Search: “models”

arXiv:2602.13697v2 Announce Type: replace Abstract: Relational databases (RDBs) contain vast amounts of heterogeneous tabular information that can be exploited for predictive modeling purposes. But si

arXiv:2606.06388v1 Announce Type: cross Abstract: Recent advances in LLM agents have enabled complex cognitive capabilities, such as multi-step reasoning, planning, and tool use, that increasingly pos

arXiv:2509.23385v5 Announce Type: replace-cross Abstract: Simulation-based inference (SBI) is transforming experimental sciences by enabling parameter estimation in complex non-linear models from simu

arXiv:2606.03609v1 Announce Type: cross Abstract: Embodied agents that navigate cities rely on world models that predict how their surroundings will change as they move. But for navigation, what matte

arXiv:2509.19305v2 Announce Type: replace-cross Abstract: Diffusion probability models have shown significant promise in offline reinforcement learning by directly modeling trajectory sequences. Howev

arXiv:2012.02110v2 Announce Type: replace Abstract: Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimize

arXiv:2602.11852v2 Announce Type: replace Abstract: While state-of-the-art language models (LMs) surpass most humans in certain domains, their reasoning remains largely opaque, reducing trust and incr

arXiv:2605.30407v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated strong performance on general tasks, while often struggling to adapt to specialized domains without hig

arXiv:2602.00942v3 Announce Type: replace Abstract: Modern large language models are increasingly deployed under compute and memory constraints, making flexible control of model capacity a central cha

arXiv:2605.30381v1 Announce Type: cross Abstract: Deceptive alignment, in which models maintain accurate internal representations while deliberately producing false outputs, remains a central challeng

arXiv:2605.29121v1 Announce Type: cross Abstract: We propose a minimal dynamical model of adaptive softmax routing for a two-expert Mixture-of-Experts (MoE) layer. The model is obtained as a mean-fiel

arXiv:2605.29524v1 Announce Type: cross Abstract: Relay and reseller APIs increasingly intermediate access to large language models (LLMs), but users have no direct way to verify that a claimed endpoi

arXiv:2603.23234v2 Announce Type: replace Abstract: LLM agents increasingly rely on memory mechanisms to reuse knowledge from past problem-solving experiences. However, existing methods typically cons

arXiv:2605.29310v1 Announce Type: new Abstract: Stepwise model routing improves the efficiency of Large Reasoning Models (LRMs) by assigning each reasoning step to a suitable model. Recent methods for

arXiv:2605.29691v1 Announce Type: new Abstract: Self-supervised learning (SSL) has produced a diverse landscape of vision transformers (ViTs) whose pretrained representations support a wide range of d

arXiv:2605.22166v2 Announce Type: replace Abstract: LLM agents are shaped not only by their language models, but also by the runtime harness that mediates observation, tool use, action execution, feed

arXiv:2508.21046v3 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models built on pre-trained Vision-Language Models (VLMs) require extensive post-training, resulting in high com

arXiv:2506.22726v4 Announce Type: replace Abstract: Deep learning for human sensing on edge systems presents significant potential for smart applications. However, its training and development are hin

arXiv:2512.11280v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved remarkable performance across a wide range of tasks, but their increasing parameter sizes significantly s

arXiv:2602.10450v2 Announce Type: replace-cross Abstract: Optimization modeling underpins decision-making in logistics, manufacturing, energy, and finance, yet translating natural-language requirement

arXiv:2605.26191v1 Announce Type: cross Abstract: This research addresses the problem of adaptive modeling in time-series data streams with clear input-output relationships. This problem is challengin

arXiv:2605.26320v1 Announce Type: cross Abstract: The application of generalist multimodal models (GMMs) to specialized scientific domains remains limited due to the scarcity of comprehensive domain-s

arXiv:2605.26322v1 Announce Type: new Abstract: Theory of Mind (ToM), the ability to infer others' knowledge, intentions, and emotions, is commonly evaluated in large language models (LLMs) using end-

