Knowledge catalogue
arXiv:2608.05761v1 Announce Type: new Abstract: The development of nanotherapeutics often involves extensive empirical optimization due to the sensitivity of nanoparticle properties, such as size and
arXiv:2608.05993v1 Announce Type: new Abstract: Much clinical value is conveyed not through structured records but through communication: exchanges in which patients describe symptoms, clinicians reas
arXiv:2608.05806v1 Announce Type: cross Abstract: While standard Next-Token Prediction (NTP) lays the foundation of language model pre- training, its teacher-forced training paradigm may not be optima
arXiv:2608.05945v1 Announce Type: new Abstract: Test-time adaptation (TTA) can improve the recognition accuracy of vision-language models under distribution shift, but often degrades calibration, maki
arXiv:2608.06179v1 Announce Type: new Abstract: Preference optimisation has proven effective for improving large language models but typically relies on costly human preference annotations. Extending
arXiv:2608.06021v1 Announce Type: new Abstract: Effective Visual Localization (VL) requires a map of the environment that combines compactness for efficient scalability with robustness against visual
📝 Introduction We present Wan-Animate-2, a novel end-to-end character animation framework that directly consumes driving videos in a redesigned Diffusion Transformer, which achieves high-fidelity moti
arXiv:2608.05576v1 Announce Type: new Abstract: When a large language model (LLM) writes Harry Potter fanfiction, it reliably produces fundamental elements of the Hogwarts universe, such as recognizab
arXiv:2608.04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times. We evaluate fixed-cutoff drcosa
arXiv:2608.04065v1 Announce Type: cross Abstract: Vehicle-to-everything (V2X) systems increasingly incorporate large language models (LLMs) for semantic tasks such as message summarization, operator a
arXiv:2608.04509v1 Announce Type: new Abstract: Vision-language systems combine images with retrieved text, but these sources can disagree or jointly fail to support an answer. Reliable models must id
arXiv:2608.04396v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have driven significant progress in robotic manipulation, yet they fundamentally struggle with the vision-override p
arXiv:2608.04252v1 Announce Type: cross Abstract: The dynamical Lie algebraic (DLA) theory of variational quantum algorithms (VQAs) predicts commonplace exponentially vanishing loss and gradient varia
arXiv:2608.04906v1 Announce Type: new Abstract: This study introduces a diffusion-based framework for robust and accurate semantic segmentation of lumbar spine MRI scans from patients with low back pa
arXiv:2608.04885v1 Announce Type: cross Abstract: Standard accuracy metrics for VLMs often mask significant reliability failures in sensitive domains. In this work, we utilize a histopathology-validat
arXiv:2607.18559v2 Announce Type: replace-cross Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications the data arise as a single trajecto
arXiv:2608.04999v1 Announce Type: cross Abstract: Analog circuit design automation using reinforcement learning (RL) has emerged as a promising approach for reducing manual effort. However, many exist
arXiv:2502.09151v3 Announce Type: replace Abstract: Diffusion models are one of the key architectures of generative AI. Their main drawback, however, is the computational costs. This study indicates t
arXiv:2503.15560v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly vulnerable to sophisticated multi-turn manipulation attacks, where adversaries strategically build conte
arXiv:2608.04820v1 Announce Type: new Abstract: Face inpainting with diffusion models has recently achieved impressive visual quality, yet preserving identity fidelity under significant occlusion and
arXiv:2510.20351v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly exposed to data contamination, i.e., performance gains driven by prior exposure of test datasets
arXiv:2608.03204v1 Announce Type: cross Abstract: Post-training reinforcement learning (RL) algorithms are commonly used to align large vision-language models (LVLMs) with human intent and the require
arXiv:2603.19087v2 Announce Type: replace Abstract: Creativity is the ability to come up with novel ideas, a capacity crucial for human development and flourishing. Are large language models (LLMs) cr
arXiv:2608.03035v1 Announce Type: new Abstract: Prior work has shown that LLMs encode the truth of factual propositions along linear directions in activation space. It's unclear how these representati
arXiv:2608.03600v1 Announce Type: new Abstract: Partial differential equations (PDEs) become actionable in science and engineering not as isolated formulae, but as executable workflows that connect mo
arXiv:2608.03812v1 Announce Type: new Abstract: Omni-modal large language models (Omni-LLMs) have achieved remarkable performance on audio-visual understanding tasks, but processing long and highly re
arXiv:2608.02942v1 Announce Type: new Abstract: Diffusion language models (dLLMs) can predict many tokens in parallel, but accurate generation still requires many iterative denoising steps. Few-step d
arXiv:2608.02953v1 Announce Type: new Abstract: Realistic weather translation is valuable for developing and evaluating autonomous driving systems, yet collecting paired videos of the same scenes unde
Two weeks ago, I resigned from OpenAI to join Conduit as a founding researcher, where we're training models to non-invasively read the human mind. I've written some thoughts about what telepathy could
arXiv:2603.10771v2 Announce Type: replace Abstract: Large language models (LLMs) trained with canonical tokenization exhibit surprising robustness to non-canonical inputs such as character-level token
arXiv:2608.01717v1 Announce Type: new Abstract: Recent reinforcement learning methods for diffusion large language models (dLLMs) commonly rely on on-policy rollouts generated by the target dLLM itsel
arXiv:2607.10729v2 Announce Type: replace Abstract: Molecular property models are commonly evaluated by holding out Bemis-Murcko scaffolds, yet a scaffold identifier is only one notion of chemical unf
arXiv:2608.01821v1 Announce Type: new Abstract: Diffusion vision-language models (dVLMs) iteratively denoise masked responses while conditioning each denoising step on visual evidence, making visual c
arXiv:2608.00793v1 Announce Type: new Abstract: Dynamic manipulation requires robots to infer target motion and respond promptly, yet existing World-Action Models (WAMs) typically condition only on th
arXiv:2608.01663v1 Announce Type: new Abstract: Promptable segmentation foundation models (FMs) such as SAM3 and Medical SAM3 promise few-shot, interactively-specified segmentation for medical imaging
arXiv:2608.01547v1 Announce Type: cross Abstract: Drifting objectives compare a target and model distribution through a vector field observed noisily at finitely many locations. We ask what distributi
arXiv:2608.01265v1 Announce Type: cross Abstract: Despite recent progress in Vision-Language-Action (VLA) models for robotic manipulation, the action chunk remains a weakly structured interface. Exist
arXiv:2608.01535v1 Announce Type: new Abstract: Vision-language models (VLMs) are emerging as a key component of embodied intelligence, with growing applications in auto-labeling and end-to-end autono
arXiv:2608.02137v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit strong generalization across multimodal tasks but remain vulnerable to adversarial perturbations. Existing attacks
arXiv:2608.01573v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models achieve promising performance in robotic manipulation, typically measured by success rates aggregated over pr
arXiv:2607.28707v1 Announce Type: new Abstract: Entropy-based pruning has been proposed as an effective method for compressing Chain-of-Thought (CoT) reasoning with negligible accuracy loss. We test t
arXiv:2602.02685v3 Announce Type: replace Abstract: Decentralized Diffusion Models (DDMs) route denoising through experts trained independently on disjoint data clusters, which can strongly disagree i
Hugging Face CEO says China is winning the AI race and dominating on open models https://www.cnbc.com/2026/08/03/hugging-face-china-ai-race-open-models.html?taid=6a70b9a7142a0e000189af30&utm_campaign=
arXiv:2607.28696v1 Announce Type: cross Abstract: Medical vision-language models (VLMs) can retain high observed marginal coverage after clinical shift while substantially under-covering an individual
Hermes Agent is now dramatically more efficient, especially for smaller/weaker/local models! With the help of @nvidia's Nemo Relay and several other strategies Hermes was able to identify a ton of opt
It feels like Ollama’s brand identity has shifted. It used to be centered on self‑hosted models, but now most of the new releases seem to be API‑based services with far more emphasis on cloud integrat
arXiv:2607.15715v2 Announce Type: replace Abstract: Large language model (LLM) agents are increasingly used for complex information-extraction tasks, yet it remains unclear whether agentic components
arXiv:2607.28410v1 Announce Type: cross Abstract: Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution
arXiv:2607.28460v1 Announce Type: new Abstract: A major issue in Security Operations Centers (SOCs) is alert fatigue, as the number of detections reported is more than staff can triage in a given day.
arXiv:2607.28397v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) over knowledge graphs requires retrievers that can effectively capture both graph structure and semantic informat
arXiv:2503.15846v2 Announce Type: replace Abstract: Dynamic Scene Graph Generation (DSGG) aims to capture objects and their evolving relations in videos. Despite recent progress, the practicality and
Excited to work with the @IntelBusiness team on enabling open models with Intel Core Ultra Series 3. Built to bring open-source LLMs to private machines, @Ollama uses Intel Core Ultra Series 3 to run
arXiv:2607.26602v1 Announce Type: cross Abstract: The rapid development of the Internet of Everything (IoE) is accelerating the adoption of intelligent applications. However, the massive number of con
arXiv:2505.01043v2 Announce Type: replace Abstract: Large language models (LLMs) have achieved impressive performance across various domains. However, the substantial hardware resources required for t
arXiv:2607.26991v1 Announce Type: new Abstract: Despite the impressive visuomotor capabilities enabled by Vision-Language-Action (VLA) models, their performance often degrades on challenging and out-o
arXiv:2607.26984v1 Announce Type: cross Abstract: Mapping an atomic structure to a compact set of geometric descriptors is an essential step in any machine-learning application to atomic-scale modelin
arXiv:2602.09611v2 Announce Type: replace-cross Abstract: Watermarking has emerged as a pivotal solution for content traceability and intellectual property protection in large vision language models (
arXiv:2607.00078v2 Announce Type: cross Abstract: We propose a Transformer-based Reinforcement Learning architecture, 'LB-Explorer', to search for heterotic line bundle standard models arising from co
arXiv:2607.24797v1 Announce Type: cross Abstract: In the literate human brain, reading and writing are two doubly-dissociable systems: a ventral decoding route (impaired in pure alexia) and a fronto-p
arXiv:2607.25818v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs), such as Qwen2.5-VL and InternVL3, generate large numbers of vision tokens for high-resolution inputs, l