Search: “models”

arXiv:2605.15916v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as an critical technique for adapting large-scale foundation models across natural language process

arXiv:2605.15383v1 Announce Type: new Abstract: Microscopy images contain rich information about how cells respond to perturbations, making them essential to applications like drug screening. To quant

arXiv:2605.15470v1 Announce Type: new Abstract: Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilis

arXiv:2605.15963v1 Announce Type: new Abstract: Large vision-language models have significantly advanced GUI agents, enabling executable interaction across web, mobile, and desktop interfaces. Yet the

arXiv:2509.20349v3 Announce Type: replace Abstract: Accurate time-series forecasting for complex physical systems is the backbone of modern industrial monitoring and control, yet deep learning models

arXiv:2605.15370v1 Announce Type: cross Abstract: Accurate salt-body delineation is essential for seismic interpretation because salt structures distort wave propagation, complicate velocity-model bui

arXiv:2605.15777v1 Announce Type: new Abstract: Computer-Using Agents (CUAs) are rapidly extending large language models (LLMs) beyond text-based reasoning toward action execution in more complex envi

arXiv:2605.15307v1 Announce Type: cross Abstract: Motion-centric video editing remains difficult for large generative video models, which often respond well to appearance changes but struggle to produ

arXiv:2601.19923v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) evolve into the core of Web-based autonomous agents and complex Web Information Systems, their ability to fait

arXiv:2602.21141v2 Announce Type: replace Abstract: Object perception is fundamental for tasks such as robotic material handling and quality inspection. However, modern supervised deep-learning models

arXiv:2509.21465v3 Announce Type: replace Abstract: Tabular foundation models are becoming increasingly popular for low-resource tabular problems. These models make up for small training datasets by p

arXiv:2503.07518v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) rely on the Key-Value (KV) Cache to store token history, enabling efficient decoding of tokens. As the KV-Cache g

Every device, user, and microservice generates data. Ingesting this data, extracting meaning and insights, and driving business decisions in real time has the potential to deliver transformational bus

G4-MeroMero-31B-uncensored-heretic is a fine-tuned variant of Gemma 4 31B optimized for creative tasks, featuring low KL divergence (0.0100) and minimal refusals (15/100). The model is designed to be

Gemini Omni is a multimodal AI model that allows users to create content from any input and edit naturally using conversational language . Users can combine images, audio, video, and text as input to

This article discusses recent advancements in large language model architecture design, focusing on key-value (KV) sharing techniques, multi-head cache (mHC) mechanisms, and compressed attention metho

arXiv:2605.14948v1 Announce Type: new Abstract: State-of-the-art diffusion models often rely on parameter-efficient fine-tuning to perform specialized image editing tasks. However, real-world applicat

arXiv:2602.11626v2 Announce Type: replace-cross Abstract: Learning solution operators for systems with complex, varying geometries and parametric physical settings is a central challenge in scientific

arXiv:2605.14073v1 Announce Type: cross Abstract: Deep neural networks have achieved strong performance in genomic sequence classification; however, relating their predictions to biologically meaningf

arXiv:2602.04473v2 Announce Type: replace Abstract: Recently, diffusion models have brought novel insights to pan-sharpening and notably boosted fusion precision. However, most existing models perform

arXiv:2605.14068v1 Announce Type: new Abstract: We introduce CurveBench, a benchmark for hierarchical topological reasoning from visual input. CurveBench consists of extbf{756 images} of pairwise non-

