Search: “model-releases”

arXiv:2607.04072v1 Announce Type: cross Abstract: Large language models can generate plausible quantum code, but it is unclear whether they can reliably target the specific software development kit (S

arXiv:2607.03015v1 Announce Type: new Abstract: Forecasting future events has attracted growing attention as a testbed for general-purpose AI. A natural way to ground this evaluation is let the models

arXiv:2607.05019v1 Announce Type: cross Abstract: In multimodal classification, late-fusion approaches classify concatenated modality-specific features extracted by unimodal neural networks. When moda

arXiv:2607.04071v1 Announce Type: cross Abstract: Portuguese remains underrepresented in text embedding evaluation, despite being one of the most widely spoken languages in the world. As a result, emb

arXiv:2607.04149v1 Announce Type: new Abstract: In safety-critical traffic scenarios, answering complex questions relies on minute, localized visual cues. However, standard Multimodal Large Language M

arXiv:2604.00392v2 Announce Type: replace-cross Abstract: Agents that synthesize their own tools ship a second artifact alongside each answer: a software library that future tasks reuse, compose, and

arXiv:2607.04514v1 Announce Type: cross Abstract: We study the small-noise asymptotics of Euclidean heat regularizations of probability measures supported on manifolds with corners. Near a boundary or

arXiv:2607.03365v1 Announce Type: cross Abstract: Vision-language models (VLMs) increasingly read news and web content as images, where the publisher's identity is visually present. We show that VLMs

arXiv:2607.04598v1 Announce Type: cross Abstract: Expressive parameterized quantum circuits (PQCs) are often designed under a dilemma: the growth of expressibility and entangling power (EP) that impro

arXiv:2607.04293v1 Announce Type: cross Abstract: Building AI Scientist agents with Large Language Models (LLMs) has recently attracted growing attention. Since scientific discovery fundamentally reli

arXiv:2506.07468v4 Announce Type: replace-cross Abstract: Conventional large language model (LLM) safety alignment relies on a reactive, disjoint loop: attackers exploit a static model, then defenders

arXiv:2607.05290v1 Announce Type: new Abstract: Large Language Models (LLMs) can produce detailed answers to complex queries, but these answers are typically presented as dense linear text, which make

arXiv:2604.03553v2 Announce Type: replace Abstract: AI is increasingly supporting, accelerating, and automating scientific discovery across subjects. Yet, the adoption of AI in historical research rem

arXiv:2607.03803v1 Announce Type: cross Abstract: The growing demand for image-to-video creation on mobile devices has increasingly focused on cinematic motion effects like bullet time, dolly zoom, sl

arXiv:2607.02930v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel in diverse vision tasks, but full-parameter retraining is computationally expensive as real-world knowled

arXiv:2607.02638v1 Announce Type: new Abstract: Neuroimaging research requires manipulating heterogeneous data structures, including raw MRI volumes, volumetric parcellations, cortical surface meshes,

arXiv:2607.05185v1 Announce Type: new Abstract: Compositional generalization, the ability to understand and produce novel combinations of known components, remains a fundamental challenge for modern a

arXiv:2601.15141v2 Announce Type: replace Abstract: Agentic Reinforcement Learning (RL) has empowered Large Language Models (LLMs) to utilize tools like Python interpreters for complex problem-solving

arXiv:2607.03650v1 Announce Type: cross Abstract: Extracting textual information from scanned medical documents, such as external laboratory reports and manually filled forms, has been a major challen

arXiv:2501.10711v5 Announce Type: replace-cross Abstract: Code-related benchmarks play a critical role in evaluating large language models (LLMs), yet their quality fundamentally shapes how the commun

