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Music Transcription with (Almost) No Supervision

arXiv:2605.24193v1 Announce Type: cross Abstract: Competitive music transcription models require large amounts of paired audio-score data, which is scarce due to collection costs, alignment difficulty

MVR-cache: Optimizing Semantic Caching via Multi-Vector Retrieval and Learned Prompt Segmentation

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Human DGX agent

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DGX agent

arXiv:2605.24914v1 Announce Type: cross Abstract: To reduce LLM costs and latency, semantic caching systems must accurately identify when a new prompt matches a cached one. Current methods often rely

researcharxiv-cs-lg

26 May 2026

Local Ai

MX-SAFE: Versatile Inference- and Training-Proof Microscaling Format with On-the-Fly Exponent and Mantissa Bit Allocation

arXiv:2605.24391v1 Announce Type: cross Abstract: As the demand for deep learning grows, cost reduction through quantization has become essential for both training and inference. In 2022, the Open Com

local-aiarxiv-cs-ai

Nano World Models: A Minimalist Implementation of Future Video Prediction

arXiv:2605.23993v1 Announce Type: cross Abstract: World models have become a central paradigm for learning predictive simulators that support generation, planning, and decision-making. Yet, despite ra

Near-Optimal Nonconvex-Strongly-Convex Bilevel Optimization with Fully First-Order Oracles

arXiv:2306.14853v5 Announce Type: replace-cross Abstract: In this work, we consider bilevel optimization when the lower-level problem is strongly convex. Recent works show that with a Hessian-vector p

NEST: Network- and Memory-Aware Device Placement For Distributed Deep Learning

arXiv:2603.06798v2 Announce Type: replace Abstract: The growing scale of deep learning demands distributed training frameworks that jointly reason about parallelism, memory, and network topology. Prio

Neural Integral Operators for Inverse Problems: An Operator-Learning Framework for Small-Sample Spectroscopic Classification

arXiv:2505.03677v3 Announce Type: replace Abstract: Learning maps between function spaces with a strong inductive bias is a central challenge in soft computing, especially when training data are scarc

Neural Router: Semantic Content Matching for Agentic AI

model-releasesarxiv-cs-cl

arXiv:2605.25701v1 Announce Type: cross Abstract: Large language models (LLMs) can serve as the semantic-matching engine of a content-based publish/subscribe broker for agentic AI across the edge-clou

Neural Scalable Symbolic Search Framework for Complex Logical Queries with Multiple Free Variables

arXiv:2605.25985v1 Announce Type: new Abstract: Complex Query Answering (CQA) is a fundamental knowledge representation and reasoning task over incomplete knowledge graphs (KGs). Answering existential

Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling

arXiv:2508.17090v4 Announce Type: replace-cross Abstract: Ecological Momentary Assessment (EMA) studies enable the collection of high-frequency self-reports of suicidal thoughts and behaviors (STBs) v

NeuralTouch: Neural Descriptors for Precise Sim-to-Real Tactile Robot Control

arXiv:2510.20390v2 Announce Type: replace Abstract: Grasping accuracy is a critical prerequisite for precise object manipulation, often requiring careful alignment between the robot hand and object. N

safetyarxiv-cs-ro

NeurIPS: Neuro-anatomical Inductive Priors for Sphere-based Brain Decoding

arXiv:2605.24993v1 Announce Type: new Abstract: Current fMRI decoders face a performance-fidelity trade-off where efficient ID encoders outperform geometrically faithful surface-based models. We argue

Neuro-Inspired Inverse Learning for Planning and Control

arXiv:2605.24152v1 Announce Type: new Abstract: We present a neuro-inspired framework for embodied planning and control. Building on three principles that enable fast and highly effective goal-directe

Neuromorphic LiDAR-based Bird's Eye View Object Detection using Energy-efficient Spiking Neural Networks

arXiv:2605.25293v1 Announce Type: cross Abstract: Autonomous driving perception demands accurate and efficient processing of three-dimensional sensor data under strict power constraints. Traditional c

Agents

Neuronal Stochastic Attention Circuit (NSAC) for Probabilistic Representation Learning

arXiv:2605.26061v1 Announce Type: cross Abstract: Reliable quantification of uncertainty estimates in continuous-time (CT) representation learning remains nascent, particularly within CT attention arc

agentsarxiv-cs-ai

NITP: Next Implicit Token Prediction for LLM Pre-training

arXiv:2605.24956v1 Announce Type: new Abstract: Standard next-token prediction (NTP) supervises language models solely through discrete labels in the output logit space. We argue that this sparse one-

researcharxiv-cs-cl

Noise-Robust Financial Numerical Entity Attribute Tagging

arXiv:2605.24910v1 Announce Type: new Abstract: Financial Numerical Entity (FNE) understanding aims to recover the meaning of numerical mentions in financial reports. Existing studies primarily focus

