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Inducing Calmness With Pocket-Sized Robotics: Reducing Movement and Heart Rate in Children through Hand-Held Tactile Interactions

arXiv:2605.27533v1 Announce Type: new Abstract: Periods of heightened arousal or restlessness can interfere with children's ability to focus, self-regulation, and physically calm. Technologies that en

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InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training

DGX agent

arXiv:2510.15859v4 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has driven recent breakthroughs in large language models (LLMs), especially for tasks where rewards can be compute

researcharxiv-cs-ai

28 May 2026

Integrated Exploration-Aware UAV Route Optimization and Path Planning

arXiv:2605.28654v1 Announce Type: new Abstract: Uncrewed aerial vehicles (UAVs) are increasingly used for exploration-driven monitoring in hazardous environments such as disaster zones, contaminated s

Intra-YOLO: A Small Object Detection Model for Caries and Molar-Incisor Hypomineralization in Intraoral Photography Based on Transfer Learning with Reinforcement Learning

arXiv:2605.28157v1 Announce Type: new Abstract: This study developed a computer-aided diagnosis (CAD) system for detecting caries and molar-incisor hypomineralization (MIH) in intraoral photographs. T

IRPO: Boosting Image Restoration via Post-training GRPO

arXiv:2512.00814v3 Announce Type: replace Abstract: Post-training has become effective for high-level generation, but its role in low-level vision remains underexplored. Existing image restoration met

Is Backpropagation Optimal? When Synthetic Gradients Improve Sample Efficiency

arXiv:2605.27946v1 Announce Type: cross Abstract: Backpropagation is the default learning rule for artificial neural networks and is often treated as the settled approach whenever differentiability is

Isometry pursuit

arXiv:2411.18502v2 Announce Type: replace-cross Abstract: Isometry pursuit is a convex algorithm for identifying orthonormal column-submatrices of wide matrices. It consists of a novel normalization m

Iterative Causal Discovery: Per-Edge Impossibility Certificates, Tier-Aware Oracle Queries, and the 1+K Lower Bound

arXiv:2605.27477v1 Announce Type: cross Abstract: Causal-discovery algorithms return a directed graph, yet provide no principled means of distinguishing edge directions identified by the data from tho

Knowledge Dependency Estimation for Reliable Question Answering

arXiv:2605.28047v1 Announce Type: new Abstract: Reliable question answering requires identifying not only whether an answer is correct, but also which available knowledge the prediction depends on. In

researcharxiv-cs-cl

La-Proteina: Atomistic Protein Generation via Partially Latent Flow Matching

arXiv:2507.09466v2 Announce Type: replace Abstract: Recently, many generative models for de novo protein structure design have emerged. Yet, only few tackle the difficult task of directly generating f

LaneRoPE: Positional Encoding for Collaborative Parallel Reasoning and Generation

arXiv:2605.27570v1 Announce Type: new Abstract: Parallel LLM test-time scaling techniques (e.g., best-of-N) require drawing N>1 sequences conditioned on the same input prompt. These methods boost accu

Latent-Conditioned Parameterized Quantum Circuits as Universal Approximators for Distributions over Quantum States

arXiv:2605.28690v1 Announce Type: cross Abstract: Many applications in quantum simulation, quantum chemistry, and quantum machine learning require not a single quantum state but an ensemble of states

Latent Diffusion for Missing Data

arXiv:2605.28427v1 Announce Type: new Abstract: Diffusion models have emerged as powerful generative approaches for missing-data imputation, yet most existing methods operate directly in data space an

Learning Logical Operations for Arbitrary Quantum Error Correction Codes

arXiv:2605.28162v1 Announce Type: cross Abstract: Logical operations are essential for quantum computation within quantum error-correcting codes. However, discovering their physical realizations is ch

Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases

arXiv:2602.22873v2 Announce Type: replace-cross Abstract: We introduce a theoretical framework that connects multi-chart autoencoders in manifold learning with the classical theory of vector bundles a

Learning Theory of the SVRG: Generalization and Convergence Analysis

arXiv:2605.28513v1 Announce Type: cross Abstract: Variance reduction (VR) methods employ stochastic gradients with decreasing variance, and they have been widely applied to solve large-scale optimizat

