Search: “models”

arXiv:2606.26029v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) demonstrate strong performance on standard visual question answering benchmarks, yet their scalability under co

arXiv:2606.24891v1 Announce Type: cross Abstract: Ontologies enable scalable energy services in buildings by supporting interoperability and automation. Project Haystack is a building ontology that is

arXiv:2606.24986v1 Announce Type: new Abstract: Automated cattle posture-classification systems frequently report near-perfect accuracy, yet their robustness under realistic deployment conditions rema

arXiv:2606.24257v1 Announce Type: new Abstract: High-quality 3D vehicle assets are essential for autonomous driving simulation. Although multi-view diffusion-based paradigms enable controllable single

arXiv:2606.13535v3 Announce Type: replace-cross Abstract: Particle physics collider experiments provide Rivet routines as part of the analysis preservation strategy for model-independent measurements.

arXiv:2606.24096v1 Announce Type: cross Abstract: Robust perception underpins autonomous driving, and most recent progress comes from scaling the model-larger backbones, foundation models, and coopera

arXiv:2606.24064v1 Announce Type: new Abstract: Distilling reasoning capabilities from strong to weak language models typically involves imitating specific solution trajectories, effectively transferr

Big Tech's 1 trillion AI moat just got DESTROYED by a free Chinese download. Microsoft, Amazon, Google, and Meta are pouring fortunes into chips and data centers because they have been told that whoev

btw Zai IPO'ed in Jan at HK$120 a share. when I first met @louszbd nobody really knew anyone using GLM's. now they have beat deepseek with the world's undisputed top open model and in some respects (s

This post discusses developing a GitLab nodes adapter to enable Hugging Face model storage on decentralized git infrastructure, leveraging open-source platforms for distributed model repository hostin

arXiv:2606.24722v1 Announce Type: new Abstract: Frontier AI training is increasingly shaped by access to dense, centrally controlled accelerator clusters. This creates a structural advantage for hyper

arXiv:2606.16821v2 Announce Type: replace Abstract: Large language model (LLM)-based search agents synthesize open-web content into actionable recommendations on behalf of users, creating a risk that

arXiv:2606.24570v1 Announce Type: new Abstract: Vision-language contrastive pretraining has become the dominant recipe for 3D medical foundation models, leveraging the large volumes of paired scans an

arXiv:2606.22419v2 Announce Type: replace Abstract: A recent Nature Medicine study reports that general-purpose frontier LLMs outperform specialized retrieval-augmented clinical tools on medical bench

arXiv:2606.23885v1 Announce Type: cross Abstract: Representation alignment has emerged as an effective approach to improve Multimodal Large Language Models (MLLMs) by regularizing their internal repre

arXiv:2606.24404v1 Announce Type: new Abstract: The incorporation of additional modalities into action recognition models increases their performance across a wide range of settings. However, how this

ParseBench is now also available on Papers with Code! Find it here: https://paperswithcode.co/benchmark/parsebench We benchmarked Mistral OCR against other frontier and open-weight models on ParseBenc

arXiv:2306.05239v3 Announce Type: replace Abstract: Sampled point and voxel methods are usually employed to downsample the dense events into sparse ones. After that, one popular way is to leverage a g

arXiv:2606.24773v1 Announce Type: new Abstract: Non-autoregressive generation offers a powerful paradigm for iterative refinement, allowing models to recursively critique, erase and regenerate arbitra

arXiv:2606.24623v1 Announce Type: cross Abstract: Retrieval-Augmented Generation enhances large language models by incorporating external knowledge, but deploying it in sensitive scenarios risks priva

arXiv:2606.24184v1 Announce Type: new Abstract: Retrosynthetic planning seeks to connect a target molecule to commercially available starting materials through a multistep route. Classical planners co

arXiv:2508.10971v2 Announce Type: replace-cross Abstract: Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret d

arXiv:2606.24361v1 Announce Type: new Abstract: Sign language models are typically trained on datasets captured under constrained conditions, with limited viewpoint, background, and signer-identity di

