Search: “models”

arXiv:2608.13831v1 Announce Type: cross Abstract: Spoken dialogue is a natural form of human--computer interaction, yet most speech language models remain limited to turn-based operation and lack real

arXiv:2608.13613v1 Announce Type: cross Abstract: Recent breakthroughs in generative models have made text-to-voice generation (TTV) possible, enabling the synthesis of speech directly from textual vo

arXiv:2608.13621v1 Announce Type: new Abstract: A hidden Markov model (HMM) combines three roles: inference of a hidden-state belief from observations, propagation through a Markov transition, and emi

arXiv:2608.12388v1 Announce Type: cross Abstract: The emergence of orientation selectivity in the primary visual cortex (V1) remains a central question in computational neuroscience. Shirazi's Bayes-M

arXiv:2608.13409v1 Announce Type: new Abstract: Existing predictive models in learning analytics often treat student academic history as a simple sequence, overlooking the concurrent nature of courses

arXiv:2608.12446v1 Announce Type: cross Abstract: Sleep stage classification is important for the diagnosis and management of sleep disorders, yet most automatic staging studies evaluate models agains

arXiv:2608.11839v1 Announce Type: new Abstract: Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern. To better understand and con

arXiv:2608.11215v1 Announce Type: new Abstract: Simulating societies of many large language model (LLM) agents is expensive, yet the questions asked of such simulations are usually macroscopic: phase

arXiv:2608.11329v1 Announce Type: cross Abstract: A common approach to adding audio to a vision-language model is to train or adapt a large omni-modal system. We show that a lightweight alternative ca

arXiv:2608.10893v1 Announce Type: new Abstract: Certified selective predictors attain whatever coverage they attain; operators impose an automation floor: answer at least a eta-fraction of shifted tar

arXiv:2608.11204v1 Announce Type: cross Abstract: Learning reliable surgical manipulation policies is bottlenecked by the scarcity of action-labeled demonstrations: teleoperated surgical robot (e.g.,

arXiv:2608.09643v1 Announce Type: cross Abstract: AI coding agents now write a growing share of production code, and human security review does not scale at the rate code is generated. The agents in w

arXiv:2608.07693v1 Announce Type: cross Abstract: Generative traffic video forecasting aims to synthesize long-horizon, temporally coherent future videos of traffic scenes from a short observation his

arXiv:2608.08067v1 Announce Type: cross Abstract: Current end-to-end speech dialogue models are primarily optimized for mainstream languages and remain limited in low-resource dialect scenarios due to

arXiv:2608.08244v1 Announce Type: new Abstract: General wearable foundation models are pretrained across broad sensor streams and populations, but are not designed around women's-health tasks. We intr

arXiv:2603.27996v2 Announce Type: replace Abstract: Diffusion models have emerged as a powerful framework for generative tasks in deep learning. They decompose generative modeling into two computation

arXiv:2608.07619v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but often degrade under visual and environmental shifts. Latent worl

arXiv:2510.19366v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) scales model capacity through sparse activation, and is becoming an important architecture for large language models (LLMs)

arXiv:2608.08528v1 Announce Type: new Abstract: Enterprise AI coding assistants incur substantial inference spend, and naive token-cost minimization often fails to reduce end-to-end cost once retries,

arXiv:2608.08139v1 Announce Type: new Abstract: Establishing the provenance of a language model---including its base checkpoint and possible overlap in training distributions---is a governance challen

arXiv:2608.07762v1 Announce Type: new Abstract: LLM benchmarks can build an organization's reputation and attract customers, but only when results are transparent and verifiable. Unverified claims tha

arXiv:2608.06772v1 Announce Type: new Abstract: Accurate estimation of building energy use is essential for achieving carbon neutral and sustainable buildings. To better understand the influence of de

arXiv:2608.07282v1 Announce Type: new Abstract: The recent advances in neural language models have also spurred much work in computational psycholinguistics, asking whether neural LMs are also promisi

arXiv:2608.06424v1 Announce Type: cross Abstract: Speech recordings often contain missing, corrupted, or incorrect regions that must be reconstructed or modified without re-synthesizing the entire utt

arXiv:2608.06975v1 Announce Type: cross Abstract: Long-horizon role-playing demands that characters remain recognizable as they evolve with the narrative. Yet existing work falls short on two fronts:

arXiv:2608.07409v1 Announce Type: new Abstract: Joint-Embedding Predictive Architectures (JEPAs) have emerged as a principled framework for self-supervised learning of world models in compact latent s

arXiv:2608.06332v1 Announce Type: new Abstract: Generalist robot policies exhibit strong capabilities, but their robustness in complex and unseen environments remains limited. Scaling robot learning a

arXiv:2604.17359v2 Announce Type: replace-cross Abstract: Language models asked to simulate psychiatric patients produce cases that survive inspection one at a time and populations that match no real

arXiv:2608.04147v1 Announce Type: cross Abstract: Label noise is common in medical imaging datasets due to factors such as inter-rater variability, annotation errors, and ambiguous cases. This can sev

arXiv:2608.02347v2 Announce Type: replace Abstract: Recurrent linear attention models (RLAs) such as Mamba offer efficient linear-time sequence modeling as an alternative to Transformers, yet their fi

arXiv:2608.02673v1 Announce Type: cross Abstract: Speech editing for content creation requires precise control over both what an edit should do and where it should apply. Free-form natural language pr

arXiv:2608.03701v1 Announce Type: cross Abstract: World-action modeling has emerged as a promising paradigm for robotic control, as it empowers models to go beyond reacting to observations and anticip

arXiv:2608.02980v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have achieved remarkable success on images and short videos, yet scaling them to long videos remains challenging due to f

arXiv:2608.03063v1 Announce Type: new Abstract: Merchant risk control at large payment platforms screens tens of millions of merchants daily, where false positives harm legitimate merchants and false

arXiv:2608.02951v1 Announce Type: cross Abstract: Preference-based reinforcement learning (PbRL) for general stochastic MDPs often requires training a reward model. Existing reward-model-free methods

arXiv:2608.02453v1 Announce Type: new Abstract: Sim-to-real policies are designed under nominal dynamics, but target-system trials may yield only a few isolated one-step transitions. We study pre-exec

arXiv:2608.01426v1 Announce Type: new Abstract: Federated learning (FL) enables distributed optimization and learning across decentralized edge devices while preserving data privacy, but its performan

arXiv:2604.02817v2 Announce Type: replace Abstract: Despite advancements in generating visually stunning content, video diffusion models (VDMs) often yield physically inconsistent results due to pixel

arXiv:2608.01458v1 Announce Type: new Abstract: Large language models are being extensively used to simulate individual user behavior, yet faithfully representing a population requires capturing the s

arXiv:2608.00129v1 Announce Type: new Abstract: Knowledge distillation (KD) is a widely utilized technique for transferring knowledge from a large model (the teacher) to a smaller model (the student).

arXiv:2607.28649v1 Announce Type: cross Abstract: COSI-Lab presents a multimodal, multi-sensor dataset of an interdisciplinary scientific workshop containing 32 academics at an international conferenc

arXiv:2607.28545v1 Announce Type: new Abstract: Large language models can write, patch, and search code, but oncall root cause analysis (RCA) demands something different: reasoning over noisy metrics,

arXiv:2411.10431v3 Announce Type: replace Abstract: Parameter estimation, which represents a classical inverse problem, is often ill-posed as different parameter combinations can yield identical outpu

arXiv:2607.25338v1 Announce Type: new Abstract: Achieving a coherent integration of spectral richness and spatial fidelity remains a central objective in hyperspectral image fusion. However, existing

arXiv:2607.25589v1 Announce Type: cross Abstract: Medical-imaging AI benchmarks combine datasets, DICOM rendering, prompts, provider APIs, automated labels, statistical code, manuscripts, and reposito

arXiv:2607.24810v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance on general remote sensing tasks. However, their capability for rare scenes remains insuff