Knowledge catalogue
arXiv:2605.18137v1 Announce Type: new Abstract: This report presents a unified technical system addressing the two core capabilities of world models for autonomous driving: world representation and wo
arXiv:2605.15562v1 Announce Type: new Abstract: Augmenting Transformers with linguistic structures effectively enhances the syntactic generalization performance of language models. Previous work in th
arXiv:2512.01843v2 Announce Type: replace Abstract: Driven by the growing capacity and training scale, Text-to-Video (T2V) generation models have recently achieved substantial progress in video qualit
arXiv:2605.16085v1 Announce Type: cross Abstract: Relational databases store much of the world's structured information, and they are essential for driving complex predictive applications. However, de
arXiv:2605.14163v1 Announce Type: new Abstract: Can a committee of weak reasoning-model calls reach the performance of much stronger models? We study verifier-backed committee search as inference-time
arXiv:2605.14025v1 Announce Type: cross Abstract: Brain-language model comparisons often interpret neural prediction scores as evidence that model representations capture brain-relevant language compu
arXiv:2602.07519v3 Announce Type: replace Abstract: In contrast to static formalisms, computational definitions describe the operational mechanisms of a model. Simulations are an essential part of the
arXiv:2605.14815v1 Announce Type: new Abstract: Video is a rich and scalable source of 3D/4D visual observations, and camera control is a key capability for video generation models to produce geometri
We just added significantly more NVIDIA Blackwell GPUs to better serve GLM-5.1 model on Ollama's cloud. We have been adding more GPUs daily for all the other models. Claude Code: ollama launch claude
arXiv:2511.17031v2 Announce Type: replace-cross Abstract: The rapidly growing computational demands of diffusion models for image generation have raised significant concerns about energy consumption a
arXiv:2605.13829v1 Announce Type: cross Abstract: We introduce Negation Neglect, where finetuning LLMs on documents that flag a claim as false makes them believe the claim is true. For example, models
arXiv:2605.11530v1 Announce Type: new Abstract: Single-model ensembles (SMEs) have attracted attention as a way to approximate some of the benefits of deep ensembles within a single network. However,
arXiv:2605.11374v1 Announce Type: cross Abstract: Test-time compute is widely believed to benefit only large reasoning models. We show it also helps small embedding models. Most modern embedding check
arXiv:2605.08567v1 Announce Type: new Abstract: Action-conditioned world models (ACWMs) have shown strong promise for video prediction and decision-making. However, existing benchmarks are largely res
arXiv:2605.10903v1 Announce Type: new Abstract: This paper proposes a novel approach to address the challenge that pretrained VLA models often fail to effectively improve performance and reduce adapta
arXiv:2605.08885v1 Announce Type: new Abstract: SO(3) equivariant graph neural networks have become the dominant paradigm for atomistic foundation models, achieving high accuracy and data efficiency b
arXiv:2605.10184v1 Announce Type: cross Abstract: We develop a foundation model using 1.2m high resolution satellite images of the Netherlands. By combining a Convolutional Neural Network and a Vision
arXiv:2605.09591v1 Announce Type: new Abstract: Segmentation is a fundamental vision task underlying numerous downstream applications. Recent promptable segmentation models, such as Segment Anything M
arXiv:2605.10942v1 Announce Type: new Abstract: World Action Models (WAMs) have emerged as a promising paradigm for robot control by modeling physical dynamics. Current WAMs generally follow two parad
arXiv:2605.10186v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into legal drafting and research workflows, where incorrect citations or fabricated precedent
arXiv:2510.21954v2 Announce Type: replace Abstract: Large Language Models (LLMs) are trained to support an increasing number of languages, yet their predefined tokenizers remain a bottleneck for adapt
arXiv:2605.10142v1 Announce Type: cross Abstract: Artificial intelligence models are increasingly scaled to improve predictive accuracy, yet it remains unclear whether scale improves the quality of po
arXiv:2605.06673v1 Announce Type: cross Abstract: Aggregate metacognitive quality scores mask within-model variation across MMLU benchmark domains. We administered 1,500 MMLU items (250 per domain, un
Open Models x Headless Agent Execution 🔥 your daily reminder that open models are plenty capable for a lot of coding work. easiest place to feel that out is deepagents! swap the model and go. i've bee
arXiv:2605.04127v1 Announce Type: cross Abstract: Model collapse, the degradation in performance that arises when generative models are trained on the outputs of prior models, is an increasing concern
arXiv:2605.03426v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have broad potential in privacy-sensitive domains such as healthcare and finance, yet strict data-sharing constraints rend
arXiv:2605.04072v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) have been applied to large language models and protein language models, but not systematically to electronic health record
arXiv:2605.03052v1 Announce Type: new Abstract: We study how Large Language Models (LLMs) process negation mechanistically. First, we establish that even though open-weight models often provide wrong
arXiv:2511.18772v2 Announce Type: replace-cross Abstract: Deep neural networks (DNNs) have become valuable intellectual property of model owners, due to the substantial resources required for their de
arXiv:2507.23386v3 Announce Type: replace Abstract: Decoder-only large language models (LLMs) have been increasingly adopted to build embedding models for diverse tasks. To overcome the inherent limit