Local Ai

Non-Invasive Reconstruction of Intracranial EEG Across the Deep Temporal Lobe from Scalp EEG based on Conditional Normalizing Flow

arXiv:2603.03354v3 Announce Type: replace-cross Abstract: Although obtaining deep brain activity from non-invasive scalp electroencephalography (sEEG) is crucial for neuroscience and clinical diagnosi

local-aiarxiv-cs-ai

Applications

Nonconvex Decentralized Stochastic Bilevel Optimization under Heavy-Tailed Noise

arXiv:2509.15543v2 Announce Type: replace Abstract: Existing decentralized stochastic optimization methods assume the lower-level loss function is strongly convex and the stochastic gradient noise has

applicationsarxiv-cs-lg

Nonstationary Generalized Linear Bandits with Discounted Online Mirror Descent

arXiv:2605.25590v1 Announce Type: cross Abstract: We study nonstationary generalized linear bandits (GLBs), where the expected reward is modeled through a nonlinear link function with an unknown time-

NormimesDirection: Restoring the Missing Query Norm in Vision Linear Attention

arXiv:2506.21137v3 Announce Type: replace Abstract: Linear attention mitigates the quadratic complexity of softmax attention but suffers from a critical loss of expressiveness. We identify two primary

Not All Transitions Matter: Evidence from PPO

arXiv:2605.24071v1 Announce Type: cross Abstract: Training a reinforcement learning agent on-policy means collecting fresh experience at every update, and that experience comes with a hidden problem.

Not only where, But when: Temporal Scheduling for RLVR

arXiv:2605.25381v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a core technique for post-training of Large Language Models (LLMs). While policy optimi

NPSolver: Neural Poisson Solver with Iterative Physics Supervision

arXiv:2605.25786v1 Announce Type: cross Abstract: Efficiently solving Poisson equations on complex, irregular domains remains a fundamental challenge in scientific computing, as classical iterative so

Applications

NSR-Boost: A Neuro-Symbolic Residual Boosting Framework for Industrial Legacy Models

arXiv:2601.10457v3 Announce Type: replace Abstract: Although the Gradient Boosted Decision Trees (GBDTs) dominate industrial tabular applications, upgrading legacy models in high-concurrency productio

applicationsarxiv-cs-ai

Nystrom Kernel Stein Discrepancy Tests

arXiv:2605.25173v1 Announce Type: cross Abstract: Kernel Stein discrepancy (KSD) is among the most popular goodness-of-fit (GoF) measures on general domains with a large number of successful deploymen

OASIS: Observation-Action Space Alignment via SE(3) Trajectory Prediction for Robotic Manipulation

arXiv:2605.25829v1 Announce Type: cross Abstract: Recent vision-language-action (VLA) models and world action models (WAMs) advance robotic manipulation by enriching intermediate representations with

Omissive Bias in Religious Representation: Benchmarking LLM Answers to Everyday Ethical Decision-making

arXiv:2605.24319v1 Announce Type: new Abstract: As large language models become a default source of guidance on personal, moral, and existential questions, it matters whether they draw on the religiou

OmniSapiens: A Foundation Model for Social Behavior Processing via Heterogeneity-Aware Relative Policy Optimization

arXiv:2602.10635v2 Announce Type: replace Abstract: Socially intelligent AI systems must entail reasoning across diverse human behavioral tasks, and generalization to new contexts. However, AI has yet

On Reliability of Efficient Membership Inference Vulnerability Evaluation

arXiv:2605.25819v1 Announce Type: new Abstract: Membership inference attacks (MIAs) are popular methods for empirically assessing the leakage of sensitive information in the training data through mode

On the Benefits of Free Exploration for Regret Minimization in Multi-Armed Bandits

arXiv:2605.25789v1 Announce Type: cross Abstract: We study a stochastic multi-armed bandit problem where an agent is granted a free exploration budget before regret accumulates, a setting not captured

Tutorials

On the Communication Complexity of Decentralized Stochastic Bilevel Optimization

arXiv:2311.11342v5 Announce Type: replace Abstract: Stochastic bilevel optimization finds widespread applications in machine learning, including meta-learning, hyperparameter optimization, and neural

tutorialsarxiv-cs-lg

On the Epistemic Uncertainty of Overparametrized Neural Networks

arXiv:2605.25234v1 Announce Type: cross Abstract: Epistemic uncertainty is often viewed as a reducible uncertainty that vanishes with increasing data. This perspective implicitly assumes parameter ide

