Knowledge catalogue
arXiv:2512.08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across se
arXiv:2507.00322v2 Announce Type: replace-cross Abstract: Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanc
arXiv:2606.09311v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing actio
arXiv:2512.20978v2 Announce Type: replace-cross Abstract: Language Model (LM)-based generative modeling has emerged as a promising direction for TSE, offering potential for improved generalization and
Nvidia Cosmos 3 is an open physical AI foundation model built on a mixture-of-transformers architecture that combines vision reasoning, world generation, and action prediction for reasoning, simulatio
arXiv:2606.09813v1 Announce Type: cross Abstract: Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventio
arXiv:2606.08400v1 Announce Type: cross Abstract: Graduate-level research reading report assessment creates a substantial labor burden for educators. While large language models (LLMs) hold great pote
arXiv:2606.09457v1 Announce Type: new Abstract: Embodied policies typically map current observations directly to actions, leaving candidate-action consequences implicit. World models provide predictiv
arXiv:2508.19857v3 Announce Type: replace Abstract: Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution. Though simple la
arXiv:2605.24890v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models commonly adapt pretrained Vision-Language Models (VLMs) to robot control by mapping visual observations and lang
arXiv:2606.08976v1 Announce Type: new Abstract: LLM-based RTL generation and reasoning is a promising direction for hardware design automation. High-quality benchmarks are critical infrastructure for
arXiv:2606.09511v1 Announce Type: new Abstract: Self-supervised data curation provides a pathway to scaling and improving the generalization capabilities of machine learning models. By leveraging self
arXiv:2606.09547v1 Announce Type: new Abstract: Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video
arXiv:2601.01279v3 Announce Type: replace-cross Abstract: When competing sellers delegate pricing to a shared AI model, such as a large language model, correlated recommendations combined with perform
arXiv:2606.07895v1 Announce Type: new Abstract: Discrete Vision-Language-Action (VLA) models typically formulate action generation as next-token prediction over discretized action spaces, conditioning
The fact that Anthropic may take away subscription access to Fable in two weeks is weird & discourages investing in learning about the model. Subscription use is how you figure out what the model is g
arXiv:2312.02873v2 Announce Type: replace-cross Abstract: The process engineering domain widely uses Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (P&IDs) to represent process
arXiv:2606.08633v1 Announce Type: new Abstract: Long-horizon maritime trajectory prediction is important for shipping management, logistics planning, and maritime risk analysis, yet month-level foreca
arXiv:2511.05017v2 Announce Type: replace Abstract: Hallucinations in Large Vision-Language Models (LVLMs) remain a persistent challenge, often stemming from inadequate integration of visual informati
arXiv:2606.08630v1 Announce Type: cross Abstract: Global wind power capacity, especially in China, is booming, with new farms spanning diverse terrains and climates. The industry urgently needs accura
arXiv:2606.09125v1 Announce Type: cross Abstract: Privacy risks in text-only Large Language Models (LLMs) are well studied, particularly their tendency to memorize and leak sensitive information. Howe
arXiv:2606.07687v1 Announce Type: cross Abstract: Video world models are increasingly used to provide predictive visual representations, yet it remains unclear which pretraining signals induce action-
arXiv:2606.06525v1 Announce Type: cross Abstract: Large language models (LLMs) have emerged as powerful foundation models with strong reasoning capabilities across domains. Beyond reactive text genera
arXiv:2606.06565v1 Announce Type: cross Abstract: Modern game engines spend significant compute animating NPCs with learned motion models. This paper proposes AI Level of Detail (AI LOD), a framework
arXiv:2606.06865v1 Announce Type: new Abstract: Large language models (LLMs) have been increasingly explored for graph computation, where tasks require reasoning over structured relationships and algo
arXiv:2606.06534v1 Announce Type: cross Abstract: Longitudinal medical visual question answering (VQA) requires reasoning about anatomical differences between an image of a current time point and an i
arXiv:2509.14380v3 Announce Type: replace Abstract: Multi-Agent Reinforcement Learning (MARL) provides a powerful framework for learning coordination in multi-agent systems. However, applying MARL to
arXiv:2606.07183v1 Announce Type: new Abstract: This work examines the semantic geometry underlying NLP models. We compare supervised vector embeddings, such as CamemBERT, with lexical co-occurrence g
Great to see AI maturing and finally adopting what we've been preaching for a while now: multi-model workloads! It will go even further progressively though with most companies not only using dozens o
arXiv:2606.06727v1 Announce Type: new Abstract: Autonomous cyber-physical systems (CPS) sit at the intersection of Model-Based Systems Engineering (MBSE) and data-driven Machine Learning and Artificia
arXiv:2606.06857v1 Announce Type: new Abstract: A central goal of cognitive neuroscience is to characterize the features that are represented by human language cortex. Artificial language models (LMs)