arXiv:2605.25393v1 Announce Type: new Abstract: Large Language Models (LLMs) and Multimodal LLMs (MLLMs) have demonstrated immense potential in autonomous driving (AD) by offering human-like reasoning

arXiv:2605.24981v1 Announce Type: new Abstract: Choosing a Large Language Model (LLM) for a given task requires comparing many strong candidates, yet standard evaluation relies on costly annotations o

arXiv:2509.07961v2 Announce Type: replace Abstract: We develop new experimental paradigms for measuring welfare in language models. We compare verbal reports of models about their preferences with pre

arXiv:2605.24667v1 Announce Type: new Abstract: Mean cross-entropy is the standard validation metric for language models, but it can fail to track model quality during training. We examine this in two

arXiv:2605.23052v1 Announce Type: cross Abstract: We present DreamerNLplus, a hybrid framework for modeling mental health dynamics from social media timelines in the CLPsych 2026 shared task. Our syst

arXiv:2605.23712v1 Announce Type: cross Abstract: Reconstructing flow fields from sparse measurements is a fundamental problem in fluid mechanics with broad implications for modeling, control, and des

arXiv:2605.23019v1 Announce Type: new Abstract: Deploying language-model agents in production often requires substantial compute and human effort to tune prompts, parsers, validators, and other compon

arXiv:2605.22080v1 Announce Type: new Abstract: We introduce JMed48k, a multi-profession Japanese healthcare licensing benchmark for evaluating vision-language models. Built from official PDF material

arXiv:2605.21167v1 Announce Type: cross Abstract: An accurate assessment of a model's complexity is crucial for topics such as interpretation, generalization, and model selection. However, most existi

arXiv:2605.21295v1 Announce Type: new Abstract: Longitudinal passive sensing enables continuous health prediction, yet models often fail under cross-dataset distribution shifts. Traditional ML overfit

arXiv:2510.14261v2 Announce Type: replace Abstract: We present an experimental recipe for studying the relationship between training data and language model (LM) behavior. We outline steps for interve

arXiv:2605.19957v1 Announce Type: cross Abstract: World models are widely explored in embodied intelligence, yet they typically predict distinct evolutions of the world and the ego within a single str

arXiv:2605.16522v1 Announce Type: new Abstract: Collective behavior in animals has long been modeled through self-propelled particle models, which reproduce striking group-level phenomena through abst

arXiv:2605.16360v1 Announce Type: cross Abstract: Efficient long-context inference in Large Language Models (LLMs) is severely constrained by the Key-Value (KV) cache memory wall, yet existing pruning

arXiv:2601.22664v4 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) is a pivotal technique for aligning large language models (LLMs) with human preferences, yet it is

arXiv:2605.16651v1 Announce Type: new Abstract: Explanation mechanisms are increasingly used to support transparency and trust in vision-language models (VLMs), particularly in settings where model de

arXiv:2605.14120v1 Announce Type: cross Abstract: Geospatial foundation models compress multispectral observations into dense embeddings increasingly used in natural-language environmental reasoning s

arXiv:2605.14038v1 Announce Type: new Abstract: Large language models (LLMs) increasingly act as autonomous agents that must decide when to answer directly vs. when to invoke external tools. Prior wor

arXiv:2601.19924v2 Announce Type: replace-cross Abstract: We investigate the capabilities and scalability of Large Language Models (LLMs) in optimization modeling, a domain requiring structured reason

arXiv:2605.14164v1 Announce Type: new Abstract: The primary way to establish and compare competencies in foundation and generative AI models has shifted from peer-reviewed literature to press releases

arXiv:2510.18245v3 Announce Type: replace-cross Abstract: Scaling the number of parameters and the size of training data has proven to be an effective strategy for improving large language model (LLM)

arXiv:2510.03206v2 Announce Type: replace-cross Abstract: Diffusion language models, especially masked discrete diffusion models, have achieved great success recently. While there are some theoretical

arXiv:2605.11836v1 Announce Type: cross Abstract: Lifelong Model Editing aims to continuously update evolving facts in Large Language Models while preserving unrelated knowledge and general capabiliti