Databricks has integrated OpenAI's GPT-5.5 model into enterprise agent workflows, enabling organizations to build and deploy AI agents with advanced language capabilities. This partnership leverages D

arXiv:2605.14053v1 Announce Type: cross Abstract: The application of Large Language Models to Question Answering has shown great promise, but important challenges such as hallucinations and erroneous

arXiv:2605.14473v1 Announce Type: cross Abstract: The Context-Compliance Regime in Retrieval-Augmented Generation (RAG) occurs when retrieved context dominates the final answer even when it conflicts

arXiv:2605.14347v1 Announce Type: new Abstract: We introduce Exemplar Partitioning (EP), an unsupervised method for constructing interpretable feature dictionaries from large language model activation

arXiv:2604.06757v2 Announce Type: replace Abstract: Multimodal generation has long been dominated by text-driven pipelines where language dictates vision but cannot reason or create within it. We chal

arXiv:2605.14218v1 Announce Type: new Abstract: The key problem facing ChatGPT-like AI's use across society is that its behavior can shift, unnoticed, from desirable to undesirable -- encouraging self

arXiv:2509.14232v5 Announce Type: replace Abstract: Exams are a fundamental test of expert-level intelligence and require integrated understanding, reasoning, and generation. Existing exam-style bench

arXiv:2605.14841v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) has become the dominant paradigm for parameter-efficient fine-tuning (PEFT) of large language models (LLMs). However, its b

arXiv:2605.14194v1 Announce Type: new Abstract: Large Language Models (LLMs) pose a significant risk of safety misalignment after finetuning, as models can be compromised by both explicitly and implic

arXiv:2605.14498v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly serve as personal assistants and workplace collaborators, where their utility depends on memory systems t

This OpenAI Academy resource explores practical applications of Codex, their AI code generation model, within data science workflows and teams. It likely covers how data scientists leverage Codex to a

arXiv:2511.15407v3 Announce Type: replace Abstract: Humans learn by observing, interacting with environments, and internalizing physics and causality. Here, we aim to ask whether an agent can similarl

arXiv:2605.14392v1 Announce Type: new Abstract: We pursue a vision for self-improving language models in which the model does not merely generate problems or traces to imitate, but constructs the envi

arXiv:2508.06202v2 Announce Type: replace-cross Abstract: Continual Visual Instruction Tuning (CVIT) enables Multimodal Large Language Models (MLLMs) to incrementally learn new tasks over time. Howeve

arXiv:2602.16898v5 Announce Type: replace-cross Abstract: Task planning for robotic manipulation with large language models (LLMs) is an emerging area. Prior approaches rely on specialized models, fin

arXiv:2605.14744v1 Announce Type: cross Abstract: Large language models in regulated financial workflows are governed by natural-language policies that the same model interprets, creating a principal-

arXiv:2605.14005v1 Announce Type: new Abstract: Speculative decoding has become a widely adopted technique for accelerating large language model (LLM) inference by drafting multiple candidate tokens a

arXiv:2605.13857v1 Announce Type: cross Abstract: The creation of cinematic-quality animal effects necessitates the precise modeling of muscle and fur dynamics, a process that remains both labor-inten

arXiv:2602.21545v3 Announce Type: replace Abstract: Muon has recently emerged as a strong optimizer for large language model pre-training, orthogonalizing the momentum matrix via Newton--Schulz polar

arXiv:2601.22197v3 Announce Type: replace-cross Abstract: Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings

arXiv:2605.15154v1 Announce Type: cross Abstract: Feature attribution analysis is critical for interpreting machine learning models and supporting reliable data-driven decisions. However, feature attr

arXiv:2605.14704v1 Announce Type: cross Abstract: In real-world scenes, target objects may reside in regions that are not visible. While humans can often infer the locations of occluded objects from c

arXiv:2507.07776v3 Announce Type: replace Abstract: Unrestricted adversarial attacks aim to fool computer vision models without being constrained by ell_p-norm bounds to remain imperceptible to humans

arXiv:2605.13999v1 Announce Type: new Abstract: Discrete diffusion models are increasingly competitive for language modeling, yet it remains unclear how their denoising objectives organize learning. A

arXiv:2605.14415v1 Announce Type: cross Abstract: Coding agents powered by large language models are increasingly expected to perform realistic software maintenance tasks beyond isolated issue resolut