Search: “research”

Muon Does Not Converge on Convex Lipschitz Functions

arXiv:2605.08980v1 Announce Type: new Abstract: Muon and its variants have shown strong empirical performance in a variety of deep learning tasks. Existing convergence analyses of Muon rely on smoothn

MuonEq: Balancing Before Orthogonalization with Lightweight Equilibration

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DGX agent

arXiv:2603.28254v2 Announce Type: replace Abstract: Orthogonalized-update optimizers such as Muon improve training of matrix-valued parameters, but existing extensions typically either rescale updates

researcharxiv-cs-lg

12 May 2026

Muown: Row-Norm Control for Muon Optimization

arXiv:2605.10797v1 Announce Type: new Abstract: Muon has emerged as a strong competitor to AdamW for language model pre-training, yet its behavior at scale is sensitive to weight decay. Recent work ha

Narrative Landscape: Mapping Narrative Dispositions Across LLMs

arXiv:2605.08742v1 Announce Type: cross Abstract: This study proposes a quantitative framework for profiling LLM dispositions as stable, model-specific regularities in output under repeated, controlle

Near-Optimal Last-Iterate Convergence for Zero-Sum Games with Bandit Feedback and Opponent Actions

arXiv:2605.09363v1 Announce Type: new Abstract: Last-iterate convergence of learning dynamics in games has attracted significant recent attention. In two-player zero-sum games with bandit feedback, wh

Nearly-Optimal Algorithm for Adversarial Kernelized Bandits

arXiv:2605.10299v1 Announce Type: new Abstract: This paper studies kernelized bandits (also known as Gaussian process bandits) in an adversarial environment, where the reward functions in a known repr

Network-Efficient World Model Token Streaming

arXiv:2605.09886v1 Announce Type: new Abstract: Generative driving world models rely on compact latent state representations that must be efficiently transmitted and synchronized across distributed co

researcharxiv-cs-ro

Neurally-plausible radial basis kernels using distributed Fourier embeddings

arXiv:2605.08458v1 Announce Type: new Abstract: Coherent, continuous spatial representations are critical for synthesizing physical and perceptual phenomena into a single representational space. Radia

Neuromorphic Monocular Depth Estimation with Uncertainty Modeling

arXiv:2605.10675v1 Announce Type: new Abstract: Event cameras offer distinct advantages over conventional frame-based sensors, including microsecond-level temporal resolution, high dynamic range, and

Neuroscience-Inspired Analyses of Visual Interestingness in Multimodal Transformers

arXiv:2605.08188v1 Announce Type: cross Abstract: Human attention is the gateway to conscious perception, memory and decision-making. However, its role in modern transformer models remains largely une

Nix and Fix: Targeting 1000x Compression of 3D Gaussian Splatting with Diffusion Models

arXiv:2602.04549v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) revolutionized novel view rendering. Instead of inferring from dense spatial points, as implicit representations do, 3D

NoiseRater: Meta-Learned Noise Valuation for Diffusion Model Training

arXiv:2605.08144v1 Announce Type: cross Abstract: Diffusion models have achieved remarkable success across a wide range of generative tasks, yet their training paradigm largely treats injected noise a

NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning

arXiv:2605.08221v1 Announce Type: cross Abstract: This paper presents NoisyCoconut, a novel inference-time method that enhances large language model (LLM) reliability by manipulating internal represen

Non-intrusive Body Composition Assessment from Full-body mmWave Scans

arXiv:2605.08306v1 Announce Type: cross Abstract: Body composition assessment (BCA) provides detailed information about the distribution of different tissue types in the body, enabling more precise ch

Non-Parametric Rehearsal Learning via Conditional Mean Embeddings

arXiv:2605.08999v1 Announce Type: new Abstract: In machine learning, a critical class of decision-related problems concerns preventing predicted undesirable outcomes, referred to as the extit{avoiding

Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials

arXiv:2605.09058v1 Announce Type: cross Abstract: We introduce Nonlinear GENERIC Informed Neural Networks (N-GINNs), a deep learning framework for discovering evolution equations of systems governed b

Not-So-Strange Love: Language Models and Generative Linguistic Theories are More Compatible than They Appear

arXiv:2605.10061v1 Announce Type: cross Abstract: Futrell and Mahowald (2025) frame the success of neural language models (LMs) as supporting gradient, usage-based linguistic theories. I argue that LM

Objective-Specific Privileged Bases via Full-Prefix Matryoshka Learning

arXiv:2605.09160v1 Announce Type: new Abstract: Learned representations are often invariant to rotational transformations, leaving individual dimensions non-identifiable and interchangeable. We study

OCP-GN: A Scalable Second-order Optimizer for Stochastic Optimization

arXiv:2512.24552v2 Announce Type: replace Abstract: This paper proposes a novel second-order optimization algorithm based on the Optimal Control Principle (OCP), applicable to large-scale optimization

On Characterizing Learnability for Adversarial Noisy Bandits

arXiv:2605.09200v1 Announce Type: new Abstract: We study adversarial noisy bandits given a known function class F. In each round, the adversary selects a function f in F, the learner chooses an arm, a

On Improving Graph Neural Networks for QSAR by Pre-training on Extended-Connectivity Fingerprints

arXiv:2605.10722v1 Announce Type: new Abstract: Molecular Graph Neural Networks (GNNs) are increasingly common in drug discovery, particularly for Quantitative Structure-Activity Relationship (QSAR) s

On periodic distributed representations using Fourier embeddings

arXiv:2605.10818v1 Announce Type: new Abstract: Periodic signals are critical for representing physical and perceptual phenomena. Scalar, real angular measures, e.g., radians and degrees, result in di

On the Convergence of Muon and Beyond

arXiv:2509.15816v5 Announce Type: replace Abstract: The Muon optimizer has demonstrated remarkable empirical success in handling matrix-structured parameters for training neural networks. However, a s

On the Convergence Rate of LoRA Gradient Descent

arXiv:2512.18248v3 Announce Type: replace Abstract: The low-rank adaptation (LoRA) algorithm for fine-tuning large models has grown popular in recent years due to its remarkable performance and low co

On the global convergence of gradient descent for wide shallow models with bounded nonlinearities

arXiv:2605.10775v1 Announce Type: cross Abstract: A surprising phenomenon in the training of neural networks is the ability of gradient descent to find global minimizers of the training loss despite i

One-step Latent-free Image Generation with Pixel Mean Flows

arXiv:2601.22158v3 Announce Type: replace Abstract: Modern diffusion/flow-based models for image generation typically exhibit two core characteristics: (i) using multi-step sampling, and (ii) operatin

One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory

arXiv:2505.23617v3 Announce Type: replace-cross Abstract: Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time p

Online Set Learning from Precision and Recall Feedback

arXiv:2605.09565v1 Announce Type: new Abstract: We consider the problem of learning an unknown subset N_ext{target} of a domain in an online setting. In each round t, the learner predicts a set of ite

Only Train Once: Uncertainty-Aware One-Class Learning for Face Authenticity Detection

arXiv:2605.10040v1 Announce Type: new Abstract: The rapid evolution of generative paradigms has enabled the creation of highly realistic imagery, which escalating the risks of identity fraud and the d

Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions

arXiv:2511.01292v2 Announce Type: replace-cross Abstract: Pretrained Transformers can perform in-context learning (ICL) from a few demonstrations, but this ability can fail sharply when the test distr

Optimal Regret for Single Index Bandits

arXiv:2605.09454v1 Announce Type: cross Abstract: We study the extit{single-index bandit} problem, where rewards depend on an unknown one-dimensional projection of high-dimensional contexts through an

Optimizer-Induced Mode Connectivity: From AdamW to Muon

arXiv:2605.09991v1 Announce Type: new Abstract: Mode connectivity has been widely studied, yet the role of the optimizer remains underexplored. We revisit it through optimizer-induced implicit regular

Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networks

arXiv:2605.09813v1 Announce Type: cross Abstract: We investigate the control and optimization of vertical federated learning (VFL), a class of distributed machine learning (ML) methods in which edge/f

ORICF -- Open Robotics Inference and Control Framework

arXiv:2605.09656v1 Announce Type: new Abstract: Recent advances in artificial intelligence (AI) have enabled effective perception and language models for robots, but their deployment remains computati

researcharxiv-cs-ro

OsteoFlow: Lyapunov-Guided Flow Distillation for Predicting Bone Remodeling after Mandibular Reconstruction

arXiv:2603.22421v2 Announce Type: replace Abstract: Predicting long-term bone remodeling after mandibular reconstruction would be of great clinical benefit, yet standard generative models struggle to

OUIDecay: Adaptive Layer-wise Weight Decay for CNNs Using Online Activation Patterns

arXiv:2605.10161v1 Announce Type: new Abstract: Weight decay remains one of the most widely used regularization mechanisms for training convolutional neural networks, yet it is still commonly applied

Outlier detection for patient monitoring and alerting

arXiv:2605.08955v1 Announce Type: new Abstract: We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management decisions using past patient cases stored in electro

Overcoming Multi-step Complexity in Multimodal Theory-of-Mind Reasoning: A Scalable Bayesian Planner

arXiv:2506.01301v2 Announce Type: replace Abstract: Theory-of-Mind (ToM) enables humans to infer mental states-such as beliefs, desires, and intentions-forming the foundation of social cognition. Howe

OZ-TAL: Online Zero-Shot Temporal Action Localization

arXiv:2605.09976v1 Announce Type: new Abstract: Online Temporal Action Localization (On-TAL) aims to detect the occurrence time and category of actions in untrimmed streaming videos immediately upon t

P-Flow: Proxy-gradient Flows for Linear Inverse Problems

arXiv:2605.08328v1 Announce Type: cross Abstract: Generative models based on flow matching have emerged as a powerful paradigm for inverse problems, offering straighter trajectories and faster samplin

PaceVGGT: Pre-Alternating-Attention Token Pruning for Visual Geometry Transformers

arXiv:2605.08371v1 Announce Type: new Abstract: Visual Geometry Transformer (VGGT) is a strong feed-forward model for multiple 3D tasks, but its Alternating-Attention (AA) stack scales quadratically i

PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation

arXiv:2605.09036v1 Announce Type: new Abstract: Accurate and efficient storm-surge emulation is essential for coastal hazard assessment, yet high-fidelity hydrodynamic models remain too expensive for

PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding

arXiv:2605.08632v1 Announce Type: cross Abstract: Speculative decoding accelerates Large Language Models (LLMs) inference by using a lightweight draft model to propose candidate tokens that are verifi

PathISE: Learning Informative Path Supervision for Knowledge Graph Question Answering

arXiv:2605.10791v1 Announce Type: new Abstract: Knowledge Graph Question Answering (KGQA) aims to answer user questions by reasoning over Knowledge Graphs (KGs). Recent KGQA methods mainly follow the

Perceptual Asymmetry Between Hue Categories: Evidence from Human Color Categorization

arXiv:2605.09339v1 Announce Type: cross Abstract: Human color categories are not uniformly distributed in perceptual space, yet most computational color models still assume fixed and evenly structured

Performance and Energy Trade-Off Analysis of Hierarchical Federated Learning for Plant Disease Classification

arXiv:2605.08121v1 Announce Type: cross Abstract: Early detection of plant diseases is critical for improving crop productivity, while it also facilitates the foundations of precision agriculture. Rec

PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection

arXiv:2605.09319v1 Announce Type: new Abstract: With the proliferation of AI-generated images, digital watermarking has become an essential safeguard for protecting intellectual property and mitigatin

Phase Transitions in Affective Meaning Divergence: The Hidden Drift Before the Break

arXiv:2605.09043v1 Announce Type: cross Abstract: One partner says 'Fine' meaning resolution; the other hears surrender. The word is shared; the affective uptake is not. We formalize this as affective