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UltraDiff: Differentiable Ray Tracing in Ultrasound for Shape Optimization

arXiv:2610.07941v1 Announce Type: cross Abstract: Physically-based differentiable rendering enables gradient-based optimization of scene parameters by matching rendered images to measurements, but has

Content type

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Unanimously Wrong: Certified Abstention from How Medical LLM Consensus Forms

DGX agent

arXiv:2610.07570v1 Announce Type: new Abstract: In clinical practice, agreement among independent experts is treated as evidence of reliability, and multi-round consensus has become a core mechanism o

agentsarxiv-cs-ai

7 Oct 2026

Unbiased Reward Modeling from Implicit Feedback for LLM Alignment

arXiv:2603.23184v2 Announce Type: replace-cross Abstract: Despite the success of reinforcement learning from human feedback (RLHF), existing reward modeling methods largely rely on explicit feedback,

Uncertainty in Representation Learning on Knowledge Graphs

model-releasesarxiv-cs-lg

arXiv:2610.06974v1 Announce Type: new Abstract: Knowledge graph embedding (KGE) methods represent entities and predicates in continuous vector spaces to infer missing knowledge. Despite strong benchma

Applications

Uncertainty Quantification Is Indispensable for Reliable Connectome-Based Graph Learning: A Narrative Review and Case Study

arXiv:2610.08353v1 Announce Type: new Abstract: While graph neural networks (GNNs) have shown substantial promise in connectome-based diagnostic classification, deterministic models inevitably suppres

applicationsarxiv-cs-lg

Understanding and Mitigating Inference-Time Overreliance Using Agentic Memory

arXiv:2610.07311v1 Announce Type: new Abstract: Agentic memory allows LLM agents to reuse past experience, yet retrieved memories can also distort inference even when they are benign, correctly stored

agentsarxiv-cs-ai

Understanding Errors in LLM-Based Question Answering over Imperfect Tables

arXiv:2610.04687v2 Announce Type: replace Abstract: Answering questions over imperfect tables requires handling errors that can affect the answer. We investigate two challenges for large language mode

agentsarxiv-cs-cl

Understanding Moral Reasoning Trajectories in Large Language Models: Toward Probing-Based Explainability

arXiv:2603.16017v2 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly participate in morally sensitive decision-making, yet how they organize ethical frameworks across re

UniCounting: Instance-Aware Proposal Consolidation for Image-Query-Free Multi-Category Counting

arXiv:2610.08379v1 Announce Type: new Abstract: Visual counting is commonly formulated as counting a single specified target, with a model receiving an image-specific exemplar, text query, or target c

Applications

Uniform Discrete Diffusion Models are Minimax Optimal for Estimating Distributions with Small Effective Support Size

arXiv:2610.07655v1 Announce Type: cross Abstract: Discrete diffusion models have emerged as a practically successful framework for generative modeling on discrete product spaces, yet their statistical

applicationsarxiv-cs-lg

UniPose9D: Universal Category-Agnostic Object Pose Estimation

arXiv:2607.09985v2 Announce Type: replace Abstract: Object pose estimation is a fundamental problem in 3D vision. Although recent state-of-the-art approaches achieve strong performance, generalization

UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation

arXiv:2610.06938v1 Announce Type: new Abstract: Medical image segmentation remains fragmented along two axes: segmentation paradigms and data dimensionality. Existing methods are typically developed s

Universe of Thoughts: A Computational Framework for Creative Reasoning in Large Language Models

arXiv:2511.20471v3 Announce Type: replace Abstract: Recent advances in Large Language Model (LLM) reasoning have improved conventional problem solving, but creative reasoning remains comparatively und

Local Ai

Unlocking Fine-Grained Perception in CLIP via Structurally-Aware Latent Masked Modeling

arXiv:2610.07689v1 Announce Type: new Abstract: Vision-Language Models (VLMs) such as CLIP excel in global semantic alignment but often lack fine-grained perceptual capabilities. This hinders dense pr

local-aiarxiv-cs-cv

UNREAL: Unifying Retrieval and Long-Context with a Single Model

arXiv:2610.08463v1 Announce Type: new Abstract: Long-context inference and Retrieval-Augmented Generation (RAG) handle evidence selection at vastly different scales, from a single long prompt to an en

researcharxiv-cs-cl

Unsupervised Long-Tailed Adaptation of Vision-Language Models

arXiv:2610.07903v1 Announce Type: new Abstract: Adapting vision-language models to downstream tasks has achieved remarkable success by leveraging pseudo-labels generated from unlabeled data. Existing

