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arXiv:2605.13530v1 Announce Type: cross Abstract: Surgical scene understanding is a cornerstone of computer-assisted intervention. While recent advances, particularly in surgical image segmentation, h

applicationsarxiv-cs-ai

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Tracing Persona Vectors Through LLM Pretraining

DGX agent

arXiv:2605.13329v1 Announce Type: cross Abstract: How large language models internally represent high-level behaviors is a core interpretability question with direct relevance to AI safety: it determi

safetyarxiv-cs-ai

14 May 2026

TrackCraft3R: Repurposing Video Diffusion Transformers for Dense 3D Tracking

arXiv:2605.12587v1 Announce Type: new Abstract: Dense 3D tracking from monocular video is fundamental to dynamic scene understanding. While recent 3D foundation models provide reliable per-frame geome

safetyarxiv-cs-cv

Training a diffusion LLM from scratch is challenging, and diffusion inference advantages don't show up at training. So we convert instead. B…

Training a diffusion LLM from scratch is challenging, and diffusion inference advantages don't show up at training. So we convert instead. Building on the TiDAR recipe, we take ZAYA1-8B-base and run d

industryemad-mostaque--x

Training Large Language Models to Predict Clinical Events

arXiv:2605.12817v1 Announce Type: cross Abstract: Longitudinal clinical notes contain rich evidence of how patients evolve over time, but converting this signal into training supervision for clinical

Training LLMs with Reinforcement Learning for Intent-Aware Personalized Question Answering

arXiv:2605.12645v1 Announce Type: cross Abstract: Effective personalized question answering (PQA) in language models requires grounding responses in the user's underlying intent, where intent refers t

Training Long-Context Vision-Language Models Effectively with Generalization Beyond 128K Context

arXiv:2605.13831v1 Announce Type: new Abstract: Long-context modeling is becoming a core capability of modern large vision-language models (LVLMs), enabling sustained context management across long-do

agentsarxiv-cs-cv

Trajectory-Level Data Augmentation for Offline Reinforcement Learning

arXiv:2605.13401v1 Announce Type: new Abstract: We propose a data augmentation method for offline reinforcement learning, motivated by active positioning problems. Particularly, our approach enables t

safetyarxiv-cs-lg

TRIAGE: Evaluating Prospective Metacognitive Control in LLMs under Resource Constraints

arXiv:2605.13414v1 Announce Type: new Abstract: Deploying language models as autonomous agents requires more than per-task accuracy: when an agent faces a queue of problems under a finite token budget

agentsarxiv-cs-ai

Tools

Try Rime Mist v3 in voice finder directly: https://findtherightvoice.com/rime-labs--rime-mist-v3 Model pages: http://www.together.ai/models/…

Try Rime Mist v3 in voice finder directly: https://findtherightvoice.com/rime-labs--rime-mist-v3 Model pages: http://www.together.ai/models/rime-mist-v3 http://www.together.ai/models/rime-mist-v3-omni

toolstogether-ai--x

Try this early Grok Build (anything) beta and let us know what to improve. Much appreciated!

Try this early Grok Build (anything) beta and let us know what to improve. Much appreciated! An early beta of Grok Build, an agentic CLI for coding, building apps, and automating workflows is now avai

agentselon-musk--x

TurboGR: An Accelerated Training System for Large-Scale Generative Recommendation

arXiv:2605.13433v1 Announce Type: cross Abstract: Generative recommendation (GR) has emerged as a promising paradigm that replaces fragmented, scenario-specific architectures with unified Transformer-

Tutorials

Twincher: Bijective Representation Learning for Robust Inversion of Continuous Systems

arXiv:2605.13470v1 Announce Type: new Abstract: Recent advances in AI have been primarily driven by large-scale neural architectures that excel at function approximation, rather than by tailored induc

tutorialsarxiv-cs-lg

two creative hackathon entries that caught my eye: @Liftaris1's herm puts hermes primitives (sessions, skills, cron, agents) as first class …

two creative hackathon entries that caught my eye: @Liftaris1's herm puts hermes primitives (sessions, skills, cron, agents) as first class citizens in the tui. @arm64le's theia turns your sessions hi

researchnous-research--x

U-HNO: A U-shaped Hybrid Neural Operator with Sparse-Point Adaptive Routing for Non-stationary PDE Dynamics

arXiv:2605.12965v1 Announce Type: new Abstract: Solutions to many partial differential equations (PDEs) display coexisting smooth global transport and localized sharp features within a single trajecto

