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MeEvo: Metacognitive Evolution Combined with Natural Evolution for Automatic Heuristic Design

arXiv:2606.14202v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have advanced Automatic Heuristic Design (AHD) by enabling heuristic generation through reasoning and code synthe

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MGDFIS: Multi-scale Global-detail Feature Integration Strategy for Small Object Detection

DGX agent

arXiv:2506.12697v3 Announce Type: replace-cross Abstract: Small-object detection in Unmanned Aerial Vehicle (UAV) imagery requires preserving weak local evidence while using broader context to separat

researcharxiv-cs-ai

30 Jun 2026

MirrorPPR: Exemplar-Based Portrait Photo Retouching

arXiv:2606.29308v1 Announce Type: new Abstract: While text-guided image editing has made remarkable progress, it remains limited in structural portrait retouching. Textual descriptions struggle to con

Miti360: A Comprehensive Dataset for Improved Reforestation Monitoring

arXiv:2606.29447v1 Announce Type: new Abstract: Over the past decade, interest in applying machine learning (ML) to automate forest monitoring has grown significantly. However, existing training datas

Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation

arXiv:2606.28697v1 Announce Type: cross Abstract: Pathology foundation models (PFMs) have demonstrated strong potential across clinical and scientific applications, yet their performance is often hind

ML-Powered LDAP Reconnaissance Detection using Weak Supervision

arXiv:2606.28917v1 Announce Type: new Abstract: Lightweight Directory Access Protocol (LDAP) is a protocol that allows users to query and modify Active Directory (AD) data. By default, all users have

Model Directions, Not Words: Mechanistic Topic Models Using Sparse Autoencoders

arXiv:2507.23220v2 Announce Type: replace Abstract: Traditional topic models are effective at uncovering latent themes in large text collections. However, due to their reliance on bag-of-words represe

Momentum Guidance: Plug-and-Play Guidance for Flow Models

arXiv:2602.20360v2 Announce Type: replace-cross Abstract: Flow-based generative methods offer a simple and effective framework for high-fidelity generation, yet pretrained flow models are rarely used

MTD-Map: Single-Stage Long-Term LiDAR Map Maintenance Framework via Mixture Transition Distribution

arXiv:2606.29469v1 Announce Type: new Abstract: While robust map maintenance has advanced significantly, existing studies have focused on specific tasks, especially dynamic object removal or change de

Multi-Block Diffusion Language Models

arXiv:2606.29215v1 Announce Type: cross Abstract: Block Diffusion Language Models (BD-LMs) improve diffusion-based text generation with KV caching and flexible-length generation. A natural next step i

Multi-Contact Force Estimation for Continuum Robots via Gaussian-Parameterized Factor Graphs

arXiv:2606.29165v1 Announce Type: new Abstract: Continuum robots offer key advantages in navigating unstructured environments, but their safe operation requires accurate estimation of the external con

Multi-Level Distributional Entropy for Explainable Network Intrusion Detection

arXiv:2606.29797v1 Announce Type: cross Abstract: Machine learning network intrusion detection systems (IDS) rely on aggregate flow statistics that discard distributional structure, while established

Multi-UAV Formation Cooperative Obstacle Avoidance and Adaptive Shape Deformation Control in Complex Environments Based on BI-APF-RRT and Affine Transformation

arXiv:2606.29755v1 Announce Type: new Abstract: Aiming at the problem that obstacle avoidance flexibility and formation integrity are difficult to coexist in multi-UAV formation motion in complex obst

Multiplicative Oracle Inequalities for Transductive Learning via Level-Set Aggregation

arXiv:2603.02043v2 Announce Type: replace Abstract: We revisit transductive learning where predictions are made with the set of all covariates known in advance. In the leave-one-out (LOO) setting, the

MuonSSM: Orthogonalizing State Space Models for Sequence Modeling

arXiv:2606.30461v1 Announce Type: new Abstract: State space models (SSMs) have emerged as efficient linear-time alternatives to attention for long-sequence modeling. However, existing SSMs often suffe

Negative Stepsizes Make Gradient-Descent-Ascent Converge

arXiv:2505.01423v2 Announce Type: replace-cross Abstract: Efficient computation of min-max problems is a central question in optimization, learning, games, and control. Arguably the most natural algor

