Search: “models”

arXiv:2607.26831v1 Announce Type: new Abstract: Despite the existence of exponentially many valid tokenizations for a given string, language models operate on a single canonical sequence deterministic

arXiv:2607.25257v1 Announce Type: cross Abstract: Item Response Theory (IRT) has recently been proposed as a framework for evaluating large language model (LLM) benchmarks by separating a model's late

arXiv:2607.25763v1 Announce Type: cross Abstract: This paper proposes a multitask wireless foundation model via adaptive low-rank masked autoencoders (WALoMA), a unified multi-task foundation model fo

arXiv:2607.22671v1 Announce Type: new Abstract: Foundation-model safety benchmarks capture the AI risks of their time of publication: as models improve and governments pass new AI-safety legislation,

arXiv:2506.05678v3 Announce Type: replace Abstract: The evolution of sequence modeling architectures, from recurrent neural networks and convolutional models to Transformers and structured state-space

arXiv:2602.17634v2 Announce Type: replace-cross Abstract: Learning time series foundation models has been shown to be a promising approach for zero-shot time series forecasting across diverse time ser

arXiv:2601.14032v2 Announce Type: replace Abstract: Reward models (RMs) play a pivotal role in aligning large language models (LLMs) with human preferences. Due to the difficulty of obtaining high-qua

Big update: Among open-weight models, Kimi K3 (Max) is #1 in the Agent Arena with +9.75% net-improvement, surpassing GLM-5.2 (Max) at +7.12%, and landed the #1 spot across 5 signals (see below). Kimi

arXiv:2607.21978v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures have been widely adopted in large language models, yet parameter-efficient fine-tuning (PEFT) for MoE models rema

arXiv:2607.20492v1 Announce Type: cross Abstract: Language models in production do not write prose. They fill forms: JSON fields, function arguments, extraction templates. We show that the form itself

arXiv:2607.19608v1 Announce Type: new Abstract: Instruction tuning is meant to make language models follow user requests, yet it is unclear whether small models comply when an instruction conflicts wi

arXiv:2509.24739v4 Announce Type: replace Abstract: Vision-Language Foundation Models (VLMs), trained on large-scale multimodal datasets, have driven significant advances in Artificial Intelligence (A

arXiv:2607.13119v1 Announce Type: cross Abstract: In standard federated learning systems, the parameter server broadcasts the global model to the participating devices in every iteration. Motivated by

arXiv:2607.13101v1 Announce Type: cross Abstract: Global Station Weather Forecasting (GSWF) is pivotal for localized and extreme weather prediction over key regions. Despite efforts to exploit look-ba

arXiv:2607.12787v1 Announce Type: new Abstract: Recent advances in multimodal large language models (MLLMs) have significantly improved the performance of multimodal emotion recognition (MER) and enab

What it is: ATH-MaaS/OvisOCR2 - a 0.8B document-parsing VLM post-trained from Qwen3.5-0.8B (SFT + RL + OPD), Apache 2.0, runs on vLLM 0.22.1. One prompt per page image -> complete markdown (HTML table

arXiv:2607.12792v1 Announce Type: cross Abstract: Jailbreak-robustness research typically evaluates safety through generated responses using an LLM-as-judge approach. Such evaluations, however, are se

For years, we’ve built with a clear priority: putting the practical needs of the enterprise first. Long before generative AI dominated the headlines, we were focused on building the global infrastruct

New model for AMD Strix Halo users: My 198B Step 3.7 Flash release was a big hit, but this one may be even better: 298B-parameter Hy3, now running on a new 2-bit FPX codebook designed to map efficient

.@satyanadella's Reverse Information Paradox is real. What @satyanadella's calling for already exists: open models. Over 9M+ developers have used @ollama to access open models and keep their competiti

arXiv:2607.08745v1 Announce Type: new Abstract: Recent advances in Vision-Language Models, Large Language Models, and Multimodal Large Language Models have improved autonomous driving tasks such as sc

arXiv:2504.01894v2 Announce Type: replace Abstract: We present a bifidelity method for uncertainty quantification of parameter estimates in complex systems, leveraging generative models trained to sam