UFO: A Domain-Unification-Free Operator Framework for Generalized Operator Learning

arXiv:2605.12700v1 Announce Type: new Abstract: Neural operators have become an effective framework for learning mappings between function spaces, yet most existing architectures realize operators wit

UK-based Iceotope, which develops data center liquid cooling tech, raised a $26M Series B co-led by Two Seas Capital and Barclays Climate Ventures (Mike Wheatley/SiliconANGLE)

Mike Wheatley / SiliconANGLE: UK-based Iceotope, which develops data center liquid cooling tech, raised a 26M Series B co-led by Two Seas Capital and Barclays Climate Ventures — Liquid cooling system

industrytechmeme

Uncertainty-Aware 3D Position Refinement for Multi-UAV Systems

arXiv:2605.13500v1 Announce Type: new Abstract: Reliable real-time 3D localization is essential for multi-UAV navigation, collision avoidance, and coordinated flight, yet onboard estimates can degrade

agentsarxiv-cs-ro

Uncertainty-Aware Prediction of Lung Tumor Growth from Sparse Longitudinal CT Data via Bayesian Physics-Informed Neural Networks

arXiv:2605.13560v1 Announce Type: new Abstract: This work studies lung tumor growth prediction from sparse and irregular longitudinal computed tomography (CT) observations with measurement variability

Uncertainty-aware Spatial-Frequency Registration and Fusion for Infrared and Visible Images

arXiv:2605.13049v1 Announce Type: new Abstract: Infrared and Visible Image Fusion (IVIF) has shown promise in visual tasks under challenging environments, but fusion under unregistered conditions face

safetyarxiv-cs-cv

Uncertainty-Driven Anomaly Detection for Psychotic Relapse Using Smartwatches: Forecasting and Multi-Task Learning Fusion

arXiv:2605.13816v1 Announce Type: new Abstract: Digital phenotyping enables continuous passive monitoring of behavior and physiology, offering a promising paradigm for early detection of psychotic rel

Tutorials

Uncovering Latent Pathological Signatures in Pulmonary CT via Cross-Window Knowledge Distillation

arXiv:2605.12562v1 Announce Type: cross Abstract: Multi-window CT imaging captures complementary pathological information across anatomical structures of differing densities, yet existing deep learnin

tutorialsarxiv-cs-ai

Uncovering Symmetry Transfer in Large Language Models via Layer-Peeled Optimization

arXiv:2605.12756v1 Announce Type: cross Abstract: Large language models (LLMs) are pretrained by minimizing the cross-entropy loss for next-token prediction. In this paper, we study whether this optim

researcharxiv-cs-ai

Understanding and Accelerating the Training of Masked Diffusion Language Models

arXiv:2605.13026v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) have emerged as a promising alternative to autoregressive models (ARMs) for language modeling. However, MDMs are known

Understanding Catastrophic Forgetting In LoRA via Mean-Field Attention Dynamics

arXiv:2402.15415v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) is the dominant parameter-efficient fine-tuning method due to its favorable compute-performance trade-off, yet it suffers

Understanding Generalization through Decision Pattern Shift

arXiv:2605.13148v1 Announce Type: cross Abstract: Understanding why deep neural networks (DNNs) fail to generalize to unseen samples remains a long-standing challenge. Existing studies mainly examine

researcharxiv-cs-cv

Unified generalization analysis for physics informed neural networks

arXiv:2605.13260v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) and their variational counterparts (VPINNs) are neural networks that incorporate physical laws, making them use

Tutorials

Unify Robot Actions in Camera Frame

arXiv:2511.17001v2 Announce Type: replace Abstract: Cross-embodiment robot learning requires a unified action representation with consistent semantics across robot platforms. Existing representations

tutorialsarxiv-cs-ro

Unifying Entropy Regularization in Optimal Control: From and Back to Classical Objectives via Iterated Soft Policies and Path Integral Solutions

arXiv:2512.06109v3 Announce Type: replace-cross Abstract: This paper develops a unified perspective on several optimal control formulations through the lens of Kullback-Leibler (KL) regularization. We

safetyarxiv-cs-lg

Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions

arXiv:2605.13158v1 Announce Type: new Abstract: Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather

researcharxiv-cs-cv

UniJEPA: Enhancing Robot Policy via Unified Continuous and Discrete Representation Learning

arXiv:2510.10642v3 Announce Type: replace-cross Abstract: Building generalist robot policies that can handle diverse tasks in open-ended environments is a central challenge in robotics. To leverage kn

safetyarxiv-cs-ai

UNIV: Unified Foundation Model for Infrared and Visible Modalities

model-releasesarxiv-cs-cv

arXiv:2509.15642v3 Announce Type: replace Abstract: Joint RGB-infrared perception is essential for achieving robustness under diverse weather and illumination conditions. Although foundation models ex

