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Physics-Informed Video Generation via Mixture-of-Experts Latent Alignment

model-releasesarxiv-cs-cv

arXiv:2606.04737v1 Announce Type: new Abstract: Large-scale video generation models have made remarkable progress in semantic consistency and visual quality, producing videos that are increasingly coh

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Research

Pinpoint: Grounded Worldwide Image Geolocation via Cross-Source Retrieval and Reranking

DGX agent

arXiv:2606.04133v1 Announce Type: new Abstract: Image geolocation aims to estimate where a photograph was taken from its visual content. At worldwide scale, this remains challenging because visual evi

researcharxiv-cs-cv

4 Jun 2026

Plan First, Judge Later, Run Better: A DMAIC-Inspired Agentic System for Industrial Anomaly Detection

arXiv:2606.04599v1 Announce Type: new Abstract: Large language model (LLM) agents have shown promise in automating complex data-analysis workflows, but their reliable deployment remains challenging in

safetyarxiv-cs-ai

Plan, Watch, Recover: A Benchmark and Architectures for Proactive Procedural Assistance

arXiv:2606.04970v1 Announce Type: cross Abstract: We envision a proactive multi-modal assistant system which gives users real-time step-by-step guidance on a procedural task, autonomously deciding ext

Platonic Transformers: A Solid Choice For Equivariance

arXiv:2510.03511v3 Announce Type: replace-cross Abstract: While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision. Existing equivariant meth

researcharxiv-cs-ai

Plug-and-Play Diffusion Meets ADMM: Dual-Variable Coupling for Robust Medical Image Reconstruction

arXiv:2602.23214v2 Announce Type: replace Abstract: Plug-and-Play diffusion prior (PnPDP) frameworks have emerged as a powerful paradigm for solving imaging inverse problems by treating pretrained gen

safetyarxiv-cs-cv

POLARIS: Guiding Small Models to Write Long Stories

arXiv:2606.04095v1 Announce Type: cross Abstract: Small open-weight models struggle at long-form creative writing: their generated stories either fall far short of the requested length, or their quali

safetyarxiv-cs-ai

Policy Gradient for Continuous-Time Robust Markov Decision Processes

arXiv:2606.04335v1 Announce Type: new Abstract: The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under w

PoliticsBench: Benchmarking Political Values in Large Language Models with Multi-Turn Roleplay

arXiv:2603.23841v2 Announce Type: replace-cross Abstract: While Large Language Models (LLMs) are increasingly used as primary sources of information, their potential for political bias may impact thei

Position: Deployed Reinforcement Learning should be Continual

arXiv:2606.04029v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has received increasing attention and adoption in real-world use cases. Most of these systems follow a train-then-fix para

agentsarxiv-cs-ai

Potential-Guided Flow Matching for Vision-Language-Action Policy Improvement

arXiv:2606.04968v1 Announce Type: new Abstract: Large vision-language-action (VLA) policies are increasingly trained as conditional generative models over action chunks. Yet deployment produces mixed-

safetyarxiv-cs-ro

Prediction Under Imperfect Compression: A Theory of Approximate MDL

arXiv:2606.04834v1 Announce Type: new Abstract: Minimum Description Length (MDL) formalizes the principle of Occam's razor by optimizing the total description length: L(model)+L(data | model). For seq

Preserving Data Privacy in Learning Causal Structure with Fully Homomorphic Encryption

arXiv:2606.05129v1 Announce Type: cross Abstract: Preserving data privacy is an important topic in structural data management and data mining. However, the issue of privacy leakage in distributed caus

Probing Outcome-Level Resemblance and Mechanism-Level Alignment in LLM Risk Decisions: Evidence from the St. Petersburg Game

arXiv:2606.04978v1 Announce Type: new Abstract: LLMs can appear cautious in risk decision-making tasks, yet cautious-looking outputs do not necessarily indicate alignment with human decision-making me

safetyarxiv-cs-cl

Prompt-Level Distillation: A Non-Parametric Alternative to Model Fine-Tuning for Efficient Reasoning

model-releasesarxiv-cs-cl

arXiv:2602.21103v2 Announce Type: replace Abstract: Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inferen

Proof-Carrying Agent Actions: Model-Agnostic Runtime Governance for Heterogeneous Agent Systems

arXiv:2606.04104v1 Announce Type: cross Abstract: Agent systems execute through runtimes with very different control points: local coding tools, framework SDKs, managed agent platforms, API gateways,

