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arXiv:2603.02794v2 Announce Type: replace-cross Abstract: We present TVF (Time-Varying Filtering), an interpretable, low-latency speech enhancement model for real-time, on-device assistive hearing. A

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Model Releases

Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction

DGX agent

arXiv:2606.27539v1 Announce Type: cross Abstract: Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations. Accurate prediction

model-releasesarxiv-cs-ai

29 Jun 2026

Agents

COOPA: A Modular LLM Agent Architecture for Operations Research Problems

arXiv:2606.27611v1 Announce Type: new Abstract: Operations Research (OR) provides a rigorous framework for high-stakes decision-making, but effective OR modeling requires substantial domain knowledge,

agentsarxiv-cs-lg

Explainable AI for Biodiversity Monitoring and Ecological Image Analysis

arXiv:2606.27667v1 Announce Type: cross Abstract: Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, dron

tutorialsarxiv-cs-ai

Local Ai

Hybrid coupling with operator inference and the overlapping Schwarz alternating method

arXiv:2511.20687v3 Announce Type: replace-cross Abstract: This paper presents a novel hybrid approach for coupling subdomain-local non-intrusive Operator Inference (OpInf) reduced order models (ROMs)

local-aiarxiv-cs-ai

MeDUET: Disentangled Unified Pretraining for 3D Medical Image Synthesis and Analysis

arXiv:2602.17901v3 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) and diffusion models have respectively advanced representation learning and generative modeling for high-dimens

tutorialsarxiv-cs-cv

On the Effect of Uncertainty on Layer-wise Inference Dynamics

arXiv:2507.06722v2 Announce Type: replace Abstract: Understanding how large language models (LLMs) internally represent and process their predictions is central to detecting uncertainty and preventing

tutorialsarxiv-cs-cl

Applications

Ranking Before Serving: Low-Latency LLM Serving via Pairwise Learning-to-Rank

arXiv:2510.03243v3 Announce Type: replace-cross Abstract: Efficient scheduling of large language model (LLM) inference tasks is critical for achieving low latency and high throughput, a challenge that

applicationsarxiv-cs-ai

ReScene: Structured Indoor Scene Reconstruction from Multi-View Captures

arXiv:2606.28060v1 Announce Type: new Abstract: Constructing simulation-ready 3D scenes from multi-view captures is a key bottleneck for Embodied Artificial Intelligence, as downstream tasks require o

SpatialUAV: Benchmarking Spatial Intelligence for Low-Altitude UAV Perception, Collaboration, and Motion

arXiv:2606.27876v1 Announce Type: cross Abstract: Spatial intelligence is essential for low-altitude unmanned aerial vehicle (UAV) perception, collaboration, and navigation. However, existing UAV benc

StableMotion: One-Step Motion Estimation with Diffusion Prior

arXiv:2505.06668v2 Announce Type: replace Abstract: We present StableMotion, a novel framework that leverages geometric and content priors from pretrained large-scale image diffusion models for motion

researcharxiv-cs-cv

There and Back Again: A Flexible-Frame Transformer for Multi-Exposure Fusion

arXiv:2606.27905v1 Announce Type: new Abstract: Multi-exposure fusion (MEF) brings the dynamic range of conventional cameras closer to that of human vision, producing images with rich scene content. G

TRUST: Efficient Abdominal Trauma Recognition via Image-to-Ultrasound-Video Transfer Learning

arXiv:2606.27777v1 Announce Type: new Abstract: Abdominal ultrasound is indispensable for rapid, noninvasive trauma triage. However, interpreting the subtle dynamic cues embedded in continuous scannin

Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation

arXiv:2606.26705v1 Announce Type: cross Abstract: Feedforward neural network (NN) expressivity is typically studied by emulating optimal basis-expansion schemes. While powerful, this perspective is in

Agents

Closing the Loop to Discover Psychological Theories with an Automated Cognitive Scientist

arXiv:2606.26448v1 Announce Type: cross Abstract: Across the sciences, autonomous systems are increasingly being used in closed-loop discovery, proposing new theories and designing and running experim

agentsarxiv-cs-ai

Continual Robot Policy Learning via Variational Neural Dynamics

arXiv:2606.27353v1 Announce Type: new Abstract: Robots deployed in the real world rarely operate under a single fixed dynamics model: wind changes, payloads vary, batteries drain, contacts shift, and

safetyarxiv-cs-ro

DanceDuo: Bridging Human Movement and AI Choreography

arXiv:2606.26507v1 Announce Type: cross Abstract: In recent years, advancements in deep learning and generative models have revolutionized music-driven dance generation. This paper introduces a novel