On the Impact of Class Imbalance on the Learning Dynamics of Deep Neural Networks:An Intuitive Insight

arXiv:2605.24908v1 Announce Type: cross Abstract: Class imbalance in deep neural networks (DNNs) has witnessed a rapid increase in research attention in recent years. However, the varying accounts of

On the Interaction of Batch Noise, Adaptivity, and Compression, under (L_0,L_1)-Smoothness: An SDE Approach

arXiv:2506.00181v2 Announce Type: replace Abstract: Distributed stochastic optimization intertwines (i) stochastic gradient noise, (ii) communication compression, and (iii) adaptive/normalized updates

On the Limits of Model Merging for Multilinguality in Pre-Training

arXiv:2605.25846v1 Announce Type: new Abstract: Endowing models with consistent multilingual performance can be achieved by mixing pre-training data, or post-training approaches such as language-speci

researcharxiv-cs-cl

On the Sample Complexity of Robust Binary Hypothesis Testing

arXiv:2605.24741v1 Announce Type: cross Abstract: We study the sample complexity of robust binary hypothesis testing under three standard contamination models: arepsilon-additive (Huber), arepsilon-su

On the Stability and Realizability of Recurrent Polynomial Surrogate Ternary Logic Gate Networks

arXiv:2605.24649v1 Announce Type: cross Abstract: Recurrent Neural Networks (RNNs) can learn to predict Signal Temporal Logic (STL) verdicts online from partial trajectories, but deploying them as run

One-for-All Model Initialization with Frequency-Domain Knowledge

arXiv:2603.07523v2 Announce Type: replace Abstract: Transferring knowledge by fine-tuning large-scale pre-trained networks has become a standard paradigm for downstream tasks, yet the knowledge of a p

One-shot Conditional Sampling: MMD meets Nearest Neighbors

arXiv:2509.25507v2 Announce Type: replace-cross Abstract: How can we generate samples from a conditional distribution that we never fully observe? This question arises across a broad range of applicat

One-Step Bellman Alignment Enables Provably Efficient Transfer in Online RL

arXiv:2601.21924v2 Announce Type: replace Abstract: We study online transfer reinforcement learning (RL) in episodic Markov decision processes, where experience from related source tasks is available

Agents

OPAL: Omnidirectional Path-efficient Aerial 3D expLoration

arXiv:2605.25423v1 Announce Type: new Abstract: Autonomous exploration is critical for robot mapping unknown environments. Desirable characteristics of exploration algorithms include compute efficienc

agentsarxiv-cs-ro

Operationalizing Reconstructive Authority: Runtime Construction, Dependency Resolution, and Execution Gating in Autonomous Agent Systems

arXiv:2605.23935v1 Announce Type: new Abstract: Autonomous agent systems fail not only due to incorrect decisions, but due to executing decisions whose authority no longer holds at runtime. Prior work

Opportunistic Target Selection: Early Directional Commitment for Query-Efficient Black-Box Adversarial Attacks

arXiv:2605.25663v1 Announce Type: new Abstract: Black-box adversarial attacks that minimize only the ground-truth confidence suffer from class drift: perturbations wander through the feature space wit

Local Ai

Optimal and Order-optimal Gated Priority-based Greedy Policies for Two-layer Multi-item Order Fulfillment

arXiv:2605.25888v1 Announce Type: new Abstract: We study how an e-commerce firm should make real-time fulfillment decisions in a two-layer distribution network when multi-item customer orders arrive s

local-aiarxiv-cs-lg

Agents

Optimal Design for Multinomial Logit Model with Applications to Best Assortment Identification

arXiv:2605.25592v1 Announce Type: cross Abstract: We study optimal experimental design for multinomial logit (MNL) bandits, where an agent repeatedly selects a subset of K items from a ground set of s

agentsarxiv-cs-lg

Optimal Non-Asymptotic Edgeworth Expansions for Multivariate Neural Network Outputs

arXiv:2605.24072v1 Announce Type: cross Abstract: Finite-width fully connected neural networks with Gaussian-initialized weights deviate from their infinite-width Gaussian limit, exhibiting non-vanish

Optimal uncertainty bounds for multivariate kernel regression under bounded noise: A Gaussian process-based dual function

arXiv:2603.16481v2 Announce Type: replace Abstract: Non-conservative uncertainty bounds are essential for making reliable predictions about latent functions from noisy data, and thus, a key enabler fo