Search: “models”

arXiv:2605.07096v1 Announce Type: cross Abstract: Evaluating a new model on an existing benchmark is often necessary to understand its behavior before deployment. For modern evaluation frameworks, gen

arXiv:2605.04082v1 Announce Type: new Abstract: Model-based solutions for nitrous oxide (N2O) emissions from wastewater treatment plants (WWTP) are informed by operational datasets designed to control

arXiv:2605.04759v1 Announce Type: new Abstract: Transformer based pre-trained large language models have become ubiquitous. There is increasing evidence to suggest that even with large scale pre-train

arXiv:2605.03195v1 Announce Type: new Abstract: Modern coding agents increasingly delegate specialized subtasks to subagents, which are smaller, focused agentic loops that handle narrow responsibiliti

arXiv:2605.00964v1 Announce Type: cross Abstract: Much research on LLMs has focused on increasing benchmark performance. However, the evaluation of such models in real-world collaborative human-AI wor

arXiv:2605.00833v1 Announce Type: new Abstract: Agentopic is a novel agent-based workflow for explainable topic modeling that leverages the reasoning capabilities of Large Language Models (LLMs). Exis

arXiv:2605.02641v1 Announce Type: new Abstract: We present Mamoda2.5, a unified AR-Diffusion framework that seamlessly integrates multimodal understanding and generation within a single architecture.

arXiv:2510.15545v4 Announce Type: replace Abstract: Accelerating the inference of large language models (LLMs) has been a critical challenge in generative AI. Speculative decoding (SD) substantially i

arXiv:2605.00644v1 Announce Type: new Abstract: Energy-based models (EBMs) are a flexible class of deep generative models and are well-suited to capture complex dependencies in multimodal data. Howeve

arXiv:2604.28109v1 Announce Type: new Abstract: Model merging has attracted attention as an effective path toward multi-task adaptation by integrating knowledge from multiple task-specific models. Amo

arXiv:2604.24172v1 Announce Type: cross Abstract: This paper uses a minimum divergence framework to introduce a new way of calculating model weights that can be used to average probabilistic predictio

arXiv:2604.24575v1 Announce Type: new Abstract: Diffusion models are primarily trained for image synthesis, yet their denoising trajectories encode rich, spatially aligned visual priors. In this paper

arXiv:2503.18836v2 Announce Type: replace-cross Abstract: Magnetic resonance imaging (MRI) is a vital diagnostic tool, but its inherently long acquisition times reduce clinical efficiency and patient

arXiv:2604.24506v1 Announce Type: new Abstract: Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models

arXiv:2604.24218v1 Announce Type: cross Abstract: As the complexity of System-on-Chip (SoC) designs grows, the shift-left paradigm necessitates the rapid development of high-fidelity reference models

arXiv:2604.23767v1 Announce Type: new Abstract: Deploying machine learning models across diverse well portfolios requires generalisation to wells with design parameters outside the training distributi

arXiv:2604.22260v1 Announce Type: cross Abstract: Urban transportation systems face growing safety challenges that require scalable intelligence for emerging smart mobility infrastructures. While rece

arXiv:2503.04492v3 Announce Type: replace-cross Abstract: In the rapidly advancing field of materials informatics, nonlinear machine learning models have demonstrated exceptional predictive capabiliti

arXiv:2410.16006v3 Announce Type: replace Abstract: A common challenge towards the adaptability of Large Language Models (LLMs) is their ability to learn new languages over time without hampering the

arXiv:2604.19789v1 Announce Type: new Abstract: We present an autonomous large language model (LLM) agent for end-to-end, data-driven materials theory development. The model can choose an equation for

arXiv:2602.09520v2 Announce Type: replace Abstract: The Rashomon set captures the collection of models that achieve near-identical empirical performance yet may differ substantially in their decision

arXiv:2603.29025v2 Announce Type: replace-cross Abstract: Large language models systematically fail when a salient surface cue conflicts with an unstated feasibility constraint. We study this through

