Search: “models”

arXiv:2607.24562v1 Announce Type: new Abstract: Large language models serve heterogeneous populations structured by domain, topic difficulty, and linguistic style. Conformal risk control (CRC) gives r

arXiv:2607.22428v1 Announce Type: cross Abstract: Explainable AI (XAI) in creative practice can be less about technocentric explanation and more about enabling artists to inspect modify and debug mode

arXiv:2607.21540v1 Announce Type: new Abstract: We present DONDO, a family of open, permissively licensed automatic speech recognition (ASR) base models for African languages, built on the w2v-BERT 2.

arXiv:2510.11686v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) promises to expand the capabilities of language models, but it is unclear if current RL techniques promote the dis

arXiv:2607.08317v1 Announce Type: new Abstract: Modern AI models achieve strong performance on many established benchmarks, yet they still fail on tasks that humans find almost trivial, such as manipu

arXiv:2308.13380v3 Announce Type: replace-cross Abstract: Is it possible to understand the intricacies of a dynamical system not solely from its input/output pattern, but also by observing the behavio

arXiv:2607.04464v1 Announce Type: cross Abstract: World-model evaluation for model-based reinforcement learning typically asks whether the learned model predicts reward and value well, which can leave

arXiv:2607.03949v1 Announce Type: new Abstract: Pixel-wise Earth-observation (EO) foundation models are now achieving state-of-the-art performance via generated spatial embeddings. However, how these

arXiv:2510.14113v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are transforming everyday applications, yet deployment in cybersecurity lags due to a lack of high-quality, domai

arXiv:2602.13792v2 Announce Type: replace Abstract: Artificial intelligence built on large foundation models has transformed language understanding, computer vision, and reasoning, yet these systems r

arXiv:2602.22960v2 Announce Type: replace Abstract: World models based on video generation demonstrate remarkable potential for simulating interactive environments yet suffer from persistent difficult

arXiv:2606.26099v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisat

arXiv:2606.25066v1 Announce Type: cross Abstract: Visual search has been one of the most productive paradigms in the study of visual attention: the way reaction time scales with the number of items di

arXiv:2606.24548v1 Announce Type: new Abstract: Text-to-image (T2I) generation models have achieved remarkable progress in producing visually realistic images from natural language prompts. Yet it rem

arXiv:2606.22085v1 Announce Type: cross Abstract: There are many reasons one may want to edit a model's chain of thought (CoT) -- e.g., to prefill it with reasoning from a stronger model or to remove

arXiv:2603.20520v2 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) with neural networks has accelerated and transformed cognitive modeling workflows. SBI enables modelers to fi

arXiv:2606.21230v1 Announce Type: new Abstract: Wound rotor synchronous motors have emerged as a strong alternative that eliminates dependence on REEs. However, WRSM design requires the simultaneous o

arXiv:2606.17710v2 Announce Type: replace Abstract: Medical vision-language models report strong chest radiograph accuracy, and this is increasingly read as evidence that they use the image. That infe

arXiv:2606.12016v1 Announce Type: cross Abstract: Model post-training, and in particular reinforcement learning (RL), is one of the primary mechanisms by which developers can shape models' values and

arXiv:2601.21898v2 Announce Type: replace Abstract: The rise of model hubs has made it easier to access reusable model components, making model merging a practical tool for combining capabilities. Yet

arXiv:2510.16152v2 Announce Type: replace-cross Abstract: Scientific literature is increasingly fragmented by disciplinary boundaries, specialized terminology, and potentially sparse keyword systems,

arXiv:2606.11605v1 Announce Type: cross Abstract: Predicting process-property relationships in manufacturing is often challenged by high experimental costs and the limited interpretability of complex

arXiv:2411.11350v2 Announce Type: replace Abstract: Deep learning models have shown strong performance in load forecasting, but they generally require large amounts of data for model training before b

arXiv:2606.06094v1 Announce Type: new Abstract: Advances in computational modeling, neuroimaging, and artificial intelligence are revolutionizing the modeling of neurological disorders for improved di

arXiv:2606.04385v1 Announce Type: new Abstract: Foundation models have driven rapid progress in computer vision, yet the two dominant paradigms, vision-language foundation models (VLMs) and vision-onl

arXiv:2602.12430v4 Announce Type: replace-cross Abstract: The transition from monolithic language models to modular, skill-equipped agents marks a defining shift in how large language models (LLMs) ar

arXiv:2606.02800v1 Announce Type: cross Abstract: We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences

arXiv:2606.03002v1 Announce Type: cross Abstract: Quantization is a standard path to deploying large language models, and a quantized model is typically judged acceptable when its perplexity or downst

arXiv:2504.03635v4 Announce Type: replace Abstract: Reasoning is a core capability of language models (LMs), yet it remains unclear how much model capacity is necessary to support reasoning during pre

arXiv:2512.20638v2 Announce Type: replace-cross Abstract: The evaluation of large language models relies heavily on standardized benchmarks. These benchmarks provide useful aggregated metrics, but can

arXiv:2605.31361v1 Announce Type: cross Abstract: In cooperative multi-agent reinforcement learning (MARL), agents must coordinate with partners whose internal policies and intentions are not directly

arXiv:2605.31140v1 Announce Type: cross Abstract: Large Language Models (LLMs) remain highly vulnerable to diverse attacks, particularly in black-box settings where the internals of target models are

arXiv:2605.29283v1 Announce Type: cross Abstract: Recent physics foundation models claim general spatiotemporal forecasting ability, yet their evaluations often collapse performance into a single aver

arXiv:2605.29343v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by pairing a target model with a lightweight draft model whose proposed tokens are verif

arXiv:2605.29744v1 Announce Type: new Abstract: The impressive performance of generalist large language models (LLMs) such as GPT and Claude in healthcare raises a critical question: will domain-speci

arXiv:2605.27975v1 Announce Type: new Abstract: Generative models, including diffusion models, are increasingly used as foundation models and adapted through sequential fine-tuning, making continual l

arXiv:2605.28277v1 Announce Type: new Abstract: Whether large language models (LLMs) construct internal spatial world models from pure-text descriptions remains contested, and whether such capabilitie

arXiv:2605.26283v1 Announce Type: new Abstract: Modern deep learning offers powerful tools for automated retinal screening, but it remains unclear how different visual model families compare in realis

arXiv:2605.27020v1 Announce Type: cross Abstract: The rapid advancement of diffusion-based image generation models has raised serious concerns regarding potential copyright and privacy infringements i

arXiv:2605.24297v1 Announce Type: cross Abstract: Which fine-tuning signals improve patent embedding models, and do gains transfer across patent landscapes? We benchmark 22 embedding models, from 22M-

arXiv:2605.25420v1 Announce Type: cross Abstract: Large language model safety evaluation remains heavily English-centered, leaving low-resource languages under-measured even when models are deployed g

arXiv:2605.23972v1 Announce Type: new Abstract: Large language models achieve strong performance in language generation and knowledge-intensive tasks, yet remain limited in settings requiring causal r

arXiv:2605.23093v1 Announce Type: new Abstract: Topic modeling in applied psychology increasingly spans two methodological traditions: probabilistic bag-of-words models and newer embedding-based appro

arXiv:2605.23238v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed as economic agents in marketplaces, auctions, and bidding settings. Anticipating their behavior i

arXiv:2605.23080v1 Announce Type: new Abstract: Feature attribution methods promise to identify which input features matter for a model output. In generative language models, however, it is often uncl

arXiv:2605.21543v1 Announce Type: new Abstract: Benchmark data contamination has become a central challenge in LLM evaluation: when evaluation examples appear in the training data of one or more audit