arXiv:2605.01675v1 Announce Type: cross Abstract: Constraint Programming (CP) is a powerful paradigm for solving combinatorial problems, yet translating natural language problem descriptions into exec
arXiv:2605.01283v1 Announce Type: new Abstract: Plants, crops and their yields are essential to our very existence, but diseases and pests cause large losses every year. As such it is vital to ensure
arXiv:2605.01580v1 Announce Type: new Abstract: Modern deep learning usually treats models as separate artifacts: trained independently, specialized for particular purposes, and replaced when improved
OpenAI's newest default model for ChatGPT might not make stuff up as much. Hallucinations have been an ongoing problem for AI models, but OpenAI says its new GPT-5.5 Instant model has 'significant imp
arXiv:2605.01831v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback has become the standard paradigm for language model alignment, where reward models directly determine alignme
There’s a bunch of conflicting stances I don’t fully understand in the debate of Proprietary RL’d vs Open Harness, Model intelligence, and Agent Labs building harnesses for bespoke tasks Not all of th
arXiv:2605.00419v1 Announce Type: cross Abstract: Model ensembling is a well-established technique for improving the performance of machine learning models. Conventionally, this involves averaging the
Use open models in Fleet! Not every step in an agent workflow needs the same model. Fleet now lets you customize which model each sub-agent uses, so you can route simple tasks to fast/cheap models and
arXiv:2604.27470v1 Announce Type: new Abstract: Millions of clinicians use ChatGPT to support clinical care, but evaluations of the most common use cases in model-clinician conversations are limited.
arXiv:2604.27872v1 Announce Type: new Abstract: Large language model (LLM) agents deployed in clinical settings often exhibit abrupt, threshold-driven behavior, offering little visibility into accumul
arXiv:2604.28031v1 Announce Type: new Abstract: When researchers iteratively refine ideas with large language models, do the models preserve fidelity to the original objective? We introduce DriftBench
arXiv:2604.27167v1 Announce Type: cross Abstract: LLM agents are known to deviate from Nash equilibria in strategic interactions, but nobody has looked inside the model to understand why, or asked whe
arXiv:2603.27341v2 Announce Type: replace-cross Abstract: Recent Artificial Intelligence (AI) models have matched or exceeded human experts in several benchmarks of biomedical task performance, but su
arXiv:2604.25358v1 Announce Type: new Abstract: Evaluating layout-guided text-to-image generative models requires assessing both semantic alignment with textual prompts and spatial fidelity to prescri
arXiv:2603.20645v2 Announce Type: replace Abstract: Diffusion models have become a leading framework in generative modeling, yet their theoretical understanding -- especially for high-dimensional data
arXiv:2604.25907v1 Announce Type: new Abstract: Adapting reasoning models to new tasks during post-training with only output-level supervision stalls under reinforcement learning from verifiable rewar
arXiv:2604.25578v1 Announce Type: new Abstract: We present Marco-MoE, a suite of fully open multilingual sparse Mixture-of-Experts (MoE) models. Marco-MoE features a highly sparse design in which only
arXiv:2502.02452v4 Announce Type: replace Abstract: Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users or object instances and to generate
arXiv:2602.11075v2 Announce Type: replace Abstract: Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic
arXiv:2604.25011v1 Announce Type: new Abstract: Reinforcement learning (RL)-based post-training often improves the reasoning performance of large language models (LLMs) beyond the training domain, whi
arXiv:2604.24589v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly proposed as general-purpose tools for scientific data interpretation, yet their reliability on real astro
arXiv:2604.23284v1 Announce Type: cross Abstract: In this work, we present Au-M-ol, a novel multimodal architecture that extends Large Language Models (LLMs) with audio processing. It is designed to i
arXiv:2604.23091v1 Announce Type: new Abstract: Scaling EEG foundation models requires pooling data across heterogeneous electrode montages, a prerequisite both for larger pretraining corpora and for
arXiv:2601.00823v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have heterogeneous inference energy costs based on which model is used and how much it reasons. To reduce energy, it i
arXiv:2510.14978v2 Announce Type: replace Abstract: Recent image editing models have achieved impressive results while following natural language editing instructions, but they rely on supervised fine
arXiv:2604.23235v1 Announce Type: new Abstract: Diffusion language models expose an explicit denoising trajectory, making it possible to ask when different kinds of information become measurable durin
arXiv:2604.22828v1 Announce Type: cross Abstract: Recent generative AI models have achieved remarkable breakthroughs in language and visual understanding. However, although these models can generate r
arXiv:2602.10298v2 Announce Type: replace Abstract: This paper investigates whether LMs recruit shared computational mechanisms for general Theory of Mind (ToM) and language-specific pragmatic reasoni
arXiv:2510.13117v3 Announce Type: replace-cross Abstract: Masked diffusion models (MDMs) for text offer a compelling alternative to traditional autoregressive language models. Parallel generation make
arXiv:2604.22771v1 Announce Type: cross Abstract: Language models cannot be random. This paper introduces Entropic Deviation (ED), the normalised KL divergence between a model's token distribution and