Search: “models”

arXiv:2511.22475v2 Announce Type: replace-cross Abstract: We present adversarial flow models, a class of generative models that belongs to both the adversarial and flow families. Our method supports n

arXiv:2306.02781v4 Announce Type: replace-cross Abstract: Generative artificial intelligence, and large language models in particular, have emerged as one of the most transformative paradigms in moder

arXiv:2604.07282v1 Announce Type: cross Abstract: Automated face recognition has made rapid strides over the past decade due to the unprecedented rise of deep neural network (DNN) models that can be t

arXiv:2604.06233v1 Announce Type: new Abstract: Safety-trained language models routinely refuse requests for help circumventing rules. But not all rules deserve compliance. When users ask for help eva

arXiv:2505.22976v2 Announce Type: replace-cross Abstract: The ability to simulate the world in a spatially consistent manner is a crucial requirements for effective world models. Such a model enables

arXiv:2608.10260v1 Announce Type: new Abstract: Lens methods interpret large language models (LLMs) by mapping intermediate activations to the output vocabulary, revealing how next-token predictions d

arXiv:2608.08023v1 Announce Type: new Abstract: Building on recent advances in world models, World Action Models (WAMs) jointly model video prediction and action generation. However, they typically re

arXiv:2607.15142v2 Announce Type: replace Abstract: World models are usually evaluated as components of model-based reinforcement learning (MBRL) systems, leaving their standalone reliability understu

arXiv:2608.09703v1 Announce Type: new Abstract: Training a language model suite classically requires training each model separately and serving them independently. We improve both training and inferen

arXiv:2606.12691v2 Announce Type: replace-cross Abstract: Auto-regressive models have emerged as powerful tools for sequential data, from language to video. Understanding how and why these models lear

arXiv:2608.06706v1 Announce Type: cross Abstract: Latent world models plan by predicting future states from an action, but when a scene contains motion the agent does not control, they quietly go acti

arXiv:2608.06526v1 Announce Type: new Abstract: Large language models can infer sensitive personal attributes, such as age, location, and occupation, from ordinary text, turning everyday writing into

arXiv:2608.07107v1 Announce Type: new Abstract: World models are increasingly used to support planning in agents by predicting how environment states evolve in response to agent actions. Yet fluent ne

arXiv:2608.06638v1 Announce Type: cross Abstract: Mechanistic interpretability of music generation has concentrated on audio models, leaving symbolic models largely unexplored. We analyze two public t

arXiv:2608.06792v1 Announce Type: cross Abstract: Foundation model(FM) for recommendation has shown strong ability to model long-horizon sequential user behavior. In practice, a single pretrained foun

arXiv:2401.07039v5 Announce Type: replace-cross Abstract: Mixed quantum states are the native description of many physically important quantum systems, making their generation a fundamental task in qu

arXiv:2509.00559v3 Announce Type: replace Abstract: Humans intuitively navigate social interactions by simulating unspoken dynamics and reasoning about others' perspectives, even with limited informat

arXiv:2608.06770v1 Announce Type: new Abstract: Controllable surgical world models can provide a generative foundation for surgical artificial intelligence and simulation by synthesizing realistic ins

arXiv:2608.03803v1 Announce Type: new Abstract: Multilingual language models are deployed across a hundred or more languages, yet most benchmarks test whether a model can perform a task _in_ a languag

arXiv:2608.00261v1 Announce Type: new Abstract: Do frontier vision-language models present a coherent Theory-of-Mind (ToM) profile across tasks, matching the same human reference group, or does that p

arXiv:2608.00071v1 Announce Type: new Abstract: The rapid emergence of 3D CT foundation models has opened new avenues for predictive modeling from CT imaging, offering a compelling alternative to trad

arXiv:2607.29602v1 Announce Type: cross Abstract: Reading a social situation often depends on behavior, not words alone. We introduce FriendBench, a benchmark for inferring whether two people are alre

arXiv:2607.29613v1 Announce Type: cross Abstract: Reinforcement learning (RL) post-training of Vision-Language-Action (VLA) models has shown strong promise for robotic manipulation. Among RL methods,

arXiv:2607.27857v1 Announce Type: new Abstract: Recent EEG-to-image retrieval models have achieved strong performance in identifying viewed images from semantically diverse candidates. Yet such succes

arXiv:2607.28394v1 Announce Type: new Abstract: Hand-object interaction (HOI) modeling remains challenging because it requires joint reasoning about hand articulation, object geometry, contact, semant

arXiv:2511.21007v2 Announce Type: replace Abstract: Selecting an appropriate pre-trained source model is a critical, yet computationally expensive, task in transfer learning. Model Transferability Est

arXiv:2607.27201v1 Announce Type: new Abstract: World models enable a predictive substrate for planning and action, yet existing formulations merely answer a physical question: what/where it is, and h

arXiv:2607.26326v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) achieve strong performance by integrating visual inputs with the rich priors of pretrained language models. How

arXiv:2607.25537v1 Announce Type: cross Abstract: In the age of foundation models, a model is only as good as its prompt. For this reason, prompt engineering has become an essential technique for impr

arXiv:2607.22694v1 Announce Type: new Abstract: Attention collapse in autoregressive language models -- manifested as repetitive token loops where the model becomes trapped in self-reinforcing attract

arXiv:2607.24371v1 Announce Type: cross Abstract: Healthcare interoperability requires AI systems to produce structured outputs conforming to standardized schemas including ICD-10 for diagnostic codin

arXiv:2607.22555v1 Announce Type: new Abstract: Medical diagnosis is a multi-stage process: extract facts, consult knowledge, generate a differential analysis, and select the best diagnosis with expla

arXiv:2607.20712v1 Announce Type: cross Abstract: Security protocol verification relies on formal tools such as ProVerif and OFMC. This study evaluates whether Large Language Models (LLMs) can perform

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:2607.23804v1 Announce Type: cross Abstract: Context attribution methods for large language models (LLMs) identify which input context contributes to the model response. Recent works show the ini

arXiv:2507.05515v4 Announce Type: replace Abstract: Vision-language models (VLMs) are facing the challenges of understanding and following multimodal assembly instructions, particularly when fine-grai

arXiv:2607.23909v1 Announce Type: new Abstract: Many recent robot policies pursue stronger control by using large pretrained vision-language models (VLMs) as the action backbone. We introduce WorldDiT

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:2607.12775v1 Announce Type: cross Abstract: Scenario-based model predictive control (SBMPC) is a variant of model predictive control (MPC) that explicitly accounts for uncertainty by optimizing

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:2607.06413v1 Announce Type: cross Abstract: Large language model coding agents increasingly perform open-ended data modeling and analysis. These agents are stochastic and adaptive, and therefore

arXiv:2607.06175v1 Announce Type: cross Abstract: Large language models (LLMs) can generate BPMN process models from natural-language descriptions, yet supervised fine-tuning (SFT) limits their output

arXiv:2602.06285v2 Announce Type: replace Abstract: Recent research in geospatial machine learning has demonstrated that models pretrained with self-supervised learning on Earth observation data can p