arXiv:2511.11793v3 Announce Type: replace Abstract: We present MiroThinker v1.0, an open-source research agent designed to advance tool-augmented reasoning and information-seeking capabilities. Unlike

arXiv:2604.19403v1 Announce Type: new Abstract: Accurate cardiac anatomy modeling requires the model to be able to handle intricate interrelations among structures. In this paper, we propose VecHeart,

arXiv:2601.14590v2 Announce Type: replace Abstract: Counterfactual explanations (CFEs) provide human-centric interpretability by identifying the minimal, actionable changes required to alter a machine

arXiv:2509.18611v2 Announce Type: replace Abstract: Pretraining on large-scale collections of PDE-governed spatiotemporal trajectories has recently shown promise for building generalizable models of d

arXiv:2601.03938v2 Announce Type: replace-cross Abstract: Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. Memo

arXiv:2604.17736v1 Announce Type: new Abstract: As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive gener

arXiv:2601.18731v2 Announce Type: replace Abstract: Alignment of Large Language Models (LLMs) aims to align outputs with human preferences, and personalized alignment further adapts models to individu

arXiv:2604.16517v1 Announce Type: cross Abstract: Large vision-language models (VLMs) achieve strong performance on multimodal tasks but often suffer from hallucination and poor grounding in knowledge

arXiv:2604.17104v1 Announce Type: cross Abstract: Modern AI models are growing rapidly in size and redundancy, leading to significant storage and distribution challenges in model hubs. We present Tens

arXiv:2503.18970v3 Announce Type: replace Abstract: Structured State Space Models (SSMs) have emerged as a transformative paradigm in sequence modeling, addressing critical limitations of Recurrent Ne

arXiv:2604.16275v1 Announce Type: new Abstract: This paper explores the response of Large Language Models (LLMs) to user prompts with different degrees of politeness and impoliteness. The Politeness T

arXiv:2601.11374v2 Announce Type: replace Abstract: Scientific writing is an expert-domain task that demands deep domain knowledge, task-specific requirements and reasoning capabilities that leverage

arXiv:2604.14661v1 Announce Type: cross Abstract: Edge AI model deployment is a multi-stage engineering process involving model conversion, operator compatibility handling, quantization calibration, r

arXiv:2502.12222v2 Announce Type: replace Abstract: The eXplainable Artificial Intelligence (XAI) research predominantly concentrates to provide explainations about AI model decisions, especially Deep

arXiv:2604.00313v2 Announce Type: replace Abstract: Automated species classification from underwater imagery is bottlenecked by the cost of expert annotation, and supervised models trained on one data

arXiv:2604.15009v1 Announce Type: cross Abstract: Flow matching retains the generation quality of diffusion models while enabling substantially faster inference, making it a compelling paradigm for ge

arXiv:2510.06180v2 Announce Type: replace-cross Abstract: In climate science, the tuning of climate models is a computationally intensive problem due to the combination of the high-dimensionality of t

arXiv:2507.01041v4 Announce Type: replace-cross Abstract: Split learning (SL) is a distributed learning paradigm that can enable computation-intensive artificial intelligence (AI) applications by part

arXiv:2604.12935v1 Announce Type: new Abstract: Efficiently merging several models fine-tuned for different tasks, but stemming from the same pretrained base model, is of great practical interest. Des

arXiv:2604.10884v1 Announce Type: cross Abstract: Automated generation of executable Business Process Model and Notation (BPMN) models from natural-language specifications is increasingly enabled by l

arXiv:2512.24503v2 Announce Type: replace-cross Abstract: Data teams at frontier AI companies routinely train small proxy models to make critical decisions about pretraining data recipes for full-scal

arXiv:2405.16772v2 Announce Type: replace-cross Abstract: Social recommendation models weave social interactions into their design to provide uniquely personalized recommendation results for users. Ho

arXiv:2604.09101v1 Announce Type: cross Abstract: Organisations with limited data and computational resources increasingly outsource model training to Machine Learning as a Service (MLaaS) providers,

arXiv:2503.00035v2 Announce Type: replace-cross Abstract: Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining th