Knowledge catalogue
arXiv:2606.29545v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing tasks. However, their tendency
arXiv:2512.16413v2 Announce Type: replace Abstract: Current token-sequence-based Large Language Models (LLMs) struggle to directly process 3D Boundary Representation (B-rep) models that contain comple
arXiv:2606.28992v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have demonstrated strong abilities in opendomain question answering, information extraction, and text gen
arXiv:2606.30562v1 Announce Type: new Abstract: Hybrid attention models improve long-context efficiency by retaining only a subset of full-attention layers and replacing the remaining layers with line
arXiv:2508.17117v3 Announce Type: replace-cross Abstract: Existing plant-disease datasets target classification and detection, leaving vision-language models unable to support interactive, reasoning-b
arXiv:2504.17421v2 Announce Type: replace-cross Abstract: Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-sp
arXiv:2606.30552v1 Announce Type: cross Abstract: Cross-embodiment transfer in vision-language-action (VLA) models remains challenging because low-level state and action spaces differ fundamentally ac
arXiv:2503.16550v2 Announce Type: replace Abstract: Neural network language models (LMs) are confronted with significant challenges in generalization and robustness. Currently, many studies focus on i
arXiv:2603.11734v2 Announce Type: replace Abstract: As virtual try-on (VTON) continues to advance, a growing number of real-world scenarios have emerged, pushing beyond the ability of the existing spe
arXiv:2407.01014v2 Announce Type: replace Abstract: Diffusion models excel in solving imaging inverse problems due to their ability to model complex image priors. However, their reliance on large, cle
arXiv:2601.16956v1 Announce Type: cross Abstract: The rapid growth of Large Transformer-based models, specifically Large Language Models (LLMs), now scaling to trillions of parameters, has necessitate
arXiv:2606.27866v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) language models scale model ability with sparsely activated experts, making this architecture a standard recipe for modern larg
arXiv:2601.18197v2 Announce Type: replace Abstract: While Large Vision-Language Models (LVLMs) have significantly advanced GUI agents' capabilities in parsing textual instructions, interpreting screen
arXiv:2606.27872v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have demonstrated strong capabilities in robotic manipulation, but their performance degrades significantly in lon
Good guy DeepSeek gives us accelerated models The most interesting one here is Gemma4-12B, I presume vision included. Might be the best local model in its weight class now, by some margin Qwen 3.5 not
arXiv:2512.04238v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly integrated into clinical workflows. However, existing benchmarks primarily assess performance on comm
arXiv:2310.11714v5 Announce Type: replace Abstract: Ranking generative models based on the fidelity and diversity of their outputs is required to identify the best generator in a group of candidate ge
arXiv:2606.26257v1 Announce Type: new Abstract: How much of my data was used to train a machine learning model? Dataset Usage Inference (DUI) aims to answer this by estimating what fraction of a datas
arXiv:2606.26530v1 Announce Type: cross Abstract: The Abstraction and Reasoning Corpus (ARC;~itealp{chollet2019measure}) contains tasks that require summarizing patterns from limited grid samples and
Great to see the new GPT-5.6 models finally announced. Sad to see this new release strategy where only a select few get access initially. Not a win for our industry IMO. Open-source AI must win! Intro
arXiv:2505.23527v4 Announce Type: replace Abstract: Modern reinforcement learning (RL) algorithms have found success by using powerful probabilistic models, such as transformers, energy-based models,
arXiv:2606.27373v1 Announce Type: new Abstract: Recently, self-evolving large multimodal models (LMMs) have received attention for improving visual reasoning in a purely unsupervised setting. However,
arXiv:2606.26783v1 Announce Type: cross Abstract: Fang et al. (2025) introduced a null-space constrained projection, named AlphaEdit, for locate-then-edit knowledge editing methods, theoretically guar
arXiv:2606.27286v1 Announce Type: new Abstract: Mechanistic epidemiological models are widely used to support infectious disease forecasting and public-health decision making. Bayesian calibration of
arXiv:2606.26987v1 Announce Type: cross Abstract: Recent work identified emotion vectors in Claude Sonnet 4.5, which are internal representations that encode emotion concepts, causally influence behav
arXiv:2510.11047v2 Announce Type: replace Abstract: Laryngeal cancer imaging research lacks standardised public datasets to enable reproducible deep learning (DL) model development. We present Larynge
arXiv:2606.25548v1 Announce Type: new Abstract: Image generative models are trained on massive, largely uncurated internet-scale datasets that contain undesirable visual concepts. Efficiently removing
arXiv:2606.26036v1 Announce Type: new Abstract: Training-time data poisoning during fine-tuning poses a significant threat to large language models (LLMs) deployed for abstractive text summarization,
arXiv:2606.26041v1 Announce Type: cross Abstract: Vision-language models (VLMs) have achieved strong performance on OCR-based benchmarks and increasingly focused on text-rich understanding, but their
arXiv:2604.05738v2 Announce Type: replace Abstract: Medical Vision-Language Models (Med-VLMs) have achieved expert-level proficiency in interpreting diagnostic imaging. However, current models are pre
arXiv:2606.25657v1 Announce Type: new Abstract: Sparse Autoencoders (SAEs) have shown promise for analyzing language models, but applying them to vision-language models (VLMs) often yields representat
arXiv:2606.23843v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are typically pre-trained on large-scale image-text datasets to capture semantic correspondences between visual content an
arXiv:2606.24575v1 Announce Type: new Abstract: Modern finance relies heavily on complex machine learning models to find patterns in the stock market. However, as these AI models get more complicated,
arXiv:2606.21414v1 Announce Type: cross Abstract: The ability to synthesize realistic X-ray images has catalyzed the development of AI models for X-ray image-guided procedures, which otherwise suffer
arXiv:2606.20246v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models pre-trained on massive video-robot datasets have revolutionized robotic manipulation, yet their multi-billion pa
arXiv:2411.05985v3 Announce Type: replace-cross Abstract: This paper examines potential biases and inconsistencies in the emotions evoked by images produced by generative artificial intelligence (AI)
arXiv:2606.22597v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to read maps for logistics, delivery, and accessible navigation, where the output is an actionable d
arXiv:2606.20959v1 Announce Type: new Abstract: Large language models can store both outdated facts and newer superseding facts in their parameters, but standard prompting may still elicit the outdate
arXiv:2605.24354v2 Announce Type: replace Abstract: Recently, world models have made significant progress in enhancing end-to-end driving systems through both future situation forecasting and improved
arXiv:2606.23670v1 Announce Type: new Abstract: Modern language models, including transformer, recurrent, and memory-based variants, share a common chassis: a stack of identical layers in which parame
arXiv:2606.20781v1 Announce Type: cross Abstract: World Action Models (WAMs) are embodied predictive-action models that make a forecast of the future available to action. Recent WAMs repurpose large v
GLM-5.2 leads open weights models and sits at #3 overall on GDPval-AA, a real-world agentic work benchmark GLM-5.2 from @Zai_org scores 1524 Elo on GDPval-AA, which measures performance on real-world,
arXiv:2606.11643v1 Announce Type: new Abstract: Large language models often remain sensitive to answer format: a question solved correctly in one form may fail in another semantically equivalent form.
arXiv:2606.11409v1 Announce Type: cross Abstract: Adversarial robustness evaluations of large language models (LLMs) typically report attack success rate (ASR) under fixed query budgets, implicitly tr
arXiv:2603.05573v2 Announce Type: replace Abstract: Scalable sequence models, such as Transformer variants and structured state-space models, often trade expressivity power for sequence-level parallel
arXiv:2512.06343v3 Announce Type: replace-cross Abstract: Reward models are central to Large Language Model (LLM) alignment within the framework of RLHF. The standard objective used in reward modeling
arXiv:2606.08415v1 Announce Type: cross Abstract: While recent text-guided video editing models excel at elementary tasks (e.g., style transfer, object insertion), real-world user requests are highly
arXiv:2606.05781v2 Announce Type: replace Abstract: Deploying frontier large language models (LLMs) for domain-specific structured evaluation tasks incurs prohibitive latency, cost, and data-privacy o
arXiv:2605.05138v2 Announce Type: replace Abstract: We evaluate an initial coding-agent system for ARC-AGI-3 in which the agent maintains an executable Python world model, verifies it against previous
arXiv:2509.24762v3 Announce Type: replace Abstract: Modeling event sequences of multiple event types with marked temporal point processes (MTPPs) provides a principled way to uncover governing dynamic
arXiv:2606.07996v1 Announce Type: cross Abstract: Pretraining is fundamental to the development of Large Language Models (LLMs), yet the opacity of pretraining data complicates model analysis and rais
arXiv:2606.07641v1 Announce Type: new Abstract: Can vision-language models predict what a 180{eg} rotation would reveal from the original image alone? We study this ability through Rotated-Outcome Pre
arXiv:2603.19183v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have emerged as a promising approach for general-purpose robot manipulation. However, little research has mechan
arXiv:2602.18020v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models leverage pretrained Vision-Language Models (VLMs) as backbones to map images and instructions to actions, demons
arXiv:2606.08775v1 Announce Type: cross Abstract: Visual world models have shown great potential in learning complex system dynamics. Recent advancements leverage these models as transition functions
arXiv:2606.06696v1 Announce Type: cross Abstract: Vision and language models (VLMs) hold immense promise to transform biomedical imaging workflows, from detecting lesions in chest X-rays to profiling
arXiv:2606.06853v1 Announce Type: cross Abstract: The new era has witnessed a remarkable capability to extend Vision-Language Models (VLMs) for tackling tasks of video understanding. While current VLM
arXiv:2602.07025v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) exhibit puzzling failures in multi-object visual tasks, such as hallucinating non-existent elements or failing t
arXiv:2606.05861v1 Announce Type: cross Abstract: The rapid development of large language models(LLMs) has led to remarkable advances in natural language processing. However, the increasing scale of t
arXiv:2606.05434v1 Announce Type: cross Abstract: Group Relative Policy Optimisation (GRPO) has emerged as an effective reinforcement-learning algorithm for aligning language models on reasoning tasks