researcharxiv-cs-cv

Local Ai

DanceOPD: On-Policy Generative Field Distillation

arXiv:2606.27377v1 Announce Type: cross Abstract: Modern image generation demands a single model that unifies diverse capabilities, including text-to-image (T2I), local editing, and global editing. Ho

local-aiarxiv-cs-cl

Diagnosing Task Insensitivity in Language Agents

arXiv:2606.26918v1 Announce Type: new Abstract: Large language models can serve as capable long-horizon agents, but their out-of-distribution (OOD) generalization remains weak. We identify a key sourc

safetyarxiv-cs-ai

Divergent Recommendations, Convergent Diagnoses: Cross-Provider Failure-Mode Convergence in AI Commercial Recommendation

arXiv:2606.26116v1 Announce Type: cross Abstract: A brand whose customers use both ChatGPT and Claude for product recommendations faces a strategic choice: a single optimization playbook, or one per p

DMSC: Dynamic Multi-Scale Coordination Framework for Time Series Forecasting

arXiv:2508.02753v5 Announce Type: replace-cross Abstract: Time Series Forecasting (TSF) faces persistent challenges in modeling intricate temporal dependencies across different scales. Despite recent

tutorialsarxiv-cs-ai

Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

model-releasesarxiv-cs-lg

arXiv:2510.17459v3 Announce Type: replace-cross Abstract: In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary sy

Extracting Problem and Method Sentence from Scientific Papers: A Context-enhanced Transformer Using Formulaic Expression Desensitization

arXiv:2606.26481v1 Announce Type: new Abstract: Billions of scientific papers lead to the need to identify essential parts from the massive text. Scientific research is an activity from putting forwar

tutorialsarxiv-cs-cl

Applications

From Celebrities to Anyone: Characterizing AI Nudification Content, Technology, and Community Dynamics on 4chan

arXiv:2606.27234v1 Announce Type: cross Abstract: AI nudification uses generative models to create synthetic non-consensual sexually explicit imagery (SNEACI) of real individuals. Prior work has exami

applicationsarxiv-cs-ai

From Lexicon to AI: A Structured-Data Pipeline for Specialized Conversational Systems in Low-Resource Languages

arXiv:2606.26112v1 Announce Type: cross Abstract: Low-resource languages face a critical challenge in AI development: creating specialized conversational systems without access to massive training cor

Graph Neural Networks Applications Across Domains: All Insights You Need

arXiv:2606.27202v1 Announce Type: new Abstract: Graph neural networks have moved from a niche representation-learning technique to the default model class wherever data carry relational structure. The

HauntAttack: When Attack Follows Reasoning as a Shadow

arXiv:2506.07031v5 Announce Type: replace-cross Abstract: Emerging Large Reasoning Models (LRMs) consistently excel in mathematical and reasoning tasks, showcasing remarkable capabilities. However, th

safetyarxiv-cs-ai

HyperDFlash: MHC-Aligned Block Speculative Decoding with Gated Residual Reduction

model-releasesarxiv-cs-cl

arXiv:2606.26744v1 Announce Type: cross Abstract: We present HyperDFlash, a block-parallel speculative decoding framework tailored to the novel multi-hyper-connection (MHC) architecture proposed by De

Improving General Role-Playing Agents via Psychology-Grounded Reasoning and Role-Aware Policy Optimization

arXiv:2606.27025v1 Announce Type: new Abstract: Building general-purpose role-playing agents that faithfully portray any character from a natural-language profile remains challenging. The dominant par

safetyarxiv-cs-cl

Interpreting 'Interpretability' and Explaining 'Explainability' in Machine Learning in Physics

arXiv:2606.26228v1 Announce Type: cross Abstract: We review the concepts of interpretability and explainability as they apply to machine learning in physics. We define interpretability as concerning t

researcharxiv-cs-lg

Jailbreaking for the Average Jane: Choosing Optimal Jailbreaks via Bandit Algorithms for Automatically Enhanced Queries

model-releasesarxiv-cs-cl

arXiv:2606.26936v1 Announce Type: cross Abstract: With a profusion of jailbreaks for LLMs now widely known, a growing concern is that non-expert malicious actors ('the average Jane') could elicit acti

