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Visualizing High-Dimensional Graph Embeddings via Informed Multi-View Projections

arXiv:2606.31119v1 Announce Type: new Abstract: Graphs are commonly visualized in 2D, where humans readily interpret spatial relationships, yet such layouts often distort higher-dimensional structure.

researcharxiv-cs-lg

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ViTL: Temporal Logic-Guided Zero-Shot Natural Language Navigation via Vision-Language Models

DGX agent

arXiv:2606.30696v1 Announce Type: cross Abstract: Enabling robots to follow natural language commands to complete zero-shot long-horizon tasks remains challenging. It requires extracting implicit temp

tutorialsarxiv-cs-cl

1 Jul 2026

Von Mises Based Uncertainty Quantification for Closely Spaced Automotive Radar Targets

arXiv:2606.31473v1 Announce Type: cross Abstract: This work investigates uncertainty-aware deep learning approaches for direction of arrival (DOA) estimation in automotive radar, focusing on probabili

VS3R: Robust Full-frame Video Stabilization via Deep 3D Reconstruction

arXiv:2603.05851v2 Announce Type: replace Abstract: Video stabilization aims to mitigate camera shake but faces a fundamental trade-off between geometric robustness and full-frame consistency. While 2

WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching

arXiv:2603.24836v3 Announce Type: replace Abstract: We introduce WAFT-Stereo, a simple and effective warping-based method for stereo matching. WAFT-Stereo demonstrates that cost volumes, a common desi

Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG

arXiv:2606.30989v1 Announce Type: cross Abstract: Warning: This paper contains several toxic and offensive statements. While reasoning generally improves fairness in recent large language models (LLMs

safetyarxiv-cs-ai

Warp RL: Reshaping Base Policy Distributions for Dynamics Adaptation

arXiv:2606.31043v1 Announce Type: new Abstract: Residual reinforcement learning adapts a pretrained robot policy by learning an additive correction to its actions. While effective when adaptation amou

safetyarxiv-cs-lg

WarpHammer: Densifying Scene Warps with 3D Object Priors for Extreme View Synthesis

arXiv:2606.31258v1 Announce Type: new Abstract: Projection-conditioned novel view synthesis (NVS) warps an explicit 3D reconstruction of the input view into the target camera and conditions a generato

WarpI2I: Image Warping for Image-to-Image Translation

arXiv:2606.31018v1 Announce Type: new Abstract: Image-to-image (I2I) translation has achieved strong results in tasks like human relighting and driving scene translation using latent diffusion models

WaterGen: Decoupling Scene and Medium in Underwater Image Generation

arXiv:2606.31147v1 Announce Type: new Abstract: Underwater computer vision tasks, such as detection, restoration, and segmentation, are limited by the scarcity of large-scale and diverse training data

Applications

Wavelet-Optimized Pseudo-3D Accelerated Diffusion Model for Truncated Computed Laminography

arXiv:2606.31318v1 Announce Type: new Abstract: Computed Laminography (CL) is a key technology for the nondestructive testing of large plate-shaped objects. However, field-of-view (FOV) limitations in

applicationsarxiv-cs-cv

What Counts as an Error? Dual-Reference Benchmarking for Atypical ASR

model-releasesarxiv-cs-cl

arXiv:2606.31112v1 Announce Type: new Abstract: ASR systems have been often reported to underperform on atypical speech. An often conflated compounding factor is the existence of two valid transcripti

Agents

What Drives Interactive Improvement from Feedback?

arXiv:2606.30774v1 Announce Type: new Abstract: We study when natural-language feedback produces improvement beyond the gains obtainable from repeated attempts alone. In multi-turn language agent sett

agentsarxiv-cs-ai

What If We Allocate Test-Time Compute Adaptively?

model-releasesarxiv-cs-cl

arXiv:2602.01070v5 Announce Type: replace Abstract: Test-time compute scaling allocates inference computation uniformly, uses fixed sampling strategies, and applies verification only for reranking. In

What Memory Do GUI Agents Really Need? From Passive Records to Active Task-Driving States

model-releasesarxiv-cs-cv

arXiv:2606.31612v1 Announce Type: new Abstract: Mobile GUI agents increasingly face long-horizon tasks that require reading, updating, and reusing task-relevant data across pages and applications. Exi