arXiv:2606.06830v1 Announce Type: cross Abstract: Data-driven pricing is increasingly prevalent in sectors such as airlines, lending, insurance, and retail. By learning demand models from customer fea
arXiv:2606.06760v1 Announce Type: new Abstract: Medical large vision-language models (Med-LVLMs) have recently achieved remarkable progress in vision-language comprehension and medical image segmentat
arXiv:2606.07180v1 Announce Type: new Abstract: The growing demand for transparency in automated decision-making has propelled eXplainable Artificial Intelligence (XAI) to the forefront of machine lea
arXiv:2606.07345v1 Announce Type: new Abstract: Tabular foundation models, exemplified by TabPFN, perform prediction via in-context learning, inferring test labels directly from labeled training examp
arXiv:2606.06902v1 Announce Type: new Abstract: Targeted post-training aims to improve reasoning, math, and code without degrading strengths. Low-rank adapters are efficient but task-global; activatio
arXiv:2606.07435v1 Announce Type: cross Abstract: Visual speech recognition (VSR) models now surpass human lipreaders on benchmarks, but do such gains establish human-like visual speech perception? To
arXiv:2606.07017v1 Announce Type: new Abstract: Foundation model agents are increasingly deployed for real-world decision-making, but suffer from the sim-to-real gap. While robotics and classical cont
With access to the latest generative AI models and high-performance accelerated compute in high global demand, AWS customers need tools to take advantage of model availability and capacity across mult
imo there’s a pretty solid default recipe that everyone should use to optimize a system of Agent = Model + Harness you should “train” both 1. Build v1 agent using a sensible base harness and some task
arXiv:2509.25397v2 Announce Type: replace-cross Abstract: The proliferation of open large language models (LLMs) is fostering a vibrant ecosystem in artificial intelligence (AI). However, the methods
arXiv:2606.05925v1 Announce Type: new Abstract: A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbatio
Running a 70B parameter model in full 16-bit precision requires roughly 140GB of memory , which is beyond most consumer hardware. Professional-tier GPUs like the RTX PRO 6000 with 96GB GDDR7 enable fu
arXiv:2606.06242v1 Announce Type: new Abstract: Institutional documents contain substantial amounts of operational and analytical information embedded within figures and tables. Current approaches for
arXiv:2504.10823v4 Announce Type: replace Abstract: Navigating dilemmas involving conflicting values is challenging even for humans in high-stakes domains, let alone for AI, yet prior work has been li
arXiv:2606.06002v1 Announce Type: new Abstract: Large Vision-Language Models have achieved significant reasoning performance in various tasks.However, there are few studies on text-to-3D indoor scene
Grok model improvement The updated Grok-build model (still the 0.5T one) is much better than before. It’s less lazy, more autonomous, and more accurate. We are still improving it on long-horizon tasks
arXiv:2606.06109v1 Announce Type: new Abstract: Entity alignment (EA) aims to identify equivalent entities across heterogeneous knowledge graphs (KGs) and is a key component of knowledge fusion and cr
arXiv:2606.05833v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) excel at 2D semantic understanding but lack intrinsic 3D awareness, resulting in representations that fail to m
arXiv:2507.00460v3 Announce Type: replace Abstract: Open Large Language Model (LLM) benchmarks, such as HELM and BIG-Bench, provide standardized and transparent evaluation protocols that support compa
arXiv:2606.04436v1 Announce Type: new Abstract: We propose a 3D-thinking-guided co-training framework that enables vision-language-action (VLA) models to perform 3D spatial reasoning implicitly during
A real problem with feeling the acceleration viscerally is that current models are really good and it is hard to feel the vibe difference on most individual tasks with new models, even as AIs continue
arXiv:2606.05106v1 Announce Type: cross Abstract: We investigate whether methods of human mathematics pedagogy can guide the training of language models toward arithmetic reasoning. Building on the GA
arXiv:2606.04618v1 Announce Type: new Abstract: Probabilistic data association (PDA) improves semantic SLAM in perceptually aliased scenes, but existing methods often assume a fixed landmark set, reco
arXiv:2606.04446v1 Announce Type: cross Abstract: Speculative decoding accelerates autoregressive large language model inference by drafting multiple tokens and verifying them in a single target-model
arXiv:2606.04764v1 Announce Type: new Abstract: Whether attention maps from pathology foundation models capture genuine biology remains unknown, yet this question is critical for clinical trust and re
arXiv:2606.04307v1 Announce Type: new Abstract: Bayesian models with finite symmetry - mixture models with exchangeable components, structural identification with closely-spaced modes - define posteri
arXiv:2606.05030v1 Announce Type: new Abstract: Autoregressive chain-of-thought (CoT) reasoning in large language models (LLMs) is fundamentally forward-directed: each step conditions only on prior to
arXiv:2606.04180v1 Announce Type: new Abstract: Vision-language foundation models such as CLIP and SigLIP provide widely used representations for multimodal learning systems. While these models are ty
arXiv:2606.04075v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a dominant post-training paradigm, enabling large language models (LLMs) to learn from rewards. We observe that