Search: “papers”

The Semi-Executable Stack: Agentic Software Engineering and the Expanding Scope of SE

arXiv:2604.15468v1 Announce Type: cross Abstract: AI-based systems, currently driven largely by LLMs and tool-using agentic harnesses, are increasingly discussed as a possible threat to software engin

agentsarxiv-cs-ai

20 Apr 2026

Safety

Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions

DGX agent

arXiv:2510.04237v5 Announce Type: replace Abstract: In this paper, we propose a novel kernel stochastic gradient descent (SGD) algorithm for large-scale supervised learning with general losses. Compar

safetyarxiv-cs-lg

20 Apr 2026

Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach

arXiv:2503.07976v2 Announce Type: replace-cross Abstract: This paper investigates approximation capabilities of two-dimensional (2D) deep convolutional neural networks (CNNs), with Korobov functions s

20 Apr 2026

A Nonlinear Separation Principle: Applications to Neural Networks, Control and Learning

arXiv:2604.15238v1 Announce Type: cross Abstract: This paper investigates continuous-time and discrete-time firing-rate and Hopfield recurrent neural networks (RNNs), with applications in nonlinear co

AI-Assisted Peer Review at Scale: The AAAI-26 AI Review Pilot

arXiv:2604.13940v1 Announce Type: new Abstract: Scientific peer review faces mounting strain as submission volumes surge, making it increasingly difficult to sustain review quality, consistency, and t

AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models

arXiv:2505.10846v3 Announce Type: replace Abstract: This paper presents AutoRAN, the first framework to automate the hijacking of internal safety reasoning in large reasoning models (LRMs). At its cor

Dark & Stormy: Modeling Humor in Sentences from the Bulwer-Lytton Fiction Contest

arXiv:2510.24538v2 Announce Type: replace Abstract: Textual humor is enormously diverse and computational studies need to account for this range, including intentionally bad humor. In this paper, we c

researcharxiv-cs-cl

Gaussian Process Regression of Steering Vectors With Physics-Aware Deep Composite Kernels for Augmented Listening

arXiv:2509.02571v2 Announce Type: replace-cross Abstract: This paper investigates continuous representations of steering vectors over frequency and microphone/source positions for augmented listening

Heat and Matern Kernels on Matchings

arXiv:2604.14331v1 Announce Type: new Abstract: Applying kernel methods to matchings is challenging due to their discrete, non