Search: “models”

arXiv:2606.21395v1 Announce Type: new Abstract: Atomistic structure and natural language have long been modeled separately, with language models either calling atomistic models as tools or being fine-

arXiv:2508.20906v3 Announce Type: replace Abstract: While foundation models have revolutionized fields such as natural language processing and computer vision, their potential in graph machine learnin

This episode discusses the growing landscape of open-source AI models, contrasts the approaches and philosophies of Model Labs versus Agent Labs in AI development, and explores fundamental limitations

arXiv:2606.12025v1 Announce Type: new Abstract: Finite element (FE) modeling of safety-critical infrastructure such as bridge barriers requires high-fidelity nonlinear dynamic analysis, yet the curren

arXiv:2606.11289v1 Announce Type: new Abstract: Diffusion models have consistently driven progress in text-to-image generation. However, it is challenging to attribute recent progress to specific mode

arXiv:2602.22962v2 Announce Type: replace Abstract: Data-driven models are revolutionizing weather forecasting. To optimize training efficiency and model performance, this paper analyzes empirical sca

arXiv:2509.23982v2 Announce Type: replace-cross Abstract: Preference alignment is a critical step in making Large Language Models (LLMs) useful and aligned with (human) preferences. Existing approache

arXiv:2606.12385v1 Announce Type: new Abstract: Modern LLM training pipelines increasingly rely on other models to generate data, filter corpora, judge outputs, and guide development decisions. These

'Switch to a cheaper model to save money' is a problem because cheaper models are worse (maybe they are good enough for a particular purpose, but still worse). More often a better approach is hierarch

arXiv:2310.05264v5 Announce Type: replace-cross Abstract: In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as 'consistent model reproducibility': g

arXiv:2606.09548v1 Announce Type: cross Abstract: Federated Learning (FL) allows a set of clients to collectively train a global model without sharing local training data. Giving the responsibility of

arXiv:2606.03540v1 Announce Type: new Abstract: Existing human attention (saliency) modeling methods persist as highly fragmented across modalities, scenes, and task formulations. Consequently, even w

arXiv:2606.02765v1 Announce Type: cross Abstract: Model dimension (d_{model}) is a fundamental hyperparameter in transformer language models, yet its role in setting the geometric limits of feature re

arXiv:2606.03603v1 Announce Type: cross Abstract: World models and multimodal large language models (MLLMs) provide complementary capabilities for predicting future outcomes from static visual observa

Jay Peters / The Verge: Microsoft debuts MAI-Thinking-1, its first advanced reasoning AI model, trained “from the ground up on clean data, without distillation from third-party models” — MAI-Thinking-

Super excited to announce seven new world-class MAI models today. They represent what we consider a new era in AI designed to keep you in control and on the frontier. First is our text foundation mode

arXiv:2605.30963v1 Announce Type: cross Abstract: We present AMix-2, a protein-text foundation model that establishes protein as a native modality in large language models (LLMs), unifying protein und

arXiv:2510.27607v3 Announce Type: replace Abstract: Augmenting vision-language-action models (VLAs) with world models is promising for robotic policy learning but faces challenges in jointly predictin

I cannot verify this claim as it references a future date (the URL timestamp appears invalid) and describes a model version (GPT-5.5 Pro) that doesn't exist in current public releases. The post appear

arXiv:2605.30219v1 Announce Type: new Abstract: Long-horizon interactions require language models to manage accumulating information: when to update their state, when to preserve their state, and what

arXiv:2605.27286v1 Announce Type: cross Abstract: Time series foundation models (TSFMs) are transforming the forecasting paradigm through large-scale cross-domain pretraining. However, most existing T

arXiv:2602.05202v2 Announce Type: replace Abstract: Aligning video generative models with human preferences remains challenging: current approaches rely on Vision-Language Models (VLMs) for reward mod

arXiv:2605.23861v1 Announce Type: cross Abstract: Causal generative modeling is essential for developing reliable and transparent AI systems capable of counterfactual reasoning. While existing approac

arXiv:2510.07962v2 Announce Type: replace Abstract: Large language models (LLMs) have demonstrated remarkable progress in reasoning, often through supervised fine-tuning (SFT). However, SFT is resourc

arXiv:2604.07303v2 Announce Type: replace Abstract: Understanding and modeling animal behavior is essential for studying collective motion, decision-making, and bio-inspired robotics. Yet, evaluating

arXiv:2605.17276v1 Announce Type: cross Abstract: While scaling laws have established a fundamental framework for foundation models in natural language processing, their applicability to electrocardio

