Search: “models”

arXiv:2605.05615v2 Announce Type: replace Abstract: Large language models (LLMs) impose rapidly growing energy demands, creating an emerging energy and carbon crisis driven by large-scale inference. S

arXiv:2605.03871v1 Announce Type: new Abstract: Language models encode substantial evaluative knowledge from pretraining, yet current post-training methods rely on external supervision (human annotati

arXiv:2605.04831v1 Announce Type: new Abstract: Story generation aims to automatically produce coherent, structured, and engaging narratives. Although large language models (LLMs) have significantly a

arXiv:2407.13278v3 Announce Type: replace Abstract: Time series, characterized by a sequence of data points organized in a discrete-time order, are ubiquitous in real-world scenarios. Unlike other dat

arXiv:2506.13687v2 Announce Type: replace-cross Abstract: Probabilistic forecasts are typically obtained using state-of-the-art statistical and machine learning models, with model parameters estimated

arXiv:2605.00907v1 Announce Type: new Abstract: Large language models (LLMs) and multimodal large models (MLLMs) are increasingly used for transportation tasks such as regulation question answering, t

arXiv:2604.27311v1 Announce Type: cross Abstract: The advent of Large Language Models (LLMs) has significantly transformed tasks across Software Engineering. In the context of Business Process Managem

arXiv:2510.08049v3 Announce Type: replace-cross Abstract: Although Large Language Models (LLMs) exhibit advanced reasoning ability, conventional alignment remains largely dominated by outcome reward m

arXiv:2604.26160v1 Announce Type: cross Abstract: Nonlinear Mixed Effects models (NLME) models are widely used in pharmacometrics and related fields to analyze hierarchical and longitudinal data. Howe

arXiv:2604.26130v1 Announce Type: cross Abstract: Every RLHF-trained language model is shaped by a reward model, yet the mechanistic interpretability toolkit -- logit lens, direct logit attribution, a

arXiv:2603.12118v2 Announce Type: replace Abstract: Any-to-Any models are an emerging class of multimodal models that accept combinations of multimodal data (e.g., text, image, video, audio) as input

arXiv:2604.24679v1 Announce Type: new Abstract: Pathology foundation models (PFMs) have recently emerged as powerful pretrained encoders for computational pathology, enabling transfer learning across

arXiv:2603.20711v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are mainstream in embodied intelligence but face high inference costs. Edge-Cloud Collaborative (ECC) depl

arXiv:2604.19966v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in settings where sensitivity to low-level image degradations matters, including content moderatio

arXiv:2512.15808v2 Announce Type: replace-cross Abstract: Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse t

arXiv:2604.20354v1 Announce Type: new Abstract: Text-to-Image generation has seen significant advancements in output realism with the advent of diffusion models. However, diffusion models encounter di

arXiv:2502.02779v3 Announce Type: replace-cross Abstract: Head computed tomography (CT) imaging is a widely-used imaging modality with multitudes of medical indications, particularly in assessing path

arXiv:2604.18775v1 Announce Type: new Abstract: Detecting jailbreak behaviour in large language models remains challenging, particularly when strongly aligned models produce harmful outputs only rarel

arXiv:2604.19394v1 Announce Type: new Abstract: This paper narrows the performance gap between small, specialized models and significantly larger general-purpose models through domain adaptation via c

arXiv:2510.04686v2 Announce Type: replace-cross Abstract: Model merging combines independent solutions with different capabilities into a single one while maintaining the same inference cost. Two popu

arXiv:2604.19642v1 Announce Type: new Abstract: Edge devices such as smartwatches and smart glasses cannot continuously run even the smallest 100M-1B parameter language models due to power and compute

arXiv:2604.18827v1 Announce Type: cross Abstract: Scaling data and artificial neural networks has transformed AI, driving breakthroughs in language and vision. Whether similar principles apply to mode

arXiv:2506.12606v2 Announce Type: replace Abstract: While Mamba has demonstrated strong performance in language modeling, its potential as a speech self-supervised learning (SSL) model remains underex