Tutorials

Prospective Dynamic 3D MRI Reconstruction via Latent-Space Motion Tracking from Single Measurement

arXiv:2606.04249v1 Announce Type: new Abstract: Prospective reconstruction is crucial in many clinical applications such as MRI-guided radiotherapy, which demands accurate image reconstruction and fas

tutorialsarxiv-cs-cv

Provably Auditable and Safe LLM Agents from Human-Authored Ontologies

arXiv:2606.04903v1 Announce Type: cross Abstract: We introduce the LLM agent architecture Agentic Redux, intended for use with nontrivial problem domains that require linear auditability. Using the ty

agentsarxiv-cs-ai

Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization

arXiv:2606.04866v1 Announce Type: new Abstract: Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing conce

Pseudospectral Bounds for Transient Amplification in Coupled Gradient Descent

arXiv:2606.04031v1 Announce Type: new Abstract: Coupled gradient descent--where the update of one parameter block depends on another--underlies bilevel optimization, two-time-scale stochastic approxim

PureLight: Learning Complex Luminaires with Light Tracing

arXiv:2606.04319v1 Announce Type: cross Abstract: We propose a neural formulation for estimating the appearance of complex luminaires. We focus on challenging luminaires with complex light transport (

researcharxiv-cs-cv

QO-Bench: Diagnosing Query-Operator-Preserving Retrieval over Typed Event Tuples

arXiv:2606.04646v1 Announce Type: cross Abstract: Many real-world questions over business, legal, and scientific corpora are natural-language versions of database-style queries over records latent in

QPredSGG: Hybrid Quantum Predicate Learning for Long-Tailed Scene Graph Generation

arXiv:2606.04689v1 Announce Type: cross Abstract: Scene Graph Generation (SGG) requires relational reasoning over objects and their interactions, but performance is often limited by severe long-tail p

Quantum entanglement provides a competitive advantage in adversarial games

arXiv:2603.10289v2 Announce Type: replace-cross Abstract: Whether uniquely quantum resources confer advantages in fully classical, competitive environments remains an open question. Competitive zero-s

QuBLAST: A Framework for Quantizing Large Language Models with Block-Level Compression Approach and Activation Scaling Strategy

arXiv:2606.04620v1 Announce Type: cross Abstract: LLMs have become the state-of-the-art algorithms for solving NLP tasks. However, they typically come at huge computational and memory costs, thus maki

Query-based Cross-Modal Projector Bolstering Mamba Multimodal LLM

arXiv:2606.04719v1 Announce Type: new Abstract: The Transformer's quadratic complexity with input length imposes an unsustainable computational load on large language models (LLMs). In contrast, the S

researcharxiv-cs-cl

Local Ai

R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search

arXiv:2606.04823v1 Announce Type: new Abstract: Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, where a system must plan, use tools, and act over extended horizon

local-aiarxiv-cs-ai

R3G: A Reasoning-Retrieval-Reranking Framework for Vision-Centric Answer Generation

arXiv:2602.00104v3 Announce Type: replace-cross Abstract: Vision-centric retrieval for VQA requires retrieving images to supply missing visual cues and integrating them into the reasoning process. How

researcharxiv-cs-ai

Radiomic Feature Selection Using Gradient Loss of Deep Neural Network for Lung Cancer Stage Detection

arXiv:2606.04453v1 Announce Type: new Abstract: Radiomics enables extraction of quantitative imaging biomarkers from medical images and has become an important tool for computer-aided cancer diagnosis

researcharxiv-cs-cv

RAMPART: Registry-based Agentic Memory with Priority-Aware Runtime Transformation

model-releasesarxiv-cs-cl

arXiv:2606.04628v1 Announce Type: new Abstract: RAMPART is a compile-time memory model and pure in-RAM block registry for LLM-based agents. Context assembly is a programmable runtime operation where c

RAVQ-HoloNet: Rate-Adaptive Vector-Quantized Hologram Compression

arXiv:2511.21035v2 Announce Type: replace Abstract: Holography offers significant potential for AR/VR applications. However, its adoption is limited by the high demand for data compression. Existing d