Neural Galerkin Normalizing Flow for Transition Probability Density Functions of Diffusion Models

arXiv:2603.18907v2 Announce Type: replace Abstract: We propose a new Neural Galerkin Normalizing Flow framework to approximate the transition probability density function of a diffusion process by sol

Neural Minimum Weight Perfect Matching for Quantum Error Codes

arXiv:2601.00242v2 Announce Type: replace-cross Abstract: Realizing the full potential of quantum computation requires Quantum Error Correction (QEC). QEC reduces error rates by encoding logical infor

NeuReasoner: Theory-grounded Mapping of Reasoning Elicitation Boundaries

arXiv:2606.29971v1 Announce Type: new Abstract: A growing body of work suggests that the reasoning capabilities of large language models are largely latent in their base form, with post-training prima

New methods to compute the generalized chi-square distribution

arXiv:2404.05062v4 Announce Type: replace-cross Abstract: We present four new mathematical methods, two exact and two approximate, along with open-source software, to compute the cdf, pdf and inverse

Non-parametric recovery of causal diffusion mechanisms from steady-state observations

arXiv:2606.30467v1 Announce Type: cross Abstract: We consider sparse multivariate stochastic systems that evolve in continuous time according to a causal mechanism and present methodology to recover t

Not All Objectives Are Born Equal: Priority-Constrained Descent for Hierarchical Multi-Objective Optimization

arXiv:2606.29521v1 Announce Type: new Abstract: Deep learning problems rarely involve objectives that are equal in importance. A primary objective defines the goal, whilst secondary objectives, such a

Notes on generative modeling: flow matching, diffusion, optimal transport and Schr{odinger bridge

arXiv:2606.30053v1 Announce Type: cross Abstract: These notes recapitulate the high level mathematical principles behind different techniques for generative modeling. I show the connections between op

OLIVE: View-Augmented Latent Prediction with Waveform Reconstruction for Speech SSL

arXiv:2606.30356v1 Announce Type: new Abstract: We propose Online Latent prediction with Invariant Views and rEconstruction (OLIVE), a self-supervised speech representation learning framework that joi

OmniDance: Multimodal Driven Dance Video Generation with Large-scale Internet Data

arXiv:2606.30019v1 Announce Type: new Abstract: Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidel

On design-unbiased algorithmic Machine Learning

arXiv:2606.28795v1 Announce Type: new Abstract: Machine Learning (ML) algorithms, such as k-Nearest Neighbours (kNN) or random forest, eschew the ideal of true data models in favour of predictive perf

On Local Population-Risk Certificates

arXiv:2606.19147v2 Announce Type: replace-cross Abstract: We develop finite-sample certificates for local population-risk increments (Pelta_v=R(heta_0+v)-R(heta_0)), (vinmathcal D). The primitive obje

On Test-Time Scaling for Vision-Language Models

arXiv:2606.28864v1 Announce Type: new Abstract: Test-time scaling is a paradigm where large models use additional compute at inference to achieve better performance, without changing model weights. Wh

On the Identifiability of Aided Inertial Navigation Under Measurement Delays: A Geometric Approach

arXiv:2606.29123v1 Announce Type: new Abstract: In aided inertial navigation, measurements from different sensors are often subject to unknown relative time delays. Consider a single aiding sensor who

On the Nonlinearity of Learning Rate Scaling for LLM Training

arXiv:2606.29158v1 Announce Type: cross Abstract: Learning-rate transfer can reduce the cost of training large language models: instead of sweeping learning rates at target scale, practitioners extrap

One-Step Gradient Delay is Not a Barrier for Large-Scale Asynchronous Pipeline Parallel LLM Pretraining

arXiv:2606.30634v1 Announce Type: new Abstract: Modern large-scale LLM pretraining benefits from utilizing Pipeline Parallelism; however, synchronous implementations leave GPUs idle during pipeline bu

Open Problems in Constitutional Preference Reconstruction

arXiv:2606.30116v1 Announce Type: new Abstract: Pairwise preference data is widely used for training and evaluating language models (e.g., RLHF), but each datapoint records a choice, not the rationale