GLM-5.1 from @Zai_org is now live on OrcaRouter • #1 open-source model on SWE-Bench Pro • Beats closed source models on real-world repo repair benchmarks • MIT licensed • 200K context • Built for long

arXiv:2510.02453v3 Announce Type: replace-cross Abstract: Frontier language models are deployed as black-box services, where model weights cannot be modified and customization is limited to prompting.

arXiv:2605.14028v1 Announce Type: new Abstract: Since the emergence of Vision Transformer (ViT), it has been widely used in generative language model and generative visual model. Especially in the cur

arXiv:2605.12843v1 Announce Type: cross Abstract: Model merging aims to combine multiple task-specific expert models into a single model without joint retraining, offering a practical alternative to m

arXiv:2605.13568v1 Announce Type: cross Abstract: Myocardial infarction (MI) is a leading cause of death, and its adverse outcomes are urgent to predict. Yet ECG-based prognostic models underperform b

arXiv:2605.13160v1 Announce Type: cross Abstract: Modern Bayesian optimization and adaptive sampling methods increasingly rely on nonlinear parametric models, yet theoretical guarantees for such model

arXiv:2605.10956v1 Announce Type: cross Abstract: Machine-learned surrogate modeling of advection may accelerate geoscientific models, but existing approaches have either achieved limited speedup or h

GLM models are now live on @tensorix_ai We’re partnering to bring cost-efficient frontier AI models to developers, startups, and enterprises across Europe and beyond — and to back the Sovereign AI eco

arXiv:2509.24244v4 Announce Type: replace Abstract: We study empirical scaling laws for language model merging measured by cross-entropy. Despite its wide practical use, merging lacks a quantitative r

arXiv:2605.08301v1 Announce Type: cross Abstract: Hybrid State-Space models combine Attention with recurrent State-Space Model (SSM) layers, balancing eidetic memory from Attention with compressed fad

arXiv:2512.13618v3 Announce Type: replace Abstract: Representing continuous time is a critical and under-explored challenge in modeling temporal event sequences with large language models (LLMs). Vari

arXiv:2605.10405v1 Announce Type: new Abstract: Selecting the best large language model (LLM) for a fixed benchmark is often expensive, since exhaustive evaluation requires running every model on ever

arXiv:2605.07301v1 Announce Type: new Abstract: Accurately predicting opponents' behavior from interactions is a fundamental capability for large language model (LLM)-based agents in multi-agent and g

arXiv:2605.01185v1 Announce Type: new Abstract: Accelerated magnetic resonance imaging (MRI) enabled by the training of deep learning (DL)-based image recon. models requires large and diverse raw k-sp

Sam Altman, CEO of OpenAI, recommended trying ChatGPT's new instant model, suggesting it performs significantly better than thinking-model-only approaches. The post encourages users who have relied ex

Stanford's latest seminar is a deep dive into the evolution of world modeling in AI. Focuses on the shift in the world model from traditional reconstruction methods toward latent space prediction. Cov

arXiv:2604.25159v1 Announce Type: new Abstract: Landslide investigation relies on sufficient and well-balanced observational data influenced by geological, hydrological, and anthropogenic factors. Ava

arXiv:2604.25416v1 Announce Type: new Abstract: Model-Based Reinforcement Learning distinguishes between physical dynamics models operating on proprioceptive inputs and latent dynamics models operatin

arXiv:2604.22981v1 Announce Type: new Abstract: Reward models in RLHF are trained to score only the final token of a response - a choice that discards rich signal from every intermediate position and

// Agentic World Modeling // Massive 40-author survey just dropped. Cleanest taxonomy of world models in agent research I've seen. (bookmark it) The paper proposes a 'levels × laws' framework. Three c