Knowledge catalogue
arXiv:2602.17196v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) incur substantial inference cost due to the processing of hundreds of visual tokens per image. Although tok
arXiv:2609.07853v1 Announce Type: new Abstract: Semantic and goal-oriented communication is increasingly studied for 6G, but generalization beyond seen data remains a key weakness under tight rate bud
arXiv:2603.01875v4 Announce Type: replace-cross Abstract: Knowledge distillation (KD) is widely used to compress and post-train large language models (LLMs), yet many existing frameworks execute teach
arXiv:2510.02532v2 Announce Type: replace-cross Abstract: Deep neural networks excel in high-dimensional problems, outperforming models such as kernel methods, which suffer from the curse of dimension
arXiv:2609.08059v1 Announce Type: cross Abstract: Acidophilic proteins that remain stable and functional under highly acidic conditions, are important for industrial biocatalysis, acid-related bioproc
arXiv:2504.15758v3 Announce Type: replace Abstract: We operate through the lens of ordinary differential equations and control theory to study the concept of observability in the context of neural sta
arXiv:2510.10681v2 Announce Type: replace-cross Abstract: High-quality data is a cornerstone of large language model (LLM) pretraining, yet its growth has not kept pace with the needs of frontier mode
arXiv:2609.06419v1 Announce Type: new Abstract: Medical vision-language models (VLMs) require confidence that reflects both answer correctness and patient-specific visual evidence. Recent GRPO-based m
arXiv:2609.05533v1 Announce Type: new Abstract: Long-horizon manipulation is partially observable: the information needed to choose the next action may appear only in observations from minutes earlier
The Information: Sources: Jeffrey Katzenberg, ex-OpenAI Sora head Bill Peebles, and ex-Dropbox CFO Sujay Jaswa plan to launch a startup to train AI video models for filmmakers — Hollywood mogul Jeffre
arXiv:2609.05916v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong multimodal understanding, but the hundreds to thousands of visual tokens they process impose substan
arXiv:2510.25183v2 Announce Type: replace-cross Abstract: Energy-efficient AI should be evaluated across the full application pipeline, not only by lowest error or shortest training time. We study thi
arXiv:2609.07959v1 Announce Type: cross Abstract: Machine learning models are widely used in clinical applications, social media, law enforcement and critical infrastructure. Verifying whether their o
arXiv:2606.14883v2 Announce Type: replace Abstract: Continual vision-language models are commonly addressed through sequential fine-tuning; however, although this paradigm enables adaptation to new en
arXiv:2505.23043v2 Announce Type: replace-cross Abstract: Unified vision-language models (VLMs) aim to support both visual understanding and generation within a single framework, but it remains unclea
arXiv:2609.04555v1 Announce Type: new Abstract: Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained backbone can provide rich representations
arXiv:2609.04541v1 Announce Type: new Abstract: Digital materials fabricated by multi-material 3D printing are designed as controlled mixtures of stiff and compliant constituents, yielding effective r
arXiv:2609.04778v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer a non-autoregressive alternative for mobile edge agentic artificial intelligence (AI) by refining tokens through
arXiv:2511.03699v2 Announce Type: replace Abstract: We investigate whether Large Language Models (LLMs) exhibit conspiratorial tendencies, whether they display socio-demographic biases in this domain,
arXiv:2609.04272v1 Announce Type: cross Abstract: This study examines the ability of large language models (LLMs) to predict the risk of weather-related forced outages in the distribution grid in a ze
arXiv:2609.04866v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) create opportunities to enrich simulation-based energy policy analysis, particularly by supporting struc
arXiv:2609.04264v1 Announce Type: new Abstract: Latent world models have become increasingly popular as a method to predict and plan in latent space rather than pixel space. Recent architectures, such
arXiv:2609.05266v1 Announce Type: new Abstract: World-action models guide action generation with predicted future observations, but vision-centric predictions miss the local contact cues that decide c
arXiv:2609.05333v1 Announce Type: new Abstract: A transformer language model assigns a single, context-independent vector to a word type at its embedding layer, yet is widely believed to individuate t
arXiv:2609.05388v1 Announce Type: new Abstract: Visual reasoning tasks require a system to jointly perceive visual content and apply formal relational constraints---a combination that neither pure neu
arXiv:2609.04550v1 Announce Type: new Abstract: Video-language benchmarks are usually constructed by the dataset authors without published reliability statistics, leaving the noise floor of the constr
arXiv:2609.04381v1 Announce Type: cross Abstract: Recognizing specific objects onboarded without a labeled training set recurs across manufacturing and service robotics, yet the conventional renderabl
arXiv:2609.04373v1 Announce Type: new Abstract: Large language models (LLMs) are being deployed at scale in consequential real-world systems, from financial markets to content moderation to hiring. We
Today we're releasing data on models accelerating research at OpenAI. Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it w
arXiv:2609.03324v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves large language model reasoning, but its practical scaling is constrained by expensive on-
