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TRAJGANR: Trajectory-Centric Urban Multimodal Learning via Geospatially Aligned Neural Representations

arXiv:2605.06990v1 Announce Type: new Abstract: Multimodal self-supervised learning (MSSL) has emerged as a key paradigm for pretraining geospatial foundation models. However, existing geospatial MSSL

safetyarxiv-cs-cv

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Transfer Learning Across Fast- and Full-Simulation Domains in High-Energy Physics

DGX agent

arXiv:2605.07471v1 Announce Type: new Abstract: Machine-learning models in high-energy physics are often trained on simulated data, where fully simulated samples are computationally expensive while fa

tutorialsarxiv-cs-lg

11 May 2026

Transformer-Based Wildlife Species Classification from Daily Movement Trajectories

arXiv:2605.06726v1 Announce Type: new Abstract: Inferring the identity of wildlife species from daily movement data alone is a challenging task. We train sequence models on large-scale, 7-species GPS

TRAS: An Interactive Software for Tracing Tree Ring Cross Sections

arXiv:2605.08025v1 Announce Type: new Abstract: Tree ring marking remains a key step in dendrometry and dendrochronology, but it is often performed manually, making the process time-consuming, subject

TraXion: Rethinking Pre-training Frameworks for Mobility and Beyond

arXiv:2605.06906v1 Announce Type: new Abstract: Human mobility differs from text and from generic time series in three structural ways: visits are tuple-valued events whose meaning depends on the join

applicationsarxiv-cs-lg

Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders

arXiv:2605.07922v1 Announce Type: new Abstract: Learning hierarchical features in Sparse Autoencoders (SAEs) is essential for capturing the structured nature of real-world data and mitigating issues l

tutorialsarxiv-cs-lg

TriDE: Triangle-Consistent Translation Directions for Global Camera Pose Estimation

arXiv:2605.06889v1 Announce Type: new Abstract: Pairwise translation directions are a key input to camera location estimation in global structure-from-motion. Existing estimators usually process each

local-aiarxiv-cs-cv

TriP: A Triangle Puzzle Approach to Robust Translation Averaging

arXiv:2605.07143v1 Announce Type: new Abstract: Translation averaging aims to recover camera locations from pairwise relative translation directions and is a fundamental component of global Structure-

Tripo 3.1 is now supported in ComfyUI via Partner Nodes. Generate high-detail 3D assets directly in your existing ComfyUI workflows, with de…

Tripo 3.1 is now supported in ComfyUI via Partner Nodes. Generate high-detail 3D assets directly in your existing ComfyUI workflows, with denser geometry and cleaner surfaces for close-up production.

applicationscomfyui--x

TRUST: Test-Time Refinement using Uncertainty-Guided SSM Traverses

arXiv:2509.22813v2 Announce Type: replace Abstract: State Space Models (SSMs) have emerged as efficient alternatives to Vision Transformers (ViTs), with VMamba standing out as a pioneering architectur

tutorialsarxiv-cs-cv

Try ComfyHub: http://comfy.org/workflows

ComfyHub is a platform for sharing and discovering ComfyUI workflows, accessible at comfy.org/workflows. It allows users to browse, use, and distribute node-based AI image generation workflows created

local-aicomfyui--x

Tools

Try Parallel Agents today at http://Replit.com

Replit announced the availability of Parallel Agents, a feature that allows developers to run multiple AI agents simultaneously on their platform. This capability enables more efficient and complex ta

toolsreplit--x

Try the split stack workflow: https://links.comfy.org/42GV5JZ

The split stack workflow is a ComfyUI technique that likely demonstrates how to efficiently organize and manage node stacks by separating different processing branches or operations into distinct work

local-aicomfyui--x

Agents

@trycua https://hermes-agent.nousresearch.com/docs/user-guide/features/computer-use

This documentation page covers the computer-use feature in Hermes Agent, detailing how the AI system can interact with computer interfaces and perform automated tasks through screen interaction and in

agentsnous-research--x

TSRBench: A Comprehensive Multi-task Multi-modal Time Series Reasoning Benchmark for Generalist Models

arXiv:2601.18744v2 Announce Type: replace Abstract: Time series are ubiquitous in real-world scenarios and crucial for applications ranging from energy management to traffic control. Consequently, the

