Search: “research”

Navigation by Skylight Polarization via Rayleigh Model Optimization on a Road Vehicle

arXiv:2610.20797v1 Announce Type: new Abstract: Reliable absolute heading estimation is critical for road-vehicle navigation, particularly when magnetic, inertial, or GNSS-based references are degrade

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Research

No Single Tokenizer Feature Reliably Predicts Downstream Language Model Performance

DGX agent

arXiv:2610.21679v1 Announce Type: new Abstract: Tokenization is a critical component of the language modeling pipeline, but it is poorly understood. Tokenizers can impact downstream performance, but t

researcharxiv-cs-cl

16 Oct 2026

Nonconvex Composite Functional Constraints via First-Order Augmented Lagrangian Methods under Local Regularity

arXiv:2607.08954v2 Announce Type: replace-cross Abstract: We study nonasymptotic convergence of primal-dual methods for a class of nonconvex constrained optimization problems under local multiplier re

Not All Unlabeled Data Are Equal: Unlabeled Data Selection for Semi-Supervised QSAR Modeling with Large Chemical Repositories

arXiv:2610.22025v1 Announce Type: new Abstract: Semi-supervised learning (SSL) is particularly attractive in domains where labels are expensive to obtain, while unlabeled examples are readily availabl

Not X, but When: Diagnosing a Mandarin LLM Writing Tic through Contextual Appropriateness

arXiv:2610.22249v1 Announce Type: new Abstract: Recurring expressions in model-written text are often treated as signatures of machine authorship, yet most of them serve legitimate functions. Whether

On the Last-Iterate Convergence of Clipped Gradient Methods

arXiv:2610.21026v1 Announce Type: cross Abstract: In stochastic (strongly) convex optimization, establishing last-iterate convergence guarantees for first-order methods has recently garnered increasin

On the Stability and Convergence of Flow Map Training

arXiv:2610.20674v1 Announce Type: cross Abstract: Flow maps are a recent method of probabilistic generation that amortize diffusion trajectories into one-step samplers, unlocking efficient generation.

Online Quantile Omniprediction for Proper Losses

arXiv:2610.21342v1 Announce Type: new Abstract: The task of constructing a predictor that is simultaneously accurate under multiple evaluation metrics has been popularized recently under the name omni

Open-world AI: a Tale of Three Requirements

arXiv:2610.20782v1 Announce Type: new Abstract: The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examp

Optimal Condition for Initialization Variance in Deep Neural Networks: An SGD Dynamics Perspective

arXiv:2508.12834v2 Announce Type: replace-cross Abstract: Stochastic gradient descent (SGD), one of the most fundamental optimization algorithms in machine learning (ML), can be recast through a conti

Optimal Explicit and Implicit Regularization Strengths in High-Dimensional Continual Linear Regression

arXiv:2610.20804v1 Announce Type: new Abstract: We study continual linear regression in a high-dimensional regime, where a learner observes a sequence of tasks and updates its parameters sequentially.

PAC-Causal Learnability: From Identification to Finite-Sample Guarantees

arXiv:2507.18903v2 Announce Type: replace-cross Abstract: Causal identification asks whether a causal target can be recovered from the observational distribution in an idealized infinite-data setting.

Parallel Layer Normalization for Universal Approximation

arXiv:2505.13142v3 Announce Type: replace Abstract: Layer Normalization (LN) is usually studied for its role in optimization, but it also acts as a multivariate nonlinearity between affine layers. We

Patch where the text is new: a compression view of where byte-level models should spend compute

arXiv:2610.22100v1 Announce Type: new Abstract: Byte-level language models such as the Byte Latent Transformer (BLT) run their large global model once per patch and start a patch where a small model's

Path Planning on Multi-level Point Cloud with a Weighted Traversability Graph

arXiv:2504.21622v2 Announce Type: replace Abstract: This article proposes a new path planning method for addressing multi-level terrain situations. The proposed method includes innovations in three as

PathFM: Fast Generative Forecasting of Multivariate Trajectories

arXiv:2610.22358v1 Announce Type: new Abstract: Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles

Pathology foundation models for training-free case retrieval in salivary gland tumors

arXiv:2610.22087v1 Announce Type: new Abstract: In pathology, pattern recognition relies on a frame of reference built through extensive training and experience, which may still fall short when encoun

Perceive, Predict, and Plan: a Unified World Action Model for Egocentric Dexterous Manipulation

arXiv:2610.22363v1 Announce Type: new Abstract: We introduce P^{3}-WAM, a unified world action model for egocentric dexterous manipulation that learns 3D perception, action-conditioned prediction, and

Personalized Projected Ascent for Federated Stackelberg Optimization

arXiv:2610.21295v1 Announce Type: new Abstract: Federated minimax problems, such as distributionally robust optimization and adversarial training, include an inner maximization variable built from eac

PET Quantification-Preserving Registration for Longitudinal Whole-Body PSMA PET/CT

arXiv:2610.21451v1 Announce Type: new Abstract: We adapt the diffeomorphic coarse-to-fine LapIRN architecture for longitudinal whole-body PSMA PET/CT registration in the Learn2Reg 2026 PSMAReg challen

Phase Kernel Lifts Capacity of Dense Associative Memory

arXiv:2610.21493v1 Announce Type: new Abstract: Dense associative memory underlies modern attention mechanisms; however, Modern Hopfield Networks (MHNs) with linear kernels yield overlapping attractor

PiCo: Physics-Informed Concept Probing for Geo-Foundation Models

arXiv:2610.20973v1 Announce Type: new Abstract: Geo-foundation models (GFMs) are typically evaluated by downstream task predictive performance, revealing little about which physical features they are

Pixels to Tokens: Token Space Efficient Active Learning for Low-Budget Semantic Segmentation

arXiv:2610.20881v1 Announce Type: new Abstract: While foundation models and vision transformers (ViTs) have redefined visual representations, current active learning (AL) strategies for these backbone

Plans Are Not Executions: Adapting Frozen World-Model Planners to Controller-Induced Execution Shift

arXiv:2610.22041v1 Announce Type: new Abstract: World-model planners evaluate candidate trajectories by predicting their future consequences, implicitly relying on the plan-to-motion relationship lear

Poison Valleys: Diagnosing Path-Dependent Failure in LLM Code Repair

arXiv:2610.21514v1 Announce Type: cross Abstract: When code-repair agents fail, final tests reveal that a patch is wrong but not which earlier commitment caused it or which software obligation failed.

Predicting Live Birth After Fresh Embryo Transfer in IVF/ICSI: Differential ICM/Trophectoderm Morphology Encoding and a Hybrid Attention--Logistic Regression Ensemble

arXiv:2610.21134v1 Announce Type: new Abstract: Predicting live birth before fresh embryo transfer could support shared decision-making in IVF/ICSI, but routine clinical data are small, heterogeneous,

Private Retrieval Augmented Generation via Random Projection

arXiv:2610.22297v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) enhances the capabilities of large language models (LLMs) by querying external structured knowledge. However, it ca

Product Interaction: An Algebraic Formalism for Deep Learning Architectures

arXiv:2602.02573v2 Announce Type: replace-cross Abstract: Modern neural architectures are largely developed as distinct empirical constructions, despite recurring principles across convolutional, atte

Provably Efficient Risk-Sensitive Offline Reinforcement Learning via Optimized Certainty Equivalents

arXiv:2610.21870v1 Announce Type: cross Abstract: Risk-sensitive reinforcement learning (RL) addresses decision-making under uncertainty and adverse outcomes. Learning risk-sensitive policies from fix

Quantile-Guided Diffusion for Probabilistic Time-Series Forecasting

arXiv:2610.20739v1 Announce Type: new Abstract: Quantile forecasts summarize uncertainty at each future time step, while diffusion forecasters represent it through sampled future trajectories. We stud

RAG4Tickets: AI-Powered Ticket Resolution via Retrieval-Augmented Generation on JIRA and GitHub Data

arXiv:2510.08667v3 Announce Type: replace-cross Abstract: Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA t

Ranking and Partitioning of Attention Heads for Sparse and Continual Reinforcement Learning Fine-tuning

arXiv:2610.21336v1 Announce Type: cross Abstract: We study sparse and continual reinforcement learning fine-tuning of language models through attention-head ranking and partitioning. For sparse learni

Real-time Chatbot Romance Scam Detection in Stage Classification

arXiv:2610.20631v1 Announce Type: cross Abstract: Romance scam has become a serious problem via social media, and the improvement of artificial intelligence enable to fake identify easily. In the stud

Realtime-Bench: Can Frontier Models Solve Real-time Interactive Problems?

arXiv:2610.22155v1 Announce Type: new Abstract: Real-time interaction requires agents to perceive evolving environments and act under temporal constraints, yet existing evaluations of real-time stream

Reasoning without Gold Standards: A Proxy-Judge Theory of Autoformalization

arXiv:2606.09449v2 Announce Type: replace Abstract: Autoformalization (AF) requires both prover acceptance and fidelity to an informal source, but prover acceptance alone cannot establish fidelity, an

Reducing the Cost of Qudit State Preparation by Learning a Sparse Structure for SNAP Gates

arXiv:2610.21557v1 Announce Type: cross Abstract: Preparing quantum states in bosonic qudits with SNAP--Displacement sequences requires both optimizing the gate parameters and deciding which Fock leve

Reference-Guided Turn-Taking Style Transfer for Full-Duplex Spoken Language Models

arXiv:2610.20763v1 Announce Type: cross Abstract: Full-duplex spoken language models decide at every frame whether to take the floor, defining a conversational personality -- yet each set of weights e

Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means

arXiv:2610.21273v1 Announce Type: new Abstract: We study the problem of online learning of quantum states with logarithmic loss, which is a quantum generalization of online portfolio selection and has

ResStreamGS: Residual-Sensitive Dual-Stream Gaussian Splatting for Mobile Volumetric Video Streaming

arXiv:2610.21121v1 Announce Type: cross Abstract: Real-time mobile volumetric video enables immersive free-viewpoint experiences, but delivering high-quality 3D content under constrained bandwidth and

ReState: Noise-Level-Aware State Correction for Training-Free Object Removal in Few-Step Diffusion Models

arXiv:2610.21287v1 Announce Type: new Abstract: Training-free object removal commonly redirects self-attention away from the target region, but on few-step distilled diffusion models this often leaves

ReText: Text Boosts Generalization in Image-Based Person Re-identification

arXiv:2602.05785v2 Announce Type: replace-cross Abstract: Generalizable image-based person re-identification (Re-ID) aims to recognize individuals across cameras in unseen domains without retraining.

Rethinking Routing Exploration in Modern Mixture-of-Experts

arXiv:2610.22353v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two desi

Rethinking Successive Rejects Algorithms

arXiv:2610.22051v1 Announce Type: cross Abstract: In the best-arm identification problem, an algorithm adaptively selects arms to find the arm of the largest mean reward. Successive Rejects (SR) is a

Agents

ReviewPilot: A Multi-Agent System for Screening and Data Extraction in Systematic Reviews

arXiv:2610.21042v1 Announce Type: cross Abstract: Systematic literature reviews follow the PRISMA process of screening scholarly works and extracting evidence from included studies. Deep research tool

agentsarxiv-cs-ai

RIGen: Beyond Action Following with Reciprocal Interaction in Generative Gameplay

arXiv:2610.20811v1 Announce Type: new Abstract: Generative gameplay requires more than immediate responses to player actions.Actions should have persistent consequences, while the generated world shou

Robotic-supported Data Loss Detection in Android Applications

arXiv:2610.20807v1 Announce Type: cross Abstract: Smartphones have become integral to modern life due to their diverse applications. However, the development of applications for these devices faces si

Robust Covariance Estimation for Entry-wise Contaminated and Partially Observed Data via Expectation Maximization Algorithm

arXiv:2610.21709v1 Announce Type: cross Abstract: Robust covariance estimation under entry-wise contamination and missing data is challenging in various signal processing applications. Under the Stude

RUSplatting: Robust 3D Gaussian Splatting for Sparse-View Underwater Scene Reconstruction

arXiv:2505.15737v3 Announce Type: replace Abstract: Reconstructing high-fidelity underwater scenes remains a challenging task due to light absorption, scattering, and limited visibility inherent in aq