UP-MOPD: Update Projection in Multi-Teacher On-Policy Distillation

arXiv:2610.08398v1 Announce Type: new Abstract: On-policy distillation from multiple teachers combines expertise from different domains in a single student, but conflicting gradients can hinder this i

Uploaded 5.6 billion TikTok videos metadata on Hugging Face, spanning from 2014 to October 2026 [P]

researchr-machinelearning

Dataset: https://huggingface.co/datasets/datasocial/tiktok-5.6B-videos If you want to explore the data without downloading billions of rows, you can query my ClickHouse database directly. It includes:

Industry

US government, tech giants and Biohub commit $1.8B to AI biology initiative

More than a half-dozen public and private sector organizations are teaming up to develop biology datasets for artificial intelligence models. The consortium, which announced the collaboration today, i

industrysiliconangle

Useful paper on what an agent harness changes when the model stays the same. One takeaway: keep your harness prompt and tools small. In this…

Useful paper on what an agent harness changes when the model stays the same. One takeaway: keep your harness prompt and tools small. In this study, that can cut costs by up to 3x without lowering accu

model-releasesdair-ai--x

UWB Meets Crazyflow: Simulating Degraded Feedback at Scale for Aerial Robotics

arXiv:2610.08225v1 Announce Type: new Abstract: In this work, we introduce Crazyflow, an accurate, differentiable simulator built on JAX. By leveraging jit compilation via XLA, Crazyflow unifies physi

hardwarearxiv-cs-ro

Valid for Free: Homophily-Gated Conformal Prediction for Training-Free Node Classification with Tabular Foundation Models

arXiv:2610.08564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) can classify the nodes of a graph without training on it, by reading node and neighborhood features as table rows next

researcharxiv-cs-lg

Validate AI Factory Changes with Digital Twins and AI Agents

NVIDIA DSX Air creates a node‑based digital twin that models an AI factory’s infrastructure and software interfaces, allowing agentic workflows to interrogate the virtual environment, run configuratio

hardwarenvidia-developer

VALSE: Vertical Adaptive Layer Skipping for Efficient Inference in Large Language Models

arXiv:2610.07606v1 Announce Type: new Abstract: This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (

researcharxiv-cs-ai

Variance-Averse n-Step Offline Reinforcement Learning for Sparse Long-Horizon Environments

arXiv:2610.07899v1 Announce Type: cross Abstract: Generative actors are transforming offline reinforcement learning (RL) by enabling expressive policy classes that model complex action distributions.

Local Ai

Variance-Optimal Off-Policy Evaluation with Conjunct Effect Modeling

arXiv:2610.08677v1 Announce Type: new Abstract: Off-policy evaluation (OPE) for contextual bandit policies becomes challenging when action-level importance weighting incurs excessive variance. Doubly

local-aiarxiv-cs-lg

Variational Physics-Informed Ansatz for Reconstructing Hidden Interaction Networks from Steady States

arXiv:2512.13708v2 Announce Type: replace Abstract: Inferring interaction structure from steady-state observations is a central inverse problem when transient trajectories are unavailable. Here we for

researcharxiv-cs-lg

Vector Map Quality Metrics for Contextual Autonomous Driving Systems

arXiv:2610.08043v1 Announce Type: new Abstract: Ensuring safety in autonomous driving requires continuous map maintenance supported by reliable quality indicators. In this context, it is crucial to id

safetyarxiv-cs-ro

Tutorials

Verdicts Without Annotated Evidence: Rejection Sampling or Label-Only Post-Training for Evidence Recovery?

arXiv:2610.06962v1 Announce Type: cross Abstract: In many review workflows the verdict is the only thing retained. The passages behind it are not marked, because that annotation costs far more than re

tutorialsarxiv-cs-ai

Tutorials

Verified, not generated: expert-verified AI study materials and the distribution of learning gains in a university course

arXiv:2610.07097v1 Announce Type: new Abstract: Experimental studies of generative AI in education mostly report average effects, yet field evidence shows that AI can narrow attainment gaps or widen t

tutorialsarxiv-cs-ai

VeriFine: Scaling Verification for Self-Improvement in Embodied Reasoning

arXiv:2610.08761v1 Announce Type: new Abstract: Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges constr