Read the Trace, Steer the Path: Trajectory-Aware Reinforcement Learning for Diffusion Language Models

arXiv:2606.04396v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) generate responses by iteratively unmasking and revising many positions in parallel. This process leaves a rich

researcharxiv-cs-cl

Read What You Hear: Reference-Free Hypotheses Evaluation with Acoustic Discrepancy

arXiv:2606.04680v1 Announce Type: cross Abstract: Automatic speech recognition systems commonly rely on reference transcriptions for evaluation, while reference-free approaches often depend on interna

researcharxiv-cs-cl

Real-Time Automatic License Plate Recognition Using YOLOv8, SORT Tracking, and Temporal Data Interpolation

arXiv:2606.04684v1 Announce Type: cross Abstract: The real-time hardships of video processing seriously limit the usage of Automatic License Plate Recognition (ALPR) with application in dynamic traffi

researcharxiv-cs-ai

Real-World Deployment of a 5G-Connected Edge-Controlled Aerial Robot in Industrial Subterranean Mines

arXiv:2606.04818v1 Announce Type: new Abstract: This article presents the first real-world autonomous flight of a 5G-connected aerial robot controlled by an edge-offloaded controller, and aims to brid

agentsarxiv-cs-ro

Local Ai

Reasoning or Fluency? Dissecting Probabilistic Confidence in Best-of-N Selection

arXiv:2601.13735v2 Announce Type: replace Abstract: Probabilistic confidence metrics are increasingly adopted as proxies for reasoning quality in Best-of-N selection, under the assumption that higher

local-aiarxiv-cs-ai

Reasoning over Boundaries: Enhancing Specification Alignment via Test-time Deliberation

model-releasesarxiv-cs-cl

arXiv:2509.14760v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly applied in diverse real-world scenarios, each governed by bespoke behavioral and safety specifications

Reasoning Shift: How Context Silently Shortens LLM Reasoning

arXiv:2604.01161v2 Announce Type: replace Abstract: Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remar

Recent Advances and Trends in Learning-based 3D Representations

arXiv:2606.04871v1 Announce Type: new Abstract: The selection of an appropriate 3D representation is a fundamental design decision that dictates the efficiency, quality, and capabilities of modern com

agentsarxiv-cs-cv

Applications

Reconciling Causality and Non-Equilibrium Thermodynamics with Hamiltonian Causal Models

arXiv:2606.04822v1 Announce Type: new Abstract: Causal modeling of physical temporal phenomena must handle interventions that act along trajectories, nonstationary induced laws, path-dependent effects

applicationsarxiv-cs-lg

ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection

arXiv:2606.04706v1 Announce Type: new Abstract: AI-generated videos are becoming increasingly realistic, raising serious concerns about misinformation, content authenticity, and media trust. Reliable

researcharxiv-cs-cv

Reconstructing Unobservable Temperature Fields via Simulation-Aided Intelligent Sensing

arXiv:2606.04582v1 Announce Type: cross Abstract: Real-time monitoring of the temperature distribution within components and sub-structures is a challenging topic in many systems due to restrictions o

Local Ai

Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

arXiv:2606.04238v1 Announce Type: cross Abstract: Aggressive weight quantization to 2-bit precision offers substantial throughput and memory gains for large language model (LLM) inference, but typical

local-aiarxiv-cs-ai

Reducing the Filtering Effect in Public School Admissions: A Bias-aware Analysis for Targeted Interventions

arXiv:2004.10846v5 Announce Type: replace-cross Abstract: Problem definition: Traditionally, New York City's top 8 public schools have selected candidates solely based on their scores in the Specializ

Applications

Reflection Separation from a Single Image via Joint Latent Diffusion

arXiv:2606.04107v1 Announce Type: new Abstract: Single-image reflection separation is highly challenging under extreme conditions like glare or weak reflections. Existing methods often struggle to rec

applicationsarxiv-cs-cv

REGAIN: REconciliation GAIN-driven Auxiliary Direction Learning

arXiv:2606.04380v1 Announce Type: cross Abstract: Forecast reconciliation usually starts from a fixed measurement system and asks how forecasts should be projected onto a coherent space. We ask a diff

Reinforcement Learning from Rich Feedback with Distributional DAgger

arXiv:2606.05152v1 Announce Type: cross Abstract: Reasoning models have advanced rapidly, but the dominant reinforcement learning from verifiable rewards (RLVR) recipe remains surprisingly narrow: sam

safetyarxiv-cs-ai

RePercENT: Scaling Disentangled Representation Learning Beyond Two Modalities

arXiv:2606.05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and explo