TTF: Temporal Token Fusion for Efficient Video-Language Model

arXiv:2605.07355v1 Announce Type: cross Abstract: Video-language models (VLMs) face rapid inference costs as visual token counts scale with video length. For example, 32 frames at 448{imes}448 resolut

researcharxiv-cs-ai

TUANDROMD-X: Advanced Entropy and Visual Analytics Dataset for Enhanced Malware Detection and Classification

arXiv:2605.06718v1 Announce Type: cross Abstract: Malware and malware-based attacks are becoming more prevalent and complex. Attackers regularly come up with new techniques that have the ability to ev

TurboQuant has drawn a lot of attention recently, but the accompanying evals didn't tell the full story. So we ran what I believe is the fir…

tutorialsjeremy-howard--x

TurboQuant has drawn a lot of attention recently, but the accompanying evals didn't tell the full story. So we ran what I believe is the first comprehensive study of TurboQuant: where it helps, where

Tyche: One Step Flow for Efficient Probabilistic Weather Forecasting

arXiv:2605.06916v1 Announce Type: new Abstract: Probabilistic weather forecasting requires not only accurate trajectories, but calibrated distributions over plausible atmospheric futures. Recent data-

UFT: Unifying Fine-Tuning of SFT and RLHF/DPO/UNA through a Generalized Implicit Reward Function

arXiv:2410.21438v3 Announce Type: replace Abstract: By pretraining on trillions of tokens, an LLM gains the capability of text generation. However, to enhance its utility and reduce potential harm, SF

safetyarxiv-cs-cl

UNA: A Unified Supervised Framework for Efficient LLM Alignment Across Feedback Types

arXiv:2408.15339v4 Announce Type: replace-cross Abstract: RL alignment methods, including RLHF and DPO, are primarily based on pairwise preference data. Although scalar or score-based feedback has bee

safetyarxiv-cs-cl

Uncertainty-Aware Structured Data Extraction from Full CMR Reports via Distilled LLMs

arXiv:2605.08045v1 Announce Type: new Abstract: Converting free-text cardiac magnetic resonance (CMR) reports into auditable structured data remains a bottleneck for cohort assembly, longitudinal cura

researcharxiv-cs-cl

Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods

arXiv:2605.07987v1 Announce Type: cross Abstract: Atlas-based approaches allow high-quality, patient-specific shape reconstructions of cardiac anatomy from sparse and/or noisy data such as point cloud

Uncertainty Quantification for Prior-Data Fitted Networks using Martingale Posteriors

arXiv:2505.11325v4 Announce Type: replace-cross Abstract: Prior-data fitted networks (PFNs) have emerged as promising foundation models for prediction from tabular datasets, achieving state-of-the-art

applicationsarxiv-cs-ai

UNCOM: Zero-shot Context-Aware Command Understanding for Tabletop Scenarios

arXiv:2410.06355v3 Announce Type: replace-cross Abstract: This paper presents UNCOM, a novel hybrid framework for interpreting natural human commands in tabletop scenarios. The system integrates multi

Uncovering and Shaping the Latent Representation of 3D Scene Topology in Vision-Language Models

arXiv:2605.07148v1 Announce Type: new Abstract: Decades of cognitive science establish that humans navigate environments by forming cognitive maps, defined as allocentric and topology-preserving repre

applicationsarxiv-cs-cv

Uncovering Hidden Systematics in Neural Network Models for High Energy Physics

arXiv:2605.07470v1 Announce Type: new Abstract: Neural networks (NNs) are inherently multidimensional classifiers that learn complex, non-linear relationships among input observables. While their flex

tutorialsarxiv-cs-lg

Understanding Performance Collapse in Layer-Pruned Large Language Models via Decision Representation Transitions

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

researcharxiv-cs-ai

Understanding Robustness of Model Editing in Code LLMs

model-releasesarxiv-cs-lg

arXiv:2511.03182v2 Announce Type: replace-cross Abstract: Large language models (LLMs) for code are increasingly used in software development, but they remain static after pretraining while APIs and s

Uneven Evolution of Cognition Across Generations of Generative AI Models

arXiv:2605.06815v1 Announce Type: new Abstract: The pursuit of artificial general intelligence necessitates robust methods for evaluating the cognitive capabilities of models beyond narrow task perfor

UniD-Shift: Towards Unified Semantic Segmentation via Interpretable Share-Private Multimodal Decomposition

arXiv:2605.07356v1 Announce Type: new Abstract: Semantic segmentation of large-scale 3D point clouds is crucial for applications such as autonomous driving and urban digital twins. However, the sparse

safetyarxiv-cs-cv

UniISP: A Unified ISP Framework for Both Human and Machine Vision

arXiv:2605.07359v1 Announce Type: new Abstract: Compared to RGB images, raw sensor data provides a richer representation of information, which is crucial for accurate recognition, particularly under c

UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection

arXiv:2605.07146v1 Announce Type: new Abstract: Underwater salient object detection (USOD) plays a vital role in marine vision tasks but remains fundamentally challenging due to severe visual degradat

tutorialsarxiv-cs-cv

Unlocking High-Fidelity Molecular Generation from Mass Spectra via Dual-Stream Line Graph Diffusion

arXiv:2605.07048v1 Announce Type: cross Abstract: De novo molecular generation from tandem mass spectra is a challenging inverse problem whose core difficulty lies in the circular dependency between a

local-aiarxiv-cs-ai

Industry

Unlocking the Archives: Turning Unstructured Documents into a Searchable Database for Groundwater Discovery

This article describes a project using Databricks technology to convert unstructured archival documents into a searchable database to facilitate groundwater research and discovery. The work likely dem

industrydatabricks

Unsolvability Ceiling in Multi-LLM Routing: An Empirical Study of Evaluation Artifacts

arXiv:2605.07395v1 Announce Type: cross Abstract: Efficient routing across multiple LLMs enables cost-quality tradeoffs by directing queries to the cheapest capable model. Prior work attributes routin

Upper Generalization Bounds for Neural Oscillators

arXiv:2603.09742v2 Announce Type: replace Abstract: Neural oscillators that originate from second-order ordinary differential equations (ODEs) have shown competitive performance in learning mappings b

User eXperience Perception Insights Dataset (UXPID): Synthetic User Feedback from Public Industrial Forums

arXiv:2509.11777v2 Announce Type: replace Abstract: Customer feedback in industrial forums offers rich but underexplored insights into real-world product experience. Yet systematic analysis remains ch

applicationsarxiv-cs-cl

Using LLM in the shebang line of a script

model-releasessimon-willison

TIL: Using LLM in the shebang line of a script Kim_Bruning on Hacker News: But seriously, you can put a shebang on an english text file now (if you're sufficiently brave) [...] This inspired me to loo

Utility-Preserving De-Identification for Math Tutoring: Investigating Numeric Ambiguity in the MathEd-PII Benchmark Dataset

model-releasesarxiv-cs-cl

arXiv:2602.16571v2 Announce Type: replace Abstract: Large-scale sharing of dialogue data is key to advancing the science of teaching and learning, yet rigorous de-identification remains a major barrie

v0.23.3: mlx: fix macOS 26 target leakage in v3 metallib (#16053)

This release fixes a macOS 26 target leakage issue in the v3 metallib component for the MLX framework. The fix addresses a problem where Metal library compilation was incorrectly targeting macOS 26, w

local-aiollama-releases

v0.23.3-rc1: mlx: fix macOS 26 target leakage in v3 metallib (#16053)

This release candidate addresses a macOS 26 target leakage issue in the v3 metallib component of Ollama, a machine learning framework. The fix prevents incorrect macOS version targeting that was affec

local-aiollama-releases

v0.30.0-rc13

The search did not return specific information about v0.30.0-rc13. Based on the version designation and source, this is a release candidate for Ollama v0.30.0. As a pre-release version, it likely cont

local-aiollama-releases

v0.30.0-rc14: Merge remote-tracking branch 'upstream/main' into llama-runner-phase-0

model-releasesollama-releases

This is a release candidate version of Ollama that merges updates from the main development branch into the llama-runner-phase-0 branch, likely incorporating bug fixes and features in preparation for

Vaporizer: Breaking Watermarking Schemes for Large Language Model Outputs

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

applicationsarxiv-cs-ai

Variable Aerodynamic Damping via Co-Contraction: A Dynamic Isomorphism with Variable Stiffness Actuators

arXiv:2605.07292v1 Announce Type: new Abstract: We prove that aerodynamic co-contraction in a redundant dual-rotor actuator can tune a passive, trim-defined aero-mechanical damping while keeping the c

local-aiarxiv-cs-ro

Agents

VDCook:DIY video data cook your MLLMs

arXiv:2603.05539v2 Announce Type: replace-cross Abstract: We introduce VDCook: a self-evolving video data operating system, a configurable video data construction platform for researchers and vertical

agentsarxiv-cs-ai

VDEGaussian: Video Diffusion Enhanced 4D Gaussian Splatting for Dynamic Urban Scenes Modeling

arXiv:2508.02129v2 Announce Type: replace Abstract: Dynamic urban scene modeling is a rapidly evolving area with broad applications. While current approaches leveraging neural radiance fields or Gauss

safetyarxiv-cs-cv