What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning

arXiv:2606.31106v1 Announce Type: cross Abstract: Large-scale datasets and fast simulators have enabled improvements in driving policies that appear safe and robust, yet strong performance in nominal

safetyarxiv-cs-ai

When Calibration Rankings Reverse: Accuracy-Controlled Evaluation for Fair Comparison of LLMs

arXiv:2606.30814v1 Announce Type: new Abstract: Calibration evaluates whether a model confidence aligns with its empirical accuracy. Existing studies often compare the calibration of different large l

researcharxiv-cs-cl

When Does Learning to Stop Help? A Cost-Aware Study of Early Exits in Reasoning Models

arXiv:2606.30852v1 Announce Type: new Abstract: Reasoning models spend different amounts of useful computation across instances, but it remains unclear when a learned stopping rule improves over simpl

When few labeled target data suffice: a theory of semi-supervised domain adaptation via fine-tuning from multiple adaptive starts

arXiv:2507.14661v2 Announce Type: replace-cross Abstract: Semi-supervised domain adaptation (SSDA) seeks to achieve accurate predictions in a target domain with limited labeled target data by exploiti

researcharxiv-cs-lg

When LLMs Read Tables Carelessly: Measuring and Reducing Data Referencing Errors

arXiv:2606.32029v1 Announce Type: cross Abstract: While large language models (LLMs) perform well on table tasks, they still make data referencing errors (DREs), i.e., incorrectly citing or omitting t

Agents

When Regulation Has Memory: Hysteresis and Control Burden in Artificial Agency

arXiv:2606.30975v1 Announce Type: new Abstract: Adaptive agents are usually judged by what they do, but an agent can appear stable while the internal effort required to keep it stable is increasing. T

agentsarxiv-cs-ai

When Reranking Hurts: Uncertainty-Based Gating for Few-Shot Reranking

arXiv:2606.31087v1 Announce Type: cross Abstract: Few-shot selection typically assumes that reranking retrieved examples always improves performance. We challenge this view by identifying that the exp

When Sinks Help or Hurt: Unified Framework for Attention Sink in Large Vision-Language Models

arXiv:2604.03316v2 Announce Type: replace Abstract: Attention sinks are defined as tokens that attract disproportionate attention. While these have been studied in single modality transformers, their

When the Database Fails: Prompting LLM Dialogue Agents for Safe Recovery in Task-Oriented Dialogue

model-releasesarxiv-cs-cl

arXiv:2606.31307v1 Announce Type: new Abstract: Large language models used in task-oriented dialogue often produce fluent but unsafe responses when backend database calls fail, return empty results, o

When to Truncate a Feature Ranking: A Residual-Overlap Stopping Rule for Subset Selection

arXiv:2606.31686v1 Announce Type: cross Abstract: Feature rankings are widely used in supervised feature selection because they are simple, scalable and easy to interpret. Variables are first ranked b

Local Ai

When transformers learn 'impossible' languages, what do they learn?

arXiv:2606.30815v1 Announce Type: cross Abstract: Recent work suggests that transformer language models show a bias towards human languages over unnatural ('impossible') languages argued to be unacqui

local-aiarxiv-cs-ai

Local Ai

Which Tokens Matter? Adaptive Token Selection for RLVR with the Relative Surprisal Index

arXiv:2606.31575v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a powerful tool for propelling Large Language Models (LLMs) beyond imitation-based training towards more robust r

local-aiarxiv-cs-ai

Who Determines the Meaning of an Emotion? Affective Sovereignty as an Epistemic Consequence of Measurement Limits

arXiv:2606.31442v1 Announce Type: new Abstract: Emotion-sensing AI is rapidly becoming embedded in vehicles, home appliances, dialogue agents, and social infrastructure, giving rise to a sphere in whi

Why Do Few-Step Text Latents Fail When Image Latents Work? Non-Commitment at Sharp Categorical Readouts

arXiv:2606.30705v1 Announce Type: cross Abstract: Deterministic few-step generation succeeds on continuous image latents but collapses to incoherent text on continuous text latents, and we show the ca

Why Solve It Twice? Hierarchical Accumulation of Skills for Transfer-Efficient ML Engineering

arXiv:2606.30911v1 Announce Type: new Abstract: ML engineering agents waste compute rediscovering known techniques because every competition is a cold start. We present HASTE, a hierarchical multi-age

WIDER-FAIR: An Annotated Version of the WIDER-FACE Dataset for Fairness Evaluation

model-releasesarxiv-cs-cv

arXiv:2606.31704v1 Announce Type: new Abstract: The deployment of face detection models in real-world applications raises important fairness concerns, as these systems may showcase performance dispari

WildProp: Visual Estimation of Wildlife Body Proportions at Scale

arXiv:2606.31125v1 Announce Type: new Abstract: Population-level morphometric measurements underpin ecological and evolutionary studies but traditionally require controlled imaging or physical specime