arXiv:2606.23743v1 Announce Type: cross Abstract: Modern video diffusion models achieve higher generation quality through scaling, but this also increases inference cost. Although many acceleration me

arXiv:2606.24460v1 Announce Type: cross Abstract: Commercial large language models bill, scale latency, and budget context per token. Yet tokenizers assign more subword tokens to the same meaning in s

arXiv:2606.23838v1 Announce Type: new Abstract: When two or more parameters or labels produce similar data, they are degenerate, or hard to distinguish. Degeneracies render both label prediction and i

arXiv:2606.24525v1 Announce Type: new Abstract: GUI agents powered by vision-language models show strong potential for automating digital tasks, yet frequently fail in long-horizon scenarios due to th

arXiv:2602.14257v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents have made remarkable progress on complex reasoning, evaluating them in real-world environments remains

arXiv:2606.21736v1 Announce Type: new Abstract: Single domain generalization (SDG) aims to learn a robust model, which could perform well on many unseen domains while there is only one single domain a

arXiv:2410.07397v2 Announce Type: replace Abstract: As data-driven modeling of physical dynamical systems becomes more prevalent, a new challenge is emerging: making these models more compatible and a

arXiv:2511.22973v2 Announce Type: replace Abstract: Long video generation is a critical step toward building realistic world models, requiring both high visual fidelity and long-range interaction cons

arXiv:2606.23270v1 Announce Type: new Abstract: As instruction-based editing models and multimodal large language models advance, diverse image editing tasks have become feasible. However, achieving p

arXiv:2606.18429v2 Announce Type: replace Abstract: Accurately aligning CAD models to their corresponding objects in indoor RGB-D scans is a central challenge in 3D semantic reconstruction. The task r

arXiv:2606.22699v1 Announce Type: new Abstract: Frontier multimodal models can guess whether a person is lying from a testimony video. To do so, they stream that raw face and voice to a third-party mo

arXiv:2606.23608v1 Announce Type: cross Abstract: Recent attempts to combine large language models (LLMs) with causal discovery ask models to infer pairwise directions, propose graph structures, or in

arXiv:2606.22862v1 Announce Type: new Abstract: Forced chain-of-thought (CoT) is widely assumed to make vision-language models more reliable on video question answering. We propose a small three-probe

arXiv:2606.21020v1 Announce Type: new Abstract: The evaluation of vision-language models (VLMs) for chest X-ray (CXR) analysis has largely been limited to disease-presence classification without visua

arXiv:2606.21146v1 Announce Type: new Abstract: Text-to-video (T2V) diffusion models have made it increasingly easy to synthesize realistic and temporally coherent videos, while recent personalization

arXiv:2606.22669v1 Announce Type: new Abstract: Adaptive stochastic convex optimization (SCO) methods face a fundamental ``price of adaptivity'' barrier: under the standard set of assumptions, they ca

arXiv:2602.04291v2 Announce Type: replace Abstract: Multi-expert systems, where multiple Large Language Models (LLMs) collaborate to solve complex tasks, are increasingly adopted for high-performance

arXiv:2606.18239v2 Announce Type: replace Abstract: We present EBench, a simulation benchmark that diagnoses generalist mobile manipulation policies beyond a single success-rate scalar. EBench compris

arXiv:2606.22925v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models increasingly rely on multi-dataset training and evaluation, yet public EEG datasets still lack a shared t

arXiv:2606.22955v1 Announce Type: new Abstract: Large-scale pretrained foundation models have revolutionized general medical screening, but often falter on rare diseases because such conditions are un

arXiv:2503.21321v2 Announce Type: replace-cross Abstract: With the rapid development of machine learning and deep learning techniques, actuaries and the broader insurance industry face a persistent tr

arXiv:2606.22875v1 Announce Type: new Abstract: Training Latent Diffusion Models (LDMs) within Federated Learning (FL) has attracted increasing attention due to its ability to combine the powerful gen

arXiv:2606.22168v1 Announce Type: new Abstract: Medical image segmentation plays an important role in computer aided diagnosis, treatment planning, and disease monitoring. U-Net has been widely used f