Learning to Label: A Reinforced Self-Evolving Framework for Semi-supervised Referring Expression Segmentation

arXiv:2605.28239v1 Announce Type: new Abstract: Semi-supervised referring expression segmentation (SS-RES) aims to achieve precise pixel-level language grounding under limited annotation, yet suffers

Learning What to Recommend: Minimax Optimal Simple Regret in Logistic Bandits

arXiv:2601.21167v2 Announce Type: replace Abstract: We study stochastic logistic bandits with d-dimensional action features under the simple-regret objective, where a learner uses T rounds of explorat

LiDDA: Data Driven Attribution at LinkedIn

arXiv:2505.09861v3 Announce Type: replace-cross Abstract: Data Driven Attribution, which assigns conversion credits to marketing interactions based on causal patterns learned from data, is the foundat

Ligand-Conditioned Discrete Diffusion for Protein Sequence-Structure Co-Design

arXiv:2605.27413v1 Announce Type: cross Abstract: Proteins perform their biological functions through three-dimensional structures encoded by amino acid sequences, and ligand-binding protein co-design

Linear Causal Representation Learning by Topological Ordering, Pruning, and Disentanglement

arXiv:2509.22553v2 Announce Type: replace-cross Abstract: Causal representation learning (CRL) has garnered increasing interest from the causal inference and artificial intelligence communities due to

Look on Demand: A Cognitive Scheduling Framework for Visual Evidence Acquisition in Multimodal Reasoning

arXiv:2605.28160v1 Announce Type: new Abstract: Existing multimodal reasoning approaches predominantly follow two paradigms: converting visual inputs into text prior to reasoning, or performing end-to

LoSATok: Low-dimensional Semantic-Acoustic Tokenizer for Cross-Domain Audio Understanding and Generation

arXiv:2605.27840v1 Announce Type: cross Abstract: Audio tokenizers are fundamental to unifying audio understanding and generation. Understanding requires high-level semantics, while generation demands

LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts

arXiv:2602.11564v2 Announce Type: replace Abstract: Recent advances in video diffusion models have significantly improved visual quality, yet ultra-high-resolution (UHR) video generation remains a for

Machine Learning methods for event classification and vertex reconstruction of the 12C + 12C reaction with the MATE-TPC

arXiv:2605.28296v1 Announce Type: new Abstract: In modern nuclear physics experiments, identifying events of interest is challenging for nuclear reaction studies with the active target Time Projection

Magnet-Based Soft Robotic Skin Using a 3D-Printed Multi-Lattice Structure and CNN-Based Tactile Super-Resolution

arXiv:2605.28352v1 Announce Type: new Abstract: This paper presents a magnet-based robotic skin that integrates a multilayer soft lattice with distributed Hall-effect sensor arrays and a tactile super

MCTS-Judge: Test-Time Scaling in LLM-as-a-Judge for Code Correctness Evaluation

arXiv:2502.12468v2 Announce Type: replace-cross Abstract: The LLM-as-a-Judge paradigm shows promise for evaluating generative content but lacks reliability in reasoning-intensive scenarios, such as pr

Measuring Form and Function in Language Models

arXiv:2605.28616v1 Announce Type: cross Abstract: We introduce quantitative metrics for child language acquisition to evaluate language models. Our focus is on the formal syntactic and functional disc

Measuring Progress Toward AGI: A Cognitive Framework

arXiv:2605.28405v1 Announce Type: new Abstract: Despite widespread discussion of AGI, there is no clear framework for measuring progress toward it. This ambiguity fuels subjective claims, makes it dif

Mechanistically Interpreting the Role of Sample Difficulty in RLVR for LLMs

arXiv:2605.28388v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Reward (RLVR) is empirically shown to notably enhance the reasoning performance of large language models (LLMs),

Memory-Based vs. Context-Only Conditioning Produces Distinct Behavioral Patterns in Stateful Personalization

arXiv:2605.27389v1 Announce Type: cross Abstract: We study how conditioning context shapes personalization behavior in a teacher-facing educational recommender system. We compare contextual conditioni