Wind and State Estimation on SE(3): Comparative Evaluation of EKF and UKF with Continuous and Discrete Quadrotor Models

arXiv:2606.30804v1 Announce Type: new Abstract: Use of quadrotor UAVs for wind velocity estimation is gaining popularity in recent studies, leveraging their maneuverability, compact size and low cost.

researcharxiv-cs-ro

Wisdom Of The (AI) Crowd: Investigating Artificial Swarm Intelligence In Large Language Models

arXiv:2606.31404v1 Announce Type: new Abstract: Human swarm intelligence demonstrates remarkable collective accuracy but faces scalability constraints in cost, coordination, and time. We investigate w

World-Model Collapse as a Phase Transition

arXiv:2606.31399v1 Announce Type: new Abstract: Water looks unchanged as it warms, then at a critical point it boils. We ask whether long-horizon language agents show an analogous transition in their

Tutorials

World Narrative Model for Highly Controllable Video Generation: A Paradigm Shift from Pixel Sampling to Physical World Orchestration

arXiv:2606.31946v1 Announce Type: new Abstract: The fundamental obstacle to industrial grade video generation is the lack of controllability: existing models treat video as a pixel distribution sampli

tutorialsarxiv-cs-cv

WorldRoamBench: An Open-World Benchmark for Long-Horizon Stability of Interactive World Models

arXiv:2606.31672v1 Announce Type: cross Abstract: Despite rapid progress in interactive world models (IWMs), existing benchmarks evaluate action following only at trajectory level and ignore memory an

Xiaomi-GUI-0 Technical Report

arXiv:2606.31410v1 Announce Type: new Abstract: Graphical user interface (GUI) agents build on vision-language models to complete user tasks end-to-end in real applications through interface actions s

Z-1: Efficient Reinforcement Learning for Vision-Language-Action Models

arXiv:2606.31846v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising framework for robotic manipulation by connecting language instructions, visual observations, and

ZEBRA: Zero-Shot Entropy-Regularized Prompt Learning for Base-to-Novel Generalization in Audio-Language Models

arXiv:2606.31587v1 Announce Type: cross Abstract: Audio-Language Models (ALMs) achieve strong zero-shot performance by aligning audio with textual class descriptions. Although prompt learning improves

Zero-Shot Quantization for Object Detectors using Off-the-Shelf Generative Models

arXiv:2606.31456v1 Announce Type: new Abstract: With an increasing number of Object Detection (OD) models being deployed on edge devices, Zero-Shot Quantization for OD (ZSQ-OD) aims to quantize these

researcharxiv-cs-lg

3D Field of Junctions: A Noise-Robust, Training-Free Structural Prior for Volumetric Inverse Problems

arXiv:2603.02149v2 Announce Type: replace Abstract: Volume denoising is a foundational problem in computational imaging, as many 3D imaging inverse problems face high levels of measurement noise. Insp

3D Scene-Adaptive Trajectory-Controllable Human Image Animation with Camera Movement

model-releasesarxiv-cs-cv

arXiv:2606.30514v1 Announce Type: new Abstract: Human image animation, which aims to generate a video of a reference subject following a provided action sequence, has received increasing research inte

5ting at SemEval-2026 Task 8: Strong End-to-End Multi-Turn RAG via LLM-Based Reranking and Faithfulness Control

arXiv:2606.28737v1 Announce Type: cross Abstract: We introduce 5ting, our system for the SemEval2026 Task 8 (MTRAGEval), which evaluates multi-turn Retrieval Augmented Generation (RAG) systems. Multi

A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantification

model-releasesarxiv-cs-lg

arXiv:2606.28871v1 Announce Type: cross Abstract: Predicting the aerodynamic performance (e.g. lift, drag, and moment coefficients) of an aircraft is challenging -- computational models are biased and

Tutorials

A binarized-domains arc-consistency algorithm for TCSPs: its computational analysis and its use as a filtering procedure in solution search algorithms

arXiv:2002.11508v3 Announce Type: replace Abstract: TCSPs (Temporal Constraint Satisfaction Problems) [Dechter et al. 1991] get rid of unary constraints by binarizing them after having added an 'origi

tutorialsarxiv-cs-ai

A causal modeling perspective on decision theory

arXiv:2606.29911v1 Announce Type: new Abstract: Decision theory provides a formal framework for how agents should make choices under uncertainty, drawing on ideas from philosophy, probability, and cau

safetyarxiv-cs-ai

A Classifier-Agnostic Zero-Shot Adversarial Attack Detection via CLIP

arXiv:2606.30342v1 Announce Type: new Abstract: Adversarial attacks pose a challenge to the reliability of deep learning models, motivating effective detection methods. Existing techniques often rely