arXiv:2512.02636v3 Announce Type: replace-cross Abstract: Log-likelihood evaluation enables important capabilities in generative models, including model comparison, certain fine-tuning objectives, and

arXiv:2601.03331v2 Announce Type: replace Abstract: Recent advances in Vision-Language Models (VLMs) have improved performance in multi-modal learning, raising the question of whether these models tru

arXiv:2601.21257v2 Announce Type: replace Abstract: Advancing beyond single monolithic language models (LMs), recent research increasingly recognizes the importance of model collaboration, where multi

arXiv:2604.18327v1 Announce Type: cross Abstract: Reward models (RMs) are central to aligning large language models (LLMs) with human preferences, powering RLHF and advanced decoding strategies. While

arXiv:2604.15395v1 Announce Type: new Abstract: Over the recent years, the field of robotics has been undergoing a transformative paradigm shift from fixed, single-task, domain-specific solutions towa

arXiv:2510.09065v2 Announce Type: replace-cross Abstract: We introduce MMAudioSep, a generative model for video/text-queried sound separation that is founded on a pretrained video-to-audio model. By l

arXiv:2509.01728v4 Announce Type: replace-cross Abstract: Recent advances in the development of robotic foundation models have led to promising end-to-end and general-purpose capabilities in robotic s

arXiv:2604.14769v1 Announce Type: new Abstract: The pre-training and fine-tuning paradigm has become the dominant approach for model adaptation. However, conventional pre-training typically yields mod

arXiv:2508.20705v3 Announce Type: replace Abstract: Recent advances in self-supervised learning for EEG representation have largely relied on masked reconstruction, where models are trained to recover

arXiv:2604.14001v1 Announce Type: new Abstract: Diffusion language models have recently emerged as a leading alternative to standard language models, due to their ability for bidirectional attention a

arXiv:2603.26259v2 Announce Type: replace-cross Abstract: While Late Interaction models exhibit strong retrieval performance, many of their underlying dynamics remain understudied, potentially hiding

arXiv:2509.15406v2 Announce Type: replace Abstract: AI generative models leave implicit traces in their generated images, which are commonly referred to as model fingerprints and are exploited for sou

arXiv:2509.26404v2 Announce Type: replace-cross Abstract: Fingerprinting Large Language Models (LLMs)is essential for provenance verification and model attribution. Existing fingerprinting methods are

arXiv:2604.10905v1 Announce Type: cross Abstract: We present Audio Flamingo Next (AF-Next), the next-generation and most capable large audio-language model in the Audio Flamingo series, designed to ad

arXiv:2508.08574v3 Announce Type: replace Abstract: We introduce DeepFleet, a suite of foundation models designed to support coordination and planning for large-scale mobile robot fleets. These models

arXiv:2604.09609v1 Announce Type: new Abstract: Human behavior models are essential as behavior references and for simulating human agents in virtual safety assessment of automated vehicles (AVs), yet

arXiv:2604.10337v1 Announce Type: new Abstract: Employee attrition presents a major challenge for organizations, increasing costs and reducing productivity. Predicting attrition accurately enables pro

arXiv:2604.11035v1 Announce Type: new Abstract: Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of intr

arXiv:2604.09791v1 Announce Type: new Abstract: Small language models are attractive for production deployment due to their low cost, fast inference, and ease of specialization. However, adapting them

arXiv:2509.20868v2 Announce Type: replace-cross Abstract: Structured reasoning can improve the inference performance of large language models (LLMs), but it also introduces computational cost and cont

arXiv:2604.10720v1 Announce Type: new Abstract: Artificial models that simulate how learners act and respond within educational systems are a promising tool for evaluating tutoring strategies and feed

arXiv:2604.09244v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as the mainstream of embodied intelligence. Recent VLA models have expanded their input modalities fr

arXiv:2604.09378v1 Announce Type: cross Abstract: Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their exec