MERIT: Matching Expertise via Rubric-Informed Training for Reviewer Assignment

arXiv:2605.27865v1 Announce Type: new Abstract: Matching submissions with suitable reviewers at scale is a growing challenge for major venues, yet existing approaches either rely on coarse proxy signa

researcharxiv-cs-cl

Metric-Aware PCA as a Linear Instance of Geometric Deep Learning

arXiv:2605.27456v1 Announce Type: new Abstract: Geometric deep learning organises neural architectures around the symmetries of their data domain, with the choice of symmetry group serving as a geomet

MGRetrieval: Memory-Guided Reflective Retrieval for Long-Term Dialogue Agents

arXiv:2605.27437v1 Announce Type: cross Abstract: Large Language Models (LLMs) have made significant progress in dialogue, yet redundant memory contexts severely limit their effectiveness in long-term

Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy

arXiv:2605.28078v1 Announce Type: cross Abstract: We design a class of additive noise mechanisms that satisfy ((arepsilon, elta))-differential privacy (DP) for scalar, real-valued query functions with

MORI-Seg: Learning Morphological Geometry for Instance Segmentation without Instance Annotations

arXiv:2605.28261v1 Announce Type: new Abstract: Instance-level quantification of kidney functional units is essential for morphometric analysis, yet most publicly available pathology datasets provide

Multi-Mixer Models: Flexible Sequence Modeling with Shared Representations

arXiv:2605.28769v1 Announce Type: new Abstract: Softmax attention is the cornerstone of modern large language models, but its memory scales linearly and compute quadratically with sequence length. Lin

Natural Functional Gradients for Smooth Trajectory Optimization

arXiv:2605.28202v1 Announce Type: new Abstract: Generating collision-free and smooth motions remains a central challenge in robotic manipulation, particularly in cluttered environments and narrow pass

Natural Locomotion: Principle and Method

arXiv:2605.28254v1 Announce Type: new Abstract: Robotic locomotion can become efficient when mechanisms exploit passive dynamics, compliance, and resonance rather than track prescribed trajectories. T

Neural Quantum Spectral Operator Learning for Solving Partial Differential Equations

arXiv:2605.27408v1 Announce Type: cross Abstract: Partial differential equations (PDEs) are central to modeling physical and engineering systems, but repeatedly solving parametric PDEs remains computa

Neural Weight Compression for Language Models

arXiv:2510.11234v3 Announce Type: replace Abstract: Efficient compression of language model weights is increasingly critical as model scale and deployment grow. Yet, most existing methods rely on hand

NEW: Aleph Prover has formalized OpenAI’s disproof of Paul Erdős’ planar unit problem. We are releasing the formalization as open source so …

NEW: Aleph Prover has formalized OpenAI’s disproof of Paul Erdős’ planar unit problem. We are releasing the formalization as open source so that other researchers can inspect, extend, and independentl

researchyann-lecun--x

Next-Scale Autoregressive Models for Text-to-Motion Generation

arXiv:2604.03799v2 Announce Type: replace Abstract: Autoregressive (AR) models offer stable and efficient training, but standard next-token prediction is not well aligned with the temporal structure r

Noise Scheduling as Information-Guided Allocation in Diffusion Training

arXiv:2602.18647v2 Announce Type: replace-cross Abstract: We introduce InfoNoise, an online adaptive noise schedule for diffusion training that reallocates optimization effort toward noise levels wher

Not All NVFP4 QAT Recipes Are Equal: How Architecture and Scale Shape Model Quality for Anomaly Segmentation

arXiv:2605.27616v1 Announce Type: cross Abstract: Real-time anomaly segmentation demands both high recall and efficient low-precision inference. We study the three-way interaction of model architectur

NUCLEUS-MoE: Unified Model of Pool Boiling for Liquid Cooling

arXiv:2605.27722v1 Announce Type: new Abstract: Two-phase boiling enables heat transfer rates an order of magnitude higher than single-phase cooling, but it remains difficult to model due to the stron

Object-Centric Vision Token Pruning for Vision Language Models

arXiv:2511.20439v2 Announce Type: replace-cross Abstract: In Vision Language Models (VLMs), vision tokens are quantity-heavy yet information-dispersed compared with language tokens, thus consume too m

Ocean4Rec: Offline LLM-Derived OCEAN Profiles for Request-Time VOD Reranking

arXiv:2605.27429v1 Announce Type: cross Abstract: Industrial video-on-demand (VOD) recommenders need richer content understanding, but LLM-as-reranker designs repeat prompt construction, token generat