Search: “models”

arXiv:2609.22216v1 Announce Type: cross Abstract: Quantization enables deployment of large language models on resource-constrained clinical edge devices, but its effect on clinical accuracy and safety

arXiv:2609.19513v1 Announce Type: new Abstract: High-quality pre-training data is a critical bottleneck for educational and STEM-specific language models targeting edge AI and on-device deployment whe

arXiv:2609.16679v1 Announce Type: new Abstract: Foundation models, alongside advances in learned game-world models, are reshaping AI across the game lifecycle. Beyond playing games, recent systems mod

arXiv:2608.00929v2 Announce Type: replace Abstract: Social dynamics encode the process in which individual network and discourse interactions aggregate into collective influence, narrative dominance a

arXiv:2609.13899v1 Announce Type: cross Abstract: The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora,

arXiv:2609.12815v1 Announce Type: cross Abstract: We present BEAST, the first-ever Bayesian Swin Transformer for atmospheric forecasting on 0.25^irc global resolution able to accurately quantify both

arXiv:2609.12278v1 Announce Type: new Abstract: World models let agents plan by predicting the consequences of their actions, but changes in the environment can make them inaccurate. We study the prob

arXiv:2609.11954v1 Announce Type: new Abstract: As deep learning models are increasingly deployed on resource constrained devices, the demand for efficient model optimization techniques continues to g

arXiv:2609.12225v1 Announce Type: cross Abstract: Out-of-Distribution (OOD) data poses a significant threat to machine learning models, often leading to model failure during deployment. All existing O

arXiv:2609.12353v1 Announce Type: new Abstract: Large language models trained recursively on their own or other models' outputs undergo model collapse, in which distributional tails and factual accura

arXiv:2609.11770v1 Announce Type: new Abstract: Large language models are superseded every few quarters; clinical evidence takes years. We asked whether medical research is keeping pace with the syste

arXiv:2609.05899v1 Announce Type: cross Abstract: Aligning large language models with human preferences remains a challenge, primarily due to the critical role of preference data quality in effective

arXiv:2609.05588v1 Announce Type: cross Abstract: World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-free video and action-labeled interaction.

arXiv:2609.06951v1 Announce Type: new Abstract: Activation steering promises modular control of language model behavior: a behavior such as politeness corresponds to a direction in a model's activatio

arXiv:2609.04699v1 Announce Type: new Abstract: Background. Large language models (LLMs) are being adopted in biomedical research at a rapid and accelerating pace, yet commercial services that host ma

arXiv:2609.04011v1 Announce Type: cross Abstract: Reliable transport models are essential when modelling and optimising many chemical engineering processes, yet, most models assume hand-picked constit

arXiv:2609.04166v1 Announce Type: new Abstract: Research and news coverage of language-model deception increasingly attributes human-like mental-state concepts to language models. Such claims can blur

arXiv:2603.18593v2 Announce Type: replace Abstract: We propose a method that represents language models by log-likelihood vectors over prompt-response pairs and constructs model maps for comparing the

arXiv:2609.02292v1 Announce Type: new Abstract: The rapid proliferation of large language models (LLMs) and the growing diversity of their applications presents a unique optimization opportunity: sele

arXiv:2609.00940v1 Announce Type: new Abstract: Solving problems through repeated attempts is a sequential modeling task: at each step, the solver receives feedback and decides how to revise their sol

arXiv:2608.30092v1 Announce Type: cross Abstract: We present Arkios, a 1.04B-parameter dense transformer pretrained from scratch on 150B tokens of bilingual English-Nepali text, using a custom single-

arXiv:2608.29928v1 Announce Type: new Abstract: State-of-the-art monocular body recovery methods predict mesh vertices and angles on the corresponding kinematic tree, but their outputs lack biomechani

arXiv:2608.30114v1 Announce Type: new Abstract: Brazilian Portuguese remains under-served by open language models, and the few that exist are difficult to reproduce and are often compared without meas

arXiv:2608.30530v1 Announce Type: new Abstract: VLM-driven self-improvement of web code has a structural flaw: the model that proposes the repair is the model that judges it, and visual plausibility u

arXiv:2608.27518v1 Announce Type: new Abstract: Continual learning (CL) and model merging (MM) both aim to obtain a single model that performs well across multiple tasks, challenged respectively by ca

arXiv:2608.27033v1 Announce Type: new Abstract: We introduce Riemann-1.0, a fully causal autoregressive World Action Model for embodied intelligence. Riemann-1.0 jointly models multi-view visual obser

arXiv:2608.25927v1 Announce Type: cross Abstract: World models aim to simulate how complex environments evolve under actions and events, yet existing video-based world models primarily learn dynamics

arXiv:2608.24688v1 Announce Type: new Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal obs

arXiv:2608.24486v1 Announce Type: cross Abstract: Purpose: To quantify how evaluation annotations influence measured pulmonary embolism (PE) segmentation performance relative to model training changes

arXiv:2608.19661v1 Announce Type: new Abstract: Large language models can turn a natural-language mission into a sequence of robot actions, but they do not have a sense of physics: they cannot judge h

arXiv:2606.14870v2 Announce Type: replace-cross Abstract: Foundation models (FMs) trained on large datasets and fine-tuned on downstream tasks have emerged as a powerful paradigm in AI for science. In

arXiv:2608.15652v1 Announce Type: new Abstract: The rapid proliferation of generative models raises the model attribution problem: given only an image, can we determine which model produced it? Existi

arXiv:2608.13596v1 Announce Type: cross Abstract: Heterogeneous model fusion seeks to combine models that differ in tasks, initializations, architectures, or scales. We study an underexplored cross-sc

arXiv:2608.14490v1 Announce Type: new Abstract: We present a Test-time World-model Inference (Twin) system, in which a frontier coding agent writes an executable world model for completing continual l

arXiv:2608.13633v1 Announce Type: cross Abstract: Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed o

arXiv:2608.13489v1 Announce Type: new Abstract: We present extbf{DreamX-Phi 1.0}, an action-conditioned video world model for robotic manipulation that, given an observed frame, a language instruction

arXiv:2303.08063v4 Announce Type: replace Abstract: For a considerable time, researchers have focused on developing a method that establishes a deep connection between the generative diffusion model a

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:2608.09703v1 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: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:2608.06792v1 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: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:2608.00261v1 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: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,