Search: “models”

arXiv:2605.13989v1 Announce Type: new Abstract: We present VectraYX-Nano, a 41.95M-parameter decoder-only language model trained from scratch in Spanish for cybersecurity, with a Latin-American focus

arXiv:2605.13357v1 Announce Type: cross Abstract: Foundation models have transformed automated code generation, yet autonomous software-engineering agents remain unreliable in realistic development se

arXiv:2605.13801v1 Announce Type: cross Abstract: As generative AI models such as large language models (LLMs) become more pervasive, ensuring the safety, robustness, and overall trustworthiness of th

arXiv:2605.13539v1 Announce Type: new Abstract: Simulative and scenario-based testing are crucial methods in the safety assurance for automated driving systems. To ensure that simulation results are r

arXiv:2605.12928v1 Announce Type: new Abstract: Modern language models have historically relied on two dominant design choices: subword tokenization and autoregressive (AR) ordering. These design deci

arXiv:2511.22663v5 Announce Type: replace Abstract: Unified multimodal models for image generation and understanding represent a significant step toward AGI and have attracted widespread attention fro

arXiv:2605.12483v1 Announce Type: new Abstract: In settings where labeled verifiable training data is the binding constraint, each checked example should be allocated carefully. The standard practice

arXiv:2602.17739v3 Announce Type: replace-cross Abstract: Long-context DNA models are limited by token-mixing cost and by how compression allocates representational budget across the genome. Existing

arXiv:2605.11125v1 Announce Type: new Abstract: Discrete Diffusion Language Models progressed rapidly as an alternative to autoregressive (AR) models, motivated by their parallel generation abilities.

arXiv:2605.11284v1 Announce Type: cross Abstract: Background: External validation is essential for assessing the transportability of predictive models. However, its interpretation is often confounded

arXiv:2605.12135v1 Announce Type: cross Abstract: We present STRUM (Spectral Transcription and Rhythm Understanding Model), an audio-to-chart pipeline that converts raw recordings into playable Clone

arXiv:2602.01977v2 Announce Type: replace Abstract: Knowledge editing methods for large language models are commonly evaluated using predefined benchmarks that assess edited facts together with a limi

arXiv:2605.10564v1 Announce Type: new Abstract: End-to-end autonomous driving systems are increasingly integrating Vision-Language Model (VLM) architectures, incorporating text reasoning or visual rea

arXiv:2512.23964v2 Announce Type: replace-cross Abstract: Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical fl

arXiv:2605.08143v1 Announce Type: cross Abstract: Large language models encode vast factual knowledge that inevitably becomes outdated or incorrect after deployment, yet retraining is costly prohibiti

arXiv:2605.10347v1 Announce Type: new Abstract: Recent advances in vision-language models have enabled mobile GUI agents to perceive visual interfaces and execute user instructions, but reliable predi

arXiv:2605.09256v1 Announce Type: cross Abstract: We introduce a use of the (M)-cover (or (M)-layer) transform for machine learning. The method replicates a model (M) times, but instead of coupling th

arXiv:2605.10574v1 Announce Type: new Abstract: As artificial intelligence advances, models are not improving uniformly. Instead, progress unfolds in a jagged fashion, with capabilities growing uneven

arXiv:2605.09771v1 Announce Type: new Abstract: Despite the central role of sensor-derived measurements such as imaging traits and plasma biomarkers in biomedical research and clinical practice, exist

arXiv:2605.10084v1 Announce Type: cross Abstract: The performance of audio latent diffusion models is primarily governed by generator expressivity and the modelability of the underlying latent space.