OpenSPM: An Environment-Transferable Robotic Key Spatial Pose Memory and Closed-Loop High-Frequency Flow-Matching Action Generation Model

arXiv:2606.29936v1 Announce Type: new Abstract: Open-environment tabletop robotic manipulation requires systems to possess semantic understanding, precise geometric pose estimation, and high-frequency

Operating Regimes of Decentralized Learning Under Mobility and Bandwidth Constraints

arXiv:2606.28342v1 Announce Type: cross Abstract: Decentralized learning is a promising paradigm for collaborative training in mobile and pervasive systems, as it avoids a central coordinator and does

Optimization Dynamics Imprint Semantic Specificity in Contrastive Embedding Norms

arXiv:2606.30625v1 Announce Type: cross Abstract: Contrastive embedding models trained with scale-invariant losses are typically paired with distance metrics like cosine similarity, effectively ignori

Optimizer Memory Makes Shuffle Order a First-Order Source of Fine-Tuning Noise

arXiv:2606.29554v1 Announce Type: new Abstract: Shuffle order can be a larger source of fine-tuning noise than a memoryless analysis predicts: fixed-clock optimizer memory makes local equal-multiset c

Optimizing Image Preparation and Compression for Face Recognition within 1024 Bytes

arXiv:2606.30321v1 Announce Type: new Abstract: ICAO-compliant machine readable travel documents enable automated biometric face verification. The biometric reference is stored on an RFID chip include

Orca: The World is in Your Mind

arXiv:2606.30534v1 Announce Type: new Abstract: We introduce Orca, an initial instantiation of a general world foundation model. Orca learns a unified world latent space from multimodal world signals

Overcoming Dependent Censoring in the Evaluation of Survival Models

arXiv:2502.19460v4 Announce Type: replace-cross Abstract: Dependent censoring occurs when the event time and censoring time are not conditionally independent given the observed covariates. This compli

PASTA: A Paraphrasing And Self-Training Approach for Knowledge Updating in LLMs

arXiv:2606.28898v1 Announce Type: new Abstract: Knowledge updating in pre-trained Large Language Models (LLMs) remains an important challenge. While continual training provides a potential avenue for

PCP-GAN: Property-Constrained Pore-scale image reconstruction via conditional Generative Adversarial Networks

arXiv:2510.19465v2 Announce Type: replace Abstract: Obtaining truly representative pore-scale images that match bulk formation properties remains a fundamental challenge in subsurface characterization

Personalized Additive Modeling for Multi-level Federated Learning

arXiv:2405.16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplif

Personalizing MLLMs via Reinforced Multimodal Reference Game

arXiv:2606.28845v1 Announce Type: new Abstract: Personalizing Multimodal Large Language Models (MLLMs) aims to recognize users' unique concepts from visual data and provide personalized responses. Alt

Perspectives on Latent Factor Indeterminacy and its Implications for Data Representation

arXiv:2606.28854v1 Announce Type: cross Abstract: The common factor analytic model is related to Helmholtz and Boltzmann machines, can be conceived as a linear autoencoder, or can be thought of as a s

Physically-Constrained Harmonic Separation for Robust Heart and Respiratory Rate Estimation from Wrist Photoplethysmography

arXiv:2606.30156v1 Announce Type: cross Abstract: Wrist-worn photoplethysmography (PPG) enables continuous monitoring of cardiopulmonary physiology, but reliable heart rate (HR) and respiratory rate (

Physics-Informed Distillation of Diffusion Models for PDE-Constrained Generation

arXiv:2505.22391v2 Announce Type: replace-cross Abstract: Modeling physical systems in a generative manner offers several advantages, including the ability to handle partial observations, generate div

PinNet: Keypoint-Aware Learned Local Descriptors with Geometric Embedding for Loop Closure in LiDAR SLAM

arXiv:2606.28637v1 Announce Type: new Abstract: Loop closure is essential to reduce drift and build globally consistent maps in large-scale environments. However, reliable loop closure with only geome

PLAA: Packet-level Adversarial Attacks in Network Traffic Detection

arXiv:2606.28439v1 Announce Type: cross Abstract: Deep neural networks (DNNs) are widely applied in Network-based Intrusion Detection System (NIDS) due to their high accuracy. However, DNNs are highly