Verifying Coordination in Parallel Coding Agents: NP-Bench and a Scheduling Planner

arXiv:2610.07261v1 Announce Type: new Abstract: A team of coding agents can look fine agent by agent yet fail as a team: each passes its own tests while the merged result is broken, and single-agent e

VETTA: Coordinating Turn- and Token-Level Credit Assignment for Multi-Turn LLM Agents

arXiv:2610.08402v1 Announce Type: new Abstract: Multi-turn LLM agents often receive sparse task feedback across several interactions, while generating each response token by token. This creates two re

safetyarxiv-cs-lg

ViDAL: A Visual Dynamics-Grounded Action Latent Space for Vision-Language-Action Models

arXiv:2610.08150v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have become a central paradigm for robot policy learning, which predict actions in three forms: raw action chunks, d

safetyarxiv-cs-ro

VietBinoculars: A Zero-Shot Approach for Detecting Vietnamese LLM-Generated Text

arXiv:2509.26189v2 Announce Type: replace Abstract: The rapid proliferation of Large Language Models has intensified the challenge of distinguishing LLM-generated text from human writing in non-Englis

researcharxiv-cs-cl

View Matters: Keyframe-Guided Text-Driven 3D Gaussian Editing

arXiv:2610.08179v1 Announce Type: new Abstract: Text-driven 3D Gaussian editing commonly does not distinguish the editing reliability of rendered views, although different viewpoints provide supervisi

VisionWeave: Weaving Elastic Visual Representations as a Native Capability of MLLMs

arXiv:2610.07987v1 Announce Type: cross Abstract: Multimodal large language models have become the dominant paradigm for visual understanding, but incur substantial costs by encoding inputs into dense

hardwarearxiv-cs-ai

Visual Abstention in Unified Multimodal Models

arXiv:2610.07887v1 Announce Type: cross Abstract: Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand abou

Visual-Invariance-Augmented Feature Optimal Alignment for Transferable Adversarial Attacks against Closed-Source MLLMs

arXiv:2610.06977v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) remain vulnerable to transferable adversarial examples, especially in black-box settings where only open-sour

Visual Orchestration Tax in Agentic VLM Pipelines: Auditing and Certifying Visual Evidence Reuse

arXiv:2610.08170v1 Announce Type: cross Abstract: Agentic VLM pipelines increasingly pass the same static visual evidence through multiple specialist agents and tools. This design creates an orchestra

agentsarxiv-cs-cv

VLA-ACL: Action-Consistent Visual Token Pruning for Efficient Vision-Language-Action Models

arXiv:2610.08133v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models achieve strong robotic manipulation performance but incur high computational costs from processing long token sequ

safetyarxiv-cs-cv

VOMMI: Collecting and Leveraging Portable Demonstrations for Mobile Manipulation

arXiv:2610.08220v1 Announce Type: cross Abstract: Portable mobile-manipulation demonstrations can help alleviate data scarcity for embodied intelligence, but obtaining reliable, low-cost, and robot-fr

Von Neumann Networks

arXiv:2605.05780v2 Announce Type: replace Abstract: In the mid-twentieth century, mathematician and polymath John von Neumann created a computational system on an array of cells as a simple model of t

WareFly-VLA: A Vision-Language-Action Framework for UAV Navigation and Human Tracking in Smart Warehouses

arXiv:2610.08526v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have achieved impressive results in robotic manipulation and ground-mobile navigation, yet language-conditioned co

WASD: Wasserstein-based Knowledge Distillation for Large Language Models

arXiv:2610.07706v1 Announce Type: cross Abstract: Autoregressive large language models (LLMs) have rapidly advanced in capability, but their increasing scale comes with substantial computational and m

WavePrune: One period is often enough for RoPE

arXiv:2610.06963v1 Announce Type: new Abstract: Rotary Position Embedding (RoPE) encodes token positions by rotating each two-dimensional channel of the query and key vectors at a channel-specific fre

hardwarearxiv-cs-cl

Industry

We are entering a new era of discovery now: https://openai.com/index/sharing-ai-progress-in-mathematics/

On Oct 7 2026, Sam Altman announced the initiative “Sharing AI Progress in Mathematics,” highlighting a new era of discovery enabled by AI advances. The announcement appeared on OpenAI’s website (http

industrysam-altman--x

We Need Explanation Cards to Connect Explanation Algorithms to the Real World

arXiv:2606.16786v2 Announce Type: replace Abstract: Algorithmic explanations are intended to help stakeholders understand opaque algorithmic decisions, but in practice, they often fall short. First, t

safetyarxiv-cs-lg