arXiv:2504.12334v2 Announce Type: replace Abstract: Large language models (LLMs) face significant challenges in specialized biomedical tasks due to the inherent complexity of medical reasoning and the

arXiv:2605.08311v1 Announce Type: cross Abstract: Model merging provides a compelling paradigm for integrating specialized expertise into a unified multi-task model, a goal that aligns naturally with

arXiv:2605.10176v1 Announce Type: cross Abstract: Natural language interfaces to structured databases are becoming increasingly common, largely due to advances in large language models (LLMs) that ena

arXiv:2602.19612v3 Announce Type: replace Abstract: Machine Unlearning (MU) enables Large Language Models (LLMs) to remove unsafe or outdated information. However, existing work assumes that all facts

arXiv:2605.07399v1 Announce Type: new Abstract: Diffusion Vision-Language Models (dVLMs), built upon the non-causal foundations of Diffusion Large Language Models (dLLMs), have demonstrated remarkable

arXiv:2605.07961v1 Announce Type: new Abstract: Federated fine-tuning (FFT) has emerged as a privacy-preserving paradigm for collaboratively adapting large language models (LLMs). Built upon federated

arXiv:2605.07766v1 Announce Type: new Abstract: Many vision applications require identity consistency beyond strict biometric recognition, especially under non-frontal views or when facial cues are mi

arXiv:2605.07933v1 Announce Type: new Abstract: Latent diffusion models offer an attractive alternative to discrete diffusion for non-autoregressive text generation by operating on continuous text rep

arXiv:2605.07112v1 Announce Type: new Abstract: Agentic AI systems that invoke external tools are powerful but costly, leading developers to default to large models and overspend inference budgets. Mo

arXiv:2605.06723v1 Announce Type: new Abstract: Language models often generate reasoning before giving a final answer, but the visible answer does not reveal when the model's answer preference became

arXiv:2605.04326v1 Announce Type: cross Abstract: Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of co

arXiv:2601.20634v2 Announce Type: replace Abstract: Virtual sensors replace expensive physical sensors in critical applications through machine learning by predicting target signals from available mea

arXiv:2605.04761v1 Announce Type: new Abstract: This paper presents the Personalized Thinking Model (PTM), a hierarchical and interpretable learner representation designed for AI supported education.

arXiv:2605.05031v1 Announce Type: new Abstract: Recent deep learning approaches seek to automate CAD creation by representing a model as a sequence of discrete commands and parameters, and then genera

arXiv:2605.05066v1 Announce Type: new Abstract: We identify and prove a fundamental trade-off governing long-sequence models: no model can simultaneously achieve (i) per-step computation independent o

arXiv:2605.03652v1 Announce Type: new Abstract: Video generation models internalize physical realism as their prior. Anime deliberately violates physics: smears, impact frames, chibi shifts; and its t

arXiv:2605.02592v1 Announce Type: new Abstract: Foundation models, particularly large language models, are increasingly integrated into agent architectures for industrial tasks such as decision suppor

arXiv:2605.03279v1 Announce Type: new Abstract: Automatic modulation classification (AMC) in real-world deployments demands robustness to distribution shifts arising from hardware impairments, unseen

arXiv:2605.00938v1 Announce Type: new Abstract: Accurate modeling of commuting flows is important for urban governance, traffic planning, and resource allocation. However, the combined influence of in

arXiv:2602.07238v2 Announce Type: replace-cross Abstract: Do leading LLM developers possess a proprietary ``secret sauce'', or is LLM performance driven by scaling up compute? Using training and bench

arXiv:2512.04032v3 Announce Type: replace Abstract: We present jina-vlm, a token-efficient 2.4B parameter vision-language model that achieves state-of-the-art multilingual VQA performance among open 2

arXiv:2605.01220v1 Announce Type: new Abstract: Visual Autoregressive Modeling (VAR) based on next-scale prediction achieves strong generation quality, but their explicit deep stacks fix the amount of

arXiv:2605.00080v1 Announce Type: cross Abstract: World models, which are predictive representations of how environments evolve under actions, have become a central component of robot learning. They s

arXiv:2604.27263v1 Announce Type: new Abstract: Subword tokenization is an essential part of modern large language models (LLMs), yet its specific contributions to training efficiency and model perfor

arXiv:2604.27267v1 Announce Type: cross Abstract: As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can

arXiv:2604.28167v1 Announce Type: cross Abstract: In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional param