Knowledge catalogue
arXiv:2605.08392v1 Announce Type: new Abstract: Practical diffusion sampling is a numerical approximation problem: under a fixed inference budget, one must simulate a reverse-time ODE or SDE using onl
arXiv:2605.10739v1 Announce Type: cross Abstract: We introduce SMART-HC-VQA, a Sentinel-2-based visual question answering dataset derived from the IARPA SMART Heavy Construction dataset, designed for
i like that there are models called bert and ernie, but in all seriousness, this update looks impressive ERNIE 5.1 is here 🚀 ERNIE 5.1 significantly reduces pretraining cost while compressing total pa
In KAME, a fast speech model starts replying instantly, while a backend LLM runs in parallel to inject deep knowledge on the fly. It’s a completely different way to approach conversational AI, making
arXiv:2605.09009v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable in-context learning (ICL) capabilities, yet their potential for sequential decision-making remains
arXiv:2605.10504v1 Announce Type: new Abstract: A causal-decoder block is hierarchical: lower layers build the residual basis that upper layers attend over. We identify a failure mode in GPT pretraini
arXiv:2605.09252v1 Announce Type: new Abstract: Tool-augmented LLM agents tend to call tools indiscriminately, even when the model can answer directly. Each unnecessary call wastes API fees and latenc
arXiv:2605.09654v1 Announce Type: cross Abstract: Sampling from score-based diffusion models incurs bias due to both time discretisation and the approximation of the score function. A common strategy
arXiv:2605.09376v1 Announce Type: new Abstract: Trajectory optimization for autonomous vehicles usually relies on the kinematic bicycle model because of its computational simplicity. However, when the
arXiv:2602.01698v3 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have recently achieved strong mathematical and code reasoning performance through Reinforcement Learning (RL) post-tra
arXiv:2605.08825v1 Announce Type: new Abstract: Event cameras provide microsecond-level temporal resolution, low latency, and high dynamic range, offering potential for perception under fast motion an
arXiv:2605.08145v1 Announce Type: cross Abstract: Current vision language models face hallucination and robustness issues against ambiguous or corrupted modalities. We hypothesize that these issues ca
Talked to a friend at a top AI lab. Their whole team is former journalists, training models on poems, summaries, and creative writing. I use AI every day and can see that it tends to flatten my writin
arXiv:2605.08440v1 Announce Type: cross Abstract: Adversarial purification with diffusion models seeks to project adversarial examples back toward the data manifold, but balancing semantic preservatio
arXiv:2605.08427v1 Announce Type: new Abstract: Self-play red team is an established approach to improving AI safety in which different instances of the same model play attacker and defender roles in
Ethan Mollick discusses a tradeoff in OpenAI's newer realtime voice model, where improved capabilities require developers to revise prompts that were written for the previous version. The post highlig
arXiv:2409.13107v3 Announce Type: replace Abstract: Large language model-based (LLM) agents are emerging as a powerful enabler of robust embodied intelligence due to their capability of planning compl
arXiv:2605.10843v1 Announce Type: cross Abstract: Large language models increasingly mediate decisions that turn on moral judgement, yet a growing body of evidence shows that their implicit preference
arXiv:2601.08321v3 Announce Type: replace Abstract: With the rapid advancement of image generation, visual text editing using natural language instructions has received increasing attention. The main
arXiv:2605.10083v1 Announce Type: new Abstract: Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model t
arXiv:2605.09040v1 Announce Type: new Abstract: Modeling ultra-long user sequences involves a difficult trade-off between efficiency and effectiveness. While current paradigms rely on either item-spec
arXiv:2605.10106v1 Announce Type: cross Abstract: Recent advances in Multi-modal Large Language Models (MLLMs) target 3D spatial intelligence, yet the progress has been largely driven by post-training
arXiv:2510.10606v4 Announce Type: replace Abstract: Post-training Large Vision-and-Language Models (LVLMs) typically involves Supervised Fine-Tuning (SFT) for knowledge injection or Reinforcement Lear
arXiv:2605.08460v1 Announce Type: cross Abstract: Since the official release of ChatGPT in 2022, large language models (LLMs) have rapidly evolved from chatbot-style interfaces into agentic systems th
arXiv:2605.08245v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate,
arXiv:2605.08992v1 Announce Type: new Abstract: Federated learning (FL) is increasingly used to fine-tune foundation models (FMs) on distributed private data. The community largely assumes that large-
arXiv:2605.08037v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) aligns language models using pairwise preference comparisons, offering a simple and effective alternative to Rein
arXiv:2605.06702v1 Announce Type: new Abstract: Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains constrained by a rigid sepa
arXiv:2403.18149v3 Announce Type: replace Abstract: Model-predictive control (MPC) is a state-of-the-art control method for constrained robotic systems, yet deployment on resource-limited hardware rem
arXiv:2605.07040v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for tutoring, feedback generation, and content creation, but their broad pretraining makes them hard to c
This post from Nous Research discusses the integration of computer use capabilities with Hermes Agent models in collaboration with Claude (CUA), enabling AI agents to interact with computer interfaces
arXiv:2605.06905v1 Announce Type: new Abstract: Modern generative modeling is dominated by transport from a noise prior to data. We propose an alternative paradigm in which generation is performed by
arXiv:2605.06865v1 Announce Type: new Abstract: Large language models (LLMs) are pre-trained and post-trained on vast amounts of loosely curated data, raising the possibility that these models may hav
arXiv:2602.00716v4 Announce Type: replace-cross Abstract: Classifier-free guidance (CFG) is the de facto standard for conditional sampling in diffusion models, yet it often reduces sample diversity. U
arXiv:2605.07005v1 Announce Type: cross Abstract: Recent work on provably efficient algorithms for learning with distribution shift has focused on two models: PQ learning (Goldwasser et al. (2020)) an
arXiv:2605.07962v1 Announce Type: new Abstract: Performance evaluation is essential for assessing the quality of machine learning (ML) models and guiding deployment decisions. In federated learning (F
For browser-use AI agents, every task is dozens of model calls in a tight loop. The inference layer isn’t background infrastructure. It’s what the product runs on. @yutori_ai runs Scouts, Delegate, an
arXiv:2602.09457v2 Announce Type: replace-cross Abstract: We study online learning in the random-order model, where the multiset of loss functions is chosen adversarially but revealed in a uniformly r
arXiv:2510.07625v2 Announce Type: replace Abstract: While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear traject
arXiv:2605.07973v1 Announce Type: new Abstract: Text-to-image diffusion models can generate visually stunning images, yet, controlling what appears and how it appears, remains surprisingly difficult,
In modern ML accelerators, FLOPS have absolutely exploded. Often though, the bottleneck is not FLOPS but memory bandwidth. Similarly, model intelligence has exploded, causing the bottleneck to be huma
arXiv:2605.07721v1 Announce Type: cross Abstract: Recurrent LLM architectures have emerged as a promising approach for improving reasoning, as they enable multi-step computation in the embedding space
arXiv:2602.04447v2 Announce Type: replace-cross Abstract: Modern chess language models are dense transformers trained on millions of games played by thousands of high-rated individuals. However, these
arXiv:2602.07026v2 Announce Type: replace-cross Abstract: Despite the success of multimodal contrastive learning in aligning visual and linguistic representations, a persistent geometric anomaly, the
arXiv:2512.12116v3 Announce Type: replace Abstract: Learned time-series models, whether continuous or discrete, are widely used for forecasting the states of dynamical systems but suffer from error ac
arXiv:2605.07931v1 Announce Type: cross Abstract: Vision-language-action (VLA) models increasingly rely on auxiliary world modules to plan over long horizons, yet how such modules should be parameteri
real time model streaming audio/video/text in and out with tool use People talk, listen, watch, think, and collaborate at the same time, in real time. We've designed an AI that works with people the s
arXiv:2605.07800v1 Announce Type: new Abstract: Recent video diffusion models (VDMs) synthesize visually convincing clips, yet still drop entities, mis-bind attributes, and weaken the interactions spe
arXiv:2509.25584v2 Announce Type: replace Abstract: Vision-language models achieve incredible performance across a wide range of tasks, but their large size makes inference costly. Recent work has sho
Washington Post: Sources: the White House's Office of the National Cyber Director and Commerce Department's CAISI are fighting over which agency should lead AI model evaluations — As the White House g
arXiv:2605.07748v1 Announce Type: new Abstract: Diffusion Transformers (DiT) trained with flow matching in a VAE latent space have unified visual generation across images and videos. A natural next st
The inability of AI models to produce creative variation is a huge gap. The fact that they generate similar ideas limits their ability to do science & the same-y writing limits their usefulness in man
Courtney Rozen / Reuters: The US Commerce Department removed from its website details about its May 5 agreement with Google, xAI, and Microsoft to test their AI models — The U.S. Commerce Department r
arXiv:2605.07955v1 Announce Type: cross Abstract: Multiple sclerosis (MS) expresses substantial clinical and radiological heterogeneity, which poses significant challenges for automatic lesion segment
arXiv:2605.07172v1 Announce Type: new Abstract: Alignment of large language models (LLMs) via SFT and RLHF/DPO typically ignores the global geometry of the representation space, relying instead on loc
arXiv:2605.07271v1 Announce Type: cross Abstract: Layer pruning efficiently reduces Large Language Model (LLM) computational costs but often triggers sudden performance collapse. Existing representati
arXiv:2605.07481v1 Announce Type: cross Abstract: In this paper, we investigate the recent state-of-the-art schemes for watermarking large language models (LLMs) outputs. These techniques are claimed
arXiv:2605.06765v1 Announce Type: cross Abstract: Human speech conveys expressiveness beyond linguistic content, including personality, mood, or performance elements, such as a comforting tone or humm
Frontier labs are betting AGI models will be so good you won't ever want to customize them. We think different. Building on a closed platform means renting your intelligence. The landlord sets the ter
This post from Harrison Chase discusses how AI agents have evolved beyond just the underlying model, emphasizing critical infrastructure components including state management, observability, retry mec