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arXiv:2609.32941v1 Announce Type: new Abstract: Inferring quantities of interest (QoI) from data is a central task in Science and Engineering. In such contexts, we often have access to both quantitati

arXiv:2609.34824v1 Announce Type: new Abstract: Under a shared-factor model, the conditional law of the next image patch factors into a posterior over the shared scene factor and a residual kernel giv

arXiv:2609.35341v1 Announce Type: new Abstract: Evaluating semantic similarity between videos is a fundamental challenge in computer vision, essential for tasks ranging from out-of-distribution (OOD)

arXiv:2609.34633v1 Announce Type: new Abstract: Long-term memory supports the self-evolution of LLM agents by retaining experience and skills across tasks and enabling their retrieval, reuse, and revi

arXiv:2609.34579v1 Announce Type: new Abstract: Single-image novel view synthesis remains challenging because the underlying 3D geometry is highly ambiguous. Recent diffusion-based approaches produce

Gentle reminder that Hugging Face was declined help from Anthropic & OpenAI models when they were being hacked by OpenAI models Self-hosted GLM 5.2 helped them protect themselves when the closed labs

arXiv:2609.34398v1 Announce Type: new Abstract: Critical mineral discovery is a positive-only problem: deposits are observed as sparse locations, while unlabeled regions are not reliable negatives, an

arXiv:2609.32510v1 Announce Type: new Abstract: Cloud removal methods are typically specialized to individual datasets and input configurations, limiting reuse across sensors, spectral bands, and obse

arXiv:2609.35527v1 Announce Type: new Abstract: Embedding structured objects into Euclidean spaces has enabled a wide range of successful machine learning applications. Such objects include words, doc

arXiv:2609.34148v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are far more reliable at recognizing what appears in a video than at reasoning about its spatial and temporal properties,

arXiv:2609.33472v1 Announce Type: new Abstract: Decision-focused surrogates can recover downstream decisions without identifying the quotient report. We characterize the equality set of the convex Sma

arXiv:2609.33695v1 Announce Type: new Abstract: Decoding signals over unknown channels with minimal pilot overhead is a critical challenge in next-generation communications. Existing deep learning app

arXiv:2609.33363v1 Announce Type: cross Abstract: Generating differentially private synthetic data with meaningful Wasserstein utility guarantees is challenging in high dimensions. For datasets of siz

arXiv:2609.34722v1 Announce Type: new Abstract: Long-horizon camera-controlled video generation requires recovering previously observed content from an ever-growing visual history. Existing approaches

arXiv:2609.33089v1 Announce Type: new Abstract: The geometry of latent representations governs which components should interact and how information should propagate, making geometry-aware operator des

arXiv:2609.32222v1 Announce Type: new Abstract: Raw 3D point clouds are a core geometric representation. Establishing their ownership is challenging because point sets are irregular, unstructured, and

arXiv:2609.33723v1 Announce Type: new Abstract: Diffusion transformers incur substantial inference cost through repeated model evaluations along a sampling trajectory. We introduce GeoShrink, a traini

arXiv:2609.35734v1 Announce Type: new Abstract: Novel view synthesis from sparse images must reconcile faithful reconstruction of observed regions with plausible completion of unseen content, while ma

arXiv:2609.31731v1 Announce Type: new Abstract: In this paper, we propose GERIS, a game-theoretic framework for instance selection in the data augmentation phase of license plate recognition systems.

On September 29, 2026, Hugging Face co‑founder Clement Delangue announced that the company had been acquired by NVIDIA. The acquisition will let Hugging Face scale its hiring beyond what a small start

arXiv:2609.32788v1 Announce Type: new Abstract: Deep learning has transformed symbolic music generation by borrowing the training paradigms of large language models, with systems such as NotaGen now p

arXiv:2609.34564v1 Announce Type: new Abstract: Post-training quantization (PTQ) efficiently compresses Vision Transformers (ViTs) without retraining, yet suffers severe accuracy degradation at low bi

arXiv:2609.32471v1 Announce Type: new Abstract: Vision-based tactile sensors (VBTS) provide rich contact information for robotic manipulation, but existing designs can be hard to simplify and adapt to

arXiv:2412.08911v4 Announce Type: replace Abstract: Multi-objective learning endeavors to concurrently optimize multiple objectives using a single model, aiming to achieve high and balanced performanc