Universal Representation of Generalized Convex Functions and their Gradients

arXiv:2509.04477v3 Announce Type: replace-cross Abstract: A wide range of optimization problems can often be written in terms of generalized convex functions (GCFs). When this structure is present, it

Tools

Unlocking asynchronicity in continuous batching

This article explains how asynchronous processing can be integrated with continuous batching techniques to improve inference efficiency in machine learning models. Continuous batching allows requests

toolshugging-face

Unlocking Patch-Level Features for CLIP-Based Class-Incremental Learning

model-releasesarxiv-cs-cv

arXiv:2605.13835v1 Announce Type: new Abstract: Class-Incremental Learning (CIL) enables models to continuously integrate new knowledge while mitigating catastrophic forgetting. Driven by the remarkab

Untrained AI agents are easy security targets — they don’t know bad people exist, says KnowBe4 CEO

The dual-threat landscape of enterprise AI security is coming into focus. The same autonomous agents transforming workforce productivity are also expanding the attack surface — and most organizations

agentssiliconangle

Unweighted ranking for value-based decision making with uncertainty

arXiv:2605.13601v1 Announce Type: new Abstract: As intelligent systems are increasingly implemented in our society to make autonomous decisions, their commitment to human values raises serious concern

safetyarxiv-cs-ai

Use this map to find the data centers in your backyard

When Oregon resident Isabelle Reksopuro heard Google was gobbling up public land to fuel its data centers in her home state, she didn't initially know what to believe. 'There's a lot of misinformation

industrythe-verge-ai

Useful Memories Become Faulty When Continuously Updated by LLMs

arXiv:2605.12978v1 Announce Type: new Abstract: Learning from past experience benefits from two complementary forms of memory: episodic traces -- raw trajectories of what happened -- and consolidated

Utility-Oriented Visual Evidence Selection for Multimodal Retrieval-Augmented Generation

arXiv:2605.13277v1 Announce Type: cross Abstract: Visual evidence selection is a critical component of multimodal retrieval-augmented generation (RAG), yet existing methods typically rely on semantic

researcharxiv-cs-ai

v0.24.0

Ollama v0.24.0-rc0 is the latest release candidate version, published on May 14, 2026. Based on recent Ollama releases, this version likely includes features such as built-in web search (OpenClaw) and

v0.24.0-rc1

v0.24.0-rc1 is a pre-release version focusing on improvements to Ollama server caching and the desktop launch experience, including plan-aware model gating and disabling Claude Desktop launch. The rel

v0.30.0-rc16

v0.30.0-rc16 is a pre-release version of Ollama that changes the architecture to directly support llama.cpp instead of building on top of GGML, and allows for compatibility with GGUF file format. MLX

v0.30.0-rc17

v0.30.0-rc17 is a pre-release version of Ollama that changes the architecture to directly support llama.cpp instead of building on top of GGML and allows compatibility with GGUF file format, with MLX

Vaporware or not? Aptera assembles its first five validation models.

Aptera Motors announced that five validation vehicles have been driven off its newly established low-volume validation assembly line in Carlsbad, California in May 2026. Running multiple vehicles thro

industryars-technica

Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction

arXiv:2605.13407v1 Announce Type: new Abstract: Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer int

VectorSmuggle: Steganographic Exfiltration in Embedding Stores and a Cryptographic Provenance Defense

arXiv:2605.13764v1 Announce Type: cross Abstract: Modern retrieval-augmented generation (RAG) systems convert sensitive content into high-dimensional embeddings and store them in vector databases that

VERA-MH Concept Paper

arXiv:2510.15297v4 Announce Type: replace-cross Abstract: We introduce VERA-MH (Validation of Ethical and Responsible AI in Mental Health), an automated evaluation of the safety of AI chatbots used in