arXiv:2609.03858v1 Announce Type: new Abstract: Attention mechanisms are central to modern foundation models, yet their training dynamics remain poorly understood, especially when the attention matric
arXiv:2609.03985v1 Announce Type: cross Abstract: Zero-shot vision-language models (VLMs) are increasingly used as training-free species recognizers, but reported accuracy can reflect more than visual
arXiv:2609.04180v1 Announce Type: cross Abstract: Gaps remain in our understanding of how large language models (LLMs) acquire knowledge during pre-training. We posit that auxiliary views, reformulati
arXiv:2609.03972v1 Announce Type: new Abstract: Label removal occurs frequently in classification systems with evolving taxonomies, where categories must be dynamically updated or eliminated. To accom
arXiv:2609.03704v1 Announce Type: new Abstract: This paper presents an integrated approach to modeling human competencies by combining the theoretical foundations of cognitive architectures with princ
arXiv:2609.03927v1 Announce Type: new Abstract: For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and reason about the consequences of those act
arXiv:2609.03824v1 Announce Type: new Abstract: 3D foundation models (3DFMs) excel at predicting camera poses and dense depth from multiple views of a scene, showcasing strong zero-shot generalization
arXiv:2609.04174v1 Announce Type: new Abstract: 3D Foundation Models (3DFMs) such as VGGT have recently pushed the boundaries of 3D vision by predicting rich unified representations with feed-foward t
arXiv:2609.02382v1 Announce Type: cross Abstract: Gaussian graphical models (GGMs) are essential tools for interpretable structure learning. However, in high-dimensional, small-sample regimes, the ava
Exciting day for NVIDIA and @huggingface. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. They allow every developer, startup, university,
arXiv:2609.02029v1 Announce Type: new Abstract: Long-context inference retains a growing key--value (KV) cache during decoding, which consumes substantial GPU memory and can reduce generation throughp
arXiv:2609.02634v1 Announce Type: new Abstract: Vision-Language-Action (VLA) Models are increasingly used in robotics for their ability to ground language and perception into action, yet the internal
Stop typing @Ollama commands the old way. Ollama’s new interactive menu makes it easier to discover and launch models, agents, and coding tools—without memorizing exact tags or digging through shell h
arXiv:2609.02526v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly used to predict the responses of human participants in survey panels. Towards that goal, persona prompting
Without Hugging Face the launch and scaling of stable diffusion & the models that followed would only have been a fraction of what they were. Great and super engaged team who have constantly flown the
arXiv:2509.25050v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a central paradigm for advancing Large Language Models (LLMs), where both pre-training and RL post-traini
arXiv:2606.12708v2 Announce Type: replace-cross Abstract: Despite their linguistic diversity and global significance, African languages remain underrepresented in research and resources to support NLP
arXiv:2609.01289v1 Announce Type: new Abstract: Hyperspectral unmixing is a key task in remote sensing that aims to decompose mixed pixels in hyperspectral images into their constituent material signa
arXiv:2605.05593v2 Announce Type: replace Abstract: Despite the remarkable success of Multimodal Large Language Models (MLLMs) across diverse tasks, the internal mechanisms governing how they encode a
arXiv:2609.00103v1 Announce Type: cross Abstract: Memory is widely viewed as an important unsolved problem for LLMs and VLMs, and current benchmarks typically evaluate it by testing accuracy over long
arXiv:2609.01168v1 Announce Type: new Abstract: Text-to-image (T2I) models remain vulnerable to jailbreak attacks that elicit Not-Safe-For-Work (NSFW) content, despite increasingly being guarded by he
arXiv:2609.00332v1 Announce Type: cross Abstract: Generative models for implied volatility surfaces must produce outputs that satisfy static no-arbitrage constraints. We study these constraints in lat
arXiv:2609.01217v1 Announce Type: new Abstract: Despite their strong performance, large language models remain highly sensitive to prompt formulation. Prior work addresses this through refined data co
arXiv:2504.01547v3 Announce Type: replace Abstract: Supervised deep learning achieves strong performance in biomedical image segmentation but relies on costly pixel-wise annotations, motivating semi-s
arXiv:2609.01004v1 Announce Type: cross Abstract: Despite their strong multimodal understanding ability, multimodal large language models (MLLMs) incur substantial computational overhead when processi
arXiv:2609.00492v1 Announce Type: new Abstract: Both privacy and factual accuracy are paramount in high-stakes domains like healthcare. Concerningly, we uncover and investigate a privacy-hallucination
arXiv:2609.00372v1 Announce Type: cross Abstract: With quantum sensors, simulators and networks emerging, a future of quantum technology may produce quantum states as data---that is, coherently rather
arXiv:2310.04363v3 Announce Type: replace-cross Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This lim
arXiv:2603.00763v2 Announce Type: replace Abstract: Text-to-image diffusion models have achieved unprecedented success but still struggle to produce high-quality results under limited sampling budgets
arXiv:2405.18540v3 Announce Type: replace Abstract: Red-teaming, or identifying prompts that elicit harmful responses, is a critical step in ensuring the safe and responsible deployment of large langu