On June 16th, Z.ai released GLM 5.2, its latest flagship model. At the time of announcement, it advertised scores at or near Anthropic and OpenAI's models, and far ahead of GLM 5.1. In the world of us

arXiv:2607.05390v1 Announce Type: cross Abstract: Predicting object dynamics (i.e., world modeling) is a fundamental challenge for robotic manipulation, and modeling deformable objects presents a part

arXiv:2607.04140v1 Announce Type: cross Abstract: Autoregressive (AR) text-to-speech (TTS) models generate discrete speech tokens sequentially, which makes inference slow and can degrade robustness by

This post from DAIR.AI recommends resources for developers and organizations scaling applications using open-weight language models, emphasizing the importance of planning infrastructure and deploymen

FULL INTERVIEW: Engineers Edward Coristine (@as400495) and Tai Groot (@taigrr) just released an ML model called Rampart for the National Design Studio. It's a local-first, open-source AI privacy model

Nous Research is conducting benchmarking tests to evaluate whether combinations of open-source models can achieve performance comparable to Anthropic's Claude Opus while maintaining significantly lowe

arXiv:2505.12843v2 Announce Type: replace Abstract: Reinforcement Learning from Human Feedback (RLHF) relies on reward models to align large language models with human preferences. However, RLHF often

arXiv:2606.22568v1 Announce Type: new Abstract: Training image generation foundation models consumes substantial resources. Previous methods have attempted to leverage semantic guidance to accelerate

arXiv:2606.12191v1 Announce Type: cross Abstract: Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the

arXiv:2408.02600v3 Announce Type: replace Abstract: Background. Biomedical language models should improve performance on biomedical text while retaining general-language-modeling fluency. For Mamba-ba

arXiv:2606.11502v1 Announce Type: cross Abstract: Language models can state that 'the Earth orbits the Sun' and, when role-playing Aristotle, assert the opposite. Recent work argues that persona adopt

arXiv:2606.10467v1 Announce Type: new Abstract: The rapid advancement of large language models (LLMs) has intensified debates about their significance for linguistic theory. These debates are commonly

arXiv:2606.08381v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly released and deployed through opaque development and deployment pipelines, enabling model providers to i

arXiv:2606.07528v1 Announce Type: cross Abstract: Hallucination in large language models (LLMs), defined as the generation of factually incorrect or unsupported content, remains a critical barrier to

arXiv:2606.07726v1 Announce Type: new Abstract: Large Language Models are typically benchmarked by evaluating every model on every test query. For practitioners seeking the best model to deploy, this

arXiv:2606.08238v1 Announce Type: new Abstract: Constitutive modeling of the relationship between process-imposed material states and fundamental material properties is critical to control of material

arXiv:2606.07810v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used as judges for evaluating model outputs, but their high cost, latency, and opacity limit scalability. We i

arXiv:2206.08598v2 Announce Type: replace Abstract: A common way to analyze learning of statistical models is to consider operations in the models parameter space, however this becomes challenging whe

arXiv:2606.06888v1 Announce Type: new Abstract: Classical scaling laws for language model pretraining balance model size against training dataset size under a fixed compute budget, assuming abundant d

arXiv:2606.07127v1 Announce Type: new Abstract: Interactive agents trained only against task return can achieve high scores while failing to represent the mechanisms that make their actions succeed. T

arXiv:2508.02039v2 Announce Type: replace Abstract: Increasing concerns for data privacy and other difficulties associated with retrieving source data for model training have created the need for sour

arXiv:2606.07157v1 Announce Type: new Abstract: Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficien

This article describes a collaborative hackathon project involving five research labs that developed a financial simulation drama using small language models, focusing on building complex multi-agent

arXiv:2606.04552v1 Announce Type: new Abstract: Genomic foundation models increasingly adopt large language model architectures, yet almost universally rely on fixed tokenization schemes such as k-mer