Good Just arrived at @OpenAI #DevDay, and I’m predicting proactive intelligence will steal the show. We’ve gone from statistics to machine learning to intelligence. Now the next step is AI that doesn’

As threat actors increasingly use AI to accelerate and develop cyberattacks, enterprise defenders need to rely on both AI and a critical defender’s advantage: Business context that only you possess. E

arXiv:2609.34172v1 Announce Type: new Abstract: Active grounding of a frozen diffusion prior requires jointly determining where new measurements should be taken and how they should be used to refine t

OpenAI has released GPT‑6.1 Sol, which becomes available immediately for all Plus, Pro, Business, Enterprise, and Educational plan users within ChatGPT Work and Codex. The announcement was posted on O

My comment on GPT 6.1 Sol: Near-Astra intelligence for a fifth of the price — Hacker News.I'm a bit late with the pelicans because I was live-blogging the keynote: https://simonwillison.net/2026/Sep/2

OpenAI has released GPT‑6.1 Sol, positioning it as a near‑Astra level model that delivers comparable performance at one‑fifth the cost of its predecessors. According to OpenAI, this makes GPT‑6.1 Sol

GPT-6.1 Sol shows major improvements over GPT-6 Sol in our alignment evaluations, bringing it more in line with GPT-6 Astra. It’s more transparent about its limitations and more reliable at respecting

arXiv:2609.32737v1 Announce Type: new Abstract: Existing geospatial vision-language models (Geo-VLMs) typically optimize diverse geospatial tasks through a unified multi-task adaptation paradigm witho

arXiv:2609.32355v1 Announce Type: cross Abstract: Model-internal features can be studied through both their ability to identify a specified concept and their causal effect when manipulated, e.g., thro

arXiv:2609.34174v1 Announce Type: new Abstract: Test-time training (TTT) allows a model to improve its predictions at inference time by updating weights after every observed token. However, sequential

arXiv:2609.35042v1 Announce Type: cross Abstract: Martian weather forecasting is important for future exploration, but atmospheric behaviour on Mars combines spatial, temporal, vertical, and dust-driv

arXiv:2609.35000v1 Announce Type: new Abstract: Robot-aided operations in space stations are essential for reducing the workload of astronauts and improving the efficiency of on-orbit activities. Mult

arXiv:2609.32056v1 Announce Type: new Abstract: Achieving precise control over multiple properties without sacrificing chemical validity remains a central challenge in molecular inverse design. Existi

arXiv:2609.32365v1 Announce Type: new Abstract: Dense associative memories have traditionally focused on storing and retrieving vector-valued patterns. Many modern machine learning problems, however,

arXiv:2508.14859v2 Announce Type: replace Abstract: Temporal graph learning is crucial for dynamic networks where nodes and edges evolve over time and new nodes continuously join the system. Inductive

Graph engineering is the design work: breaking a task into nodes, connecting them with edges, and deciding where code, models, or people control the next step. The Agent Development Kit (ADK)'s Workfl

arXiv:2609.35545v1 Announce Type: new Abstract: Epidemic policy planning often requires coordination between geographical regions, taking into account mobility-driven spillovers and how to make use of

arXiv:2509.23373v3 Announce Type: replace-cross Abstract: We propose Graph Consistency Regularization (GCR), a novel framework that injects relational graph structures, derived from model predictions,

arXiv:2609.35084v1 Announce Type: new Abstract: Group-based reinforcement learning (RL) has advanced large language models (LLMs) and is increasingly extending to agentic tasks, where sparse terminal

arXiv:2609.33561v1 Announce Type: new Abstract: Multimodal graph predictors combine text, images, and relations to classify connected entities. How much of this input is needed to preserve their predi

arXiv:2609.32064v1 Announce Type: new Abstract: This paper presents Grasp2Twist, a bimanual dexterous manipulation system that learns to grasp and twist open jar lids using reinforcement learning. Lea

arXiv:2603.03270v2 Announce Type: replace-cross Abstract: Mobile devices are frequent targets of eCrime threat actors through SMS spearphishing (smishing) links that leverage Domain Generation Algorit