نطلق اليوم نموذج @cohere Transcribe العربي، أفضل نموذج مفتوح المصدر لتحويل الكلام العربي من الصوت إلى نص، مع مراعاة اللهجات العربية المختلفة. يتصدر معيار Open Universal Arabic ASR Leaderboard. صُمم ال

arXiv:2607.02034v2 Announce Type: replace Abstract: Physics-based Human-Scene Interaction (HSI) imitation learning is crucial for embodied intelligence as it bridges the gap between kinematic 3D motio

arXiv:2607.03783v1 Announce Type: new Abstract: Cross-subject generalization remains a fundamental challenge in surface electromyography (sEMG)-based gesture recognition. Although deep learning method

arXiv:2607.02681v1 Announce Type: cross Abstract: Integrating information across related tasks can improve estimation and prediction in transfer, multi-task, and federated learning, but contamination

arXiv:2607.04755v1 Announce Type: new Abstract: Model merging offers a practical alternative to conventional continual learning by integrating independently fine-tuned models without retaining previou

arXiv:2607.02884v1 Announce Type: new Abstract: We address sampling-based motion planning for continuous-time stochastic systems under process and measurement uncertainty, with probabilistic guarantee

arXiv:2607.03851v1 Announce Type: new Abstract: A unified multiplex virtual staining model enables scalable and non-destructive multiplex analysis from H&E slides while promoting parameter efficiency,

arXiv:2607.02576v1 Announce Type: cross Abstract: Parameter-Efficient Fine-Tuning (PEFT) commonly adapts pretrained weights through low-rank updates, and recent methods further exploit the singular va

arXiv:2607.05177v1 Announce Type: new Abstract: Workforce scheduling is an NP-hard combinatorial optimization problem requiring simultaneous satisfaction of labor regulations, coverage requirements, e

arXiv:2607.02575v1 Announce Type: cross Abstract: Vision-language models can perform new tasks without parameter updates through in-context learning (ICL), whose core mechanism is utilizing the suppor

arXiv:2607.04179v1 Announce Type: cross Abstract: End-to-end Vision-Language Models (VLMs) show immense potential in autonomous driving. However, standard Supervised Fine-Tuning (SFT) often suffers fr

arXiv:2607.03339v1 Announce Type: new Abstract: Learning dissipative dynamics from discrete observations is essential for reliable long-horizon prediction and physically meaningful parameter identific

arXiv:2603.01878v2 Announce Type: replace Abstract: Recent advances in generative AI have made synthetic Computed Tomography (CT) images increasingly realistic, enabling promising applications in medi

arXiv:2607.03145v1 Announce Type: cross Abstract: The informativeness of a training set is as consequential as its size, yet most sampling strategies remain agnostic to the intrinsic geometry of the d

arXiv:2506.11389v4 Announce Type: replace Abstract: As the cost of pretraining large language models grows, there is continued interest in strategies to improve learning efficiency during this core tr

CyberProof Inc., a co-managed security services company owned by UST Global Inc., today launched an agentic artificial intelligence service that it says can autonomously handle up to two-thirds of sec

arXiv:2607.04727v1 Announce Type: cross Abstract: Automatic data visualization generation has advanced rapidly with multi-modal large language models, yet existing efforts largely focus on static char

arXiv:2607.04650v1 Announce Type: cross Abstract: Probabilistic inference in high-dimensional Bayesian networks is difficult because exact manipulation of the joint distribution scales exponentially w

arXiv:2607.03764v1 Announce Type: cross Abstract: The geometric size and regularity of detonation cells are key physical parameters for characterizing detonation waves. Traditional manual measurement

arXiv:2607.05390v1 Announce Type: cross Abstract: Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a part

arXiv:2602.07488v3 Announce Type: replace-cross Abstract: Despite the fact that experimental neural scaling laws have substantially guided empirical progress in large-scale machine learning, no existi

arXiv:2607.02873v1 Announce Type: cross Abstract: Large language model agents driving security tool suites over the Model Context Protocol are increasingly common. Yet the factors that bound their cap

arXiv:2607.04108v1 Announce Type: new Abstract: Large language models are increasingly used as evolutionary engines for scientific discovery: generate candidates, select winners, feed them back as par

arXiv:2607.03574v1 Announce Type: cross Abstract: AI systems increasingly propose executable scientific models whose value depends on both their symbolic structure and their fitted continuous paramete

arXiv:2607.03671v1 Announce Type: cross Abstract: Models of complex systems often have many parameters, yet are constrained by far fewer experimentally accessible observables: similar activity can eme

arXiv:2508.01651v2 Announce Type: replace Abstract: 3D affordance grounding aims to understand how diverse objects can be manipulated, making it a cornerstone of embodied interaction. However, prior w