Announce Type: replace Abstract: Data-driven planar pushing methods have recently gained attention as they reduce manual engineering effort and improve generalization compared to an
arXiv:2609.18260v1 Announce Type: new Abstract: Recent advances in artificial intelligence and computer vision are reshaping sports performance analysis by enabling automated detection, tracking, and
arXiv:2512.09524v2 Announce Type: replace-cross Abstract: Neural decoding is fundamental to brain-computer interfaces, with growing applications in healthcare. Previous research has focused on leverag
arXiv:2609.18191v1 Announce Type: new Abstract: Agile quadrotor avoidance of fast-moving obstacles requires anticipating collisions and selecting feasible maneuvers within short reaction windows. Reli
arXiv:2609.18599v1 Announce Type: cross Abstract: Monte Carlo Tree Search (MCTS) is a powerful framework for solving complex decision-making problems, yet it often relies on the assumption that the si
arXiv:2602.15882v2 Announce Type: replace-cross Abstract: Robot manipulation uses temporal context to select actions and visual foresight to assess their consequences, yet dense representations of pas
arXiv:2609.17560v1 Announce Type: cross Abstract: Every production model is updated, by retraining, fine-tuning, quantization, or a silent vendor swap, and each update risks being worse than what it r
arXiv:2609.18336v1 Announce Type: new Abstract: Muscle activity is fundamental to human movement, and understanding its patterns is critical for injury prevention and rehabilitation. Conventional musc
arXiv:2609.19096v1 Announce Type: cross Abstract: Artificial intelligence (AI) is increasingly integrated into healthcare systems worldwide, yet its successful clinical adoption depends critically on
arXiv:2609.17995v1 Announce Type: new Abstract: Natural-language datasets support many downstream applications and research studies, but releasing text can reveal sensitive global properties of the un