Just how sure are you? Improving Verbalized Uncertainty Calibration in Medical VQA

arXiv:2606.27023v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) applied to Medical Visual Question Answering (VQA) tend to produce overconfident outputs regardless of actual

safetyarxiv-cs-cl

LCAi: Life Cycle Assessment with big data fusion and retrieval-augmented generation-assisted interpretation

arXiv:2606.26857v1 Announce Type: new Abstract: The interpretation phase of life cycle assessment often lacks structured mechanisms for translating quantified improvement opportunities addressing envi

Learning Language-Driven Sequence-Level Modal-Invariant Representations for Video-Based Visible-Infrared Person Re-Identification

arXiv:2601.12062v2 Announce Type: replace Abstract: The core of video-based visible-infrared person re-identification (VVI-ReID) lies in learning sequence-level modal-invariant representations across

Life After Benchmark Saturation: A Case Study of CORE-Bench

arXiv:2606.26158v1 Announce Type: new Abstract: When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version. We show that this approach privileges accuracy

Limited Reference, Reliable Generation: A Two-Component Framework for Tabular Data Generation in Low-Data Regimes

arXiv:2509.09960v2 Announce Type: replace-cross Abstract: Synthetic tabular data generation is increasingly essential in machine learning, supporting downstream applications when real-world, high-qual

Liquid Fusion of Heterogeneous Representations Towards General Salient Object Detection

arXiv:2606.26849v1 Announce Type: new Abstract: General Salient Object Detection (SOD) aims to identify and segment visually interesting objects from uni-modality or multi-modality scenes, recently ad

Necessary but Not Sufficient: Temperature Control and Reproducibility in LLM-as-Judge Safety Evaluations

model-releasesarxiv-cs-lg

arXiv:2606.26185v1 Announce Type: new Abstract: LLM-as-judge ('grader') components are now standard in evaluation harnesses, including safety evaluations where a pass/fail verdict may gate downstream

Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis

model-releasesarxiv-cs-lg

arXiv:2406.10090v3 Announce Type: replace Abstract: Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having

Paved with True Intents: Intent-Aware Training Improves LLM Safety Classification Across Training Regimes

arXiv:2606.27210v1 Announce Type: new Abstract: We argue that safety classifiers should model user intent as an explicit signal between the prompt and the final label. To study this, we introduce AIMS

safetyarxiv-cs-cl

Physics-guided Convolutional Neural Network for Domain Growth Prediction in Systems with Conserved Kinetics

arXiv:2606.26128v1 Announce Type: new Abstract: The spatiotemporal evolution of many physical, chemical, and biological systems is described by nonlinear partial differential equations (PDEs). Recentl

tutorialsarxiv-cs-lg

PhysRAG: Enhancing Physics-Awareness in Video Generation via Retrieval-Augmented Generation

arXiv:2606.26916v1 Announce Type: new Abstract: Developing physically aware video generation models remains a significant challenge due to the difficulty in capturing diverse physical phenomena, such

researcharxiv-cs-cv

Real-Time Safety Evaluation of Human Arm Operations Using a Wrist-Mounted IMU with PSM System

model-releasesarxiv-cs-ro

arXiv:2502.09241v2 Announce Type: replace Abstract: This paper presents a novel approach to real-time safety monitoring in human-robot collaborative manufacturing environments through a wrist-mounted

RecallRisk-BERT: A Multi-Task Framework for Post-Report Medical Device Recall Triage

arXiv:2606.27174v1 Announce Type: new Abstract: Medical device recalls are a critical regulatory mechanism for protecting patient safety. The growing volume of FDA recall records presents challenges i

Agents

Rethinking Training & Inference for Forecasting: Linking Winner-Take-All back to GMMs

arXiv:2606.26424v1 Announce Type: cross Abstract: Trajectory forecasting for autonomous driving has advanced rapidly, yet representative models often produce uninformative posteriors over forecast mod

agentsarxiv-cs-cv

RolloutPipe: Overlapping Pipelined Rollout and Training in Disaggregated On-Policy LLM Reinforcement Learning

arXiv:2606.26997v1 Announce Type: cross Abstract: Large language model (LLM) post-training for reasoning increasingly relies on reinforcement learning with verifiable rewards (RLVR), where models lear

SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference

arXiv:2606.26587v1 Announce Type: cross Abstract: Low-bit floating-point formats and semi-structured sparsity are increasingly supported by modern accelerators, yet combining them for LLM activation c

Sketched Linear Contrastive Learning: Approximation, Optimization, and Statistical Scaling

arXiv:2606.26617v1 Announce Type: new Abstract: Scaling laws describe how learning performance varies with model size, data size, and compute. While recent theoretical work has established scaling law