Knowledge catalogue
Guinevere Grant / Bloomberg: Norm, which runs an AI law firm that provides AI-powered legal services to clients alongside human lawyers, raised 120M led by Khosla at a 1.2B valuation — Norm Ai has rai
arXiv:2607.03957v1 Announce Type: cross Abstract: Language models can reach the right normative verdict for the wrong reason. We introduce NormWorlds-CF, a solver-verified environment for counterfactu
arXiv:2607.02714v1 Announce Type: cross Abstract: There is no doubt that safety alignment is an essential step in LLM training. However, conceptually it does not distinguish between various domains an
arXiv:2607.02544v1 Announce Type: cross Abstract: DiLoCo-style training reduces communication by letting learner islands train locally before occasional outer synchronization, making it attractive for
arXiv:2607.03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Spea
Now I'm regretting having carefully blasted through 100% of my Fable allotment in time for being cut off at the end of today! (Presumably 'Wed 12:00 PM' means I get it back at noon on Wednesday, not a
arXiv:2606.20408v3 Announce Type: replace-cross Abstract: Large language model (LLM) agents are increasingly proposed as supervisory components for safety-critical systems, yet their robustness under
nvidia ai just handed dgx spark owners a real gift. nemotron labs 3 puzzle 75b a9b nvfp4 is basically built for this box. 75b total, 9.3b active, nvfp4, mamba plus moe, 256k context in the config, and
Open source AI has shown how quickly developers can innovate when models, data and tools are shared. Robotics has the same opportunity, but advancements in physical AI development can still be gated b
NVIDIA Vera is a CPU designed to help AI factories scale agentic AI and reinforcement learning by shortening CPU execution time, increasing task throughput, and enabling smarter, longer-thinking agent
arXiv:2607.04537v1 Announce Type: cross Abstract: Code language models are now trusted collaborators in production workflows for debugging, refactoring, and iterative repair, and every benchmark that
arXiv:2607.02846v1 Announce Type: new Abstract: Large language model (LLM) agents can improve through accumulated experience, but free-form textual memories become difficult to maintain, validate, and
arXiv:2607.03828v1 Announce Type: new Abstract: Learning robot dexterous manipulation from human manipulation videos requires reliably retargeting human intent to executable robot actions while mainta
arXiv:2607.03108v1 Announce Type: cross Abstract: Post-hoc analysis of trained neural network weights often seeks to recover geometric structure directly from the parameters. We show that, for positio
arXiv:2607.03758v1 Announce Type: new Abstract: Adversarial attacks on motion planning are crucial for evaluating and quantifying the intrinsic robustness of robotic manipulation. However, existing ap
arXiv:2510.01764v3 Announce Type: replace Abstract: Reinforcement learning (RL) research requires diverse, challenging environments that are both tractable and scalable. While modern video games may o
arXiv:2509.23722v2 Announce Type: replace-cross Abstract: Pipeline parallelism is widely used to train large language models (LLMs). However, increasing heterogeneity in model architectures exacerbate
arXiv:2603.06577v2 Announce Type: replace Abstract: While recent multimodal large language models (MLLMs) have made impressive strides, they predominantly employ a conventional autoregressive architec
arXiv:2607.03038v1 Announce Type: new Abstract: Omnidirectional depth estimation from multi-fisheye camera rigs is complicated by visibility conflicts: wide baselines cause different cameras to observ
arXiv:2607.03050v1 Announce Type: cross Abstract: Omni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate larg
arXiv:2607.03261v1 Announce Type: new Abstract: Recent large language models (LLMs) have demonstrated remarkable progress in 3D spatial reasoning, spatial grounding, and fine-grained geometric underst
arXiv:2607.04033v1 Announce Type: cross Abstract: Optimizer selection for large-scale model training has become a system-level design decision constrained jointly by compute, memory, tuning budget, an
arXiv:2607.03723v1 Announce Type: cross Abstract: Visual policies learned from human videos, teleoperation, and robot demonstrations offer scalable motion priors, but often fail in contact-rich manipu
arXiv:2509.10650v4 Announce Type: replace-cross Abstract: Effective analysis in neuroscience benefits significantly from robust conceptual frameworks. Traditional metrics of interbrain synchrony in so
arXiv:2607.04431v1 Announce Type: cross Abstract: Quantile regression provides a powerful tool for summarizing the conditional distribution of a real valued random variable (r.v.) of interest Y as a f
arXiv:2607.04262v1 Announce Type: new Abstract: Convolutional Neural Network (CNN) and Vision Transformer (ViT) for image classification exploit a dense grid of pixels containing redundant information
arXiv:2406.04425v2 Announce Type: replace Abstract: A fundamental problem in machine learning is understanding the effect of early stopping on the parameters obtained and the generalization capabiliti
arXiv:2603.19954v2 Announce Type: replace Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been
arXiv:2607.03519v1 Announce Type: new Abstract: We show that projected Adam for online optimization with arbitrary moment decay parameters eta_1,eta_2in[0,1) can have average regret bounded away from
arXiv:2607.02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures. At test time, howeve
arXiv:2607.04332v1 Announce Type: new Abstract: In this paper, we consider the setting where large language models (LLMs) are trained using reinforcement learning (RL) to simultaneously improve reason
arXiv:2607.04339v1 Announce Type: cross Abstract: Large generative models across text-to-text, text-to-image, and image-to-text modalities have been shown to pose significant privacy risks. One fundam
One of our creative technologists @hellorob just ran a full production pipeline without touching a single node - using Comfy MCP + Claude Fable 5. Production pipeline: → Shot pulled from web → scene d
arXiv:2601.09448v3 Announce Type: replace-cross Abstract: Conventional audio equalization is a static process that requires manual and cumbersome adjustments to adapt to changing listening contexts (e
One year since USAID was dismantled, and the bill is coming due. On @TheLastWord last night, Lawrence O'Donnell and I talked about the human cost — and Musk's lie that no one has died. USAID staffers
arXiv:2607.03948v1 Announce Type: new Abstract: We study the online routing problem in large language model serving, where requests arrive sequentially and must be dispatched to parallel decode worker
arXiv:2508.01389v3 Announce Type: replace Abstract: Person retrieval in surveillance videos often depends on attributes described by witnesses or operators. However, the most useful cues in practice a
arXiv:2607.02896v1 Announce Type: cross Abstract: We ask whether interaction is necessary for order-optimal 1-bit mean estimation over nonparametric finite-moment classes. Adaptive threshold-query pro
arXiv:2607.05163v1 Announce Type: cross Abstract: AI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we re
arXiv:2607.03134v1 Announce Type: cross Abstract: Recent research expands beyond binary anti-spoofing with the emergence of Source Tracing, the task of identifying the specific generative origins of s
Open-source robotics just leveled up. 🦾 @HuggingFace's LeRobot 0.6 now includes NVIDIA Isaac GR00T 1.7 and Isaac Teleop, bringing the latest Isaac models and frameworks to the open robotics community.
arXiv:2607.01983v1 Announce Type: cross Abstract: Robust 3D object detection under adverse weather remains a critical hurdle for autonomous driving. Despite progress with LiDAR-4D radar fusion, most m
Wall Street Journal: OpenAI, Anthropic, and other top AI labs are offering startups token credits and special promotions, as the AI labs seek lasting streams of enterprise revenue — Pitched battle for
OpenAI just asked the US government to take a 5% stake in the company. The pitch: give every citizen a share in the profits of AI. The problem: AI has no profits. So the real plan is to give every cit
Axios: OpenAI says GPT-5.6 Sol, along with Terra and Luna, will launch publicly on Thursday; a source says the US Department of Commerce cleared a broad rollout — The U.S. Department of Commerce has g
arXiv:2607.03213v1 Announce Type: cross Abstract: We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind
Kai Nicol-Schwarz / CNBC: OpenRouter: Chinese AI models have drawn 30%+ of token use by US companies each week since February 8, peaking at 46%, up from 11% over the previous 12 months — Chinese-built
arXiv:2511.00602v4 Announce Type: replace Abstract: Recent advances in large language model (LLM) reasoning through reinforcement learning rely on annotated datasets for verifiable rewards, which may
arXiv:2601.07376v2 Announce Type: replace Abstract: We introduce extsc{OpenTinker}, an open infrastructure for training large language model (LLM) agents with many LoRA-backed policies over shared exe
arXiv:2607.04464v1 Announce Type: cross Abstract: World-model evaluation for model-based reinforcement learning typically asks whether the learned model predicts reward and value well, which can leave
arXiv:2607.03200v1 Announce Type: new Abstract: Origin-destination (OD) flow prediction is central to urban analytics, yet deep models trained on raw counts remain vulnerable to distribution shift. Th
arXiv:2607.05346v1 Announce Type: new Abstract: We propose OptiAgent, a multi-agent framework that, given a natural language description of an Operations Research problem, is able to output a solver-r
arXiv:2511.02200v2 Announce Type: replace Abstract: The emergence of multi-agent systems powered by large language models (LLMs) has unlocked new frontiers in complex task-solving, enabling diverse ag
arXiv:2607.04360v1 Announce Type: cross Abstract: Conditional generative models have emerged as powerful tools for sampling from target conditional distributions, driving substantial advances across a
arXiv:2607.02741v1 Announce Type: cross Abstract: This paper develops an Optimality-Informed Neural Network (OINN) approach for the energy-optimal, free-final-time powered descent of a lunar lander fr
arXiv:2607.03704v1 Announce Type: new Abstract: Background: Growing individual case safety report (ICSR) volumes have intensified demand for scalable automated causality assessment. Large Language Mod
arXiv:2607.05240v1 Announce Type: cross Abstract: Computing-in-Memory (CIM) accelerators execute Matrix-Vector Multiplications (MVMs) in memory, making them a compelling solution for Machine Learning
arXiv:2607.03971v1 Announce Type: cross Abstract: Causality has become an increasingly important tool for gaining a deeper understanding of complex systems. Among various causal analysis methods, caus
arXiv:2607.03228v1 Announce Type: new Abstract: LLM-based agents offer new opportunities for automating business process execution beyond the limits of rule-based systems. However, general-purpose LLM
arXiv:2606.19145v2 Announce Type: replace-cross Abstract: Dynamical systems are fundamental to modeling the natural world, yet modeling them involves a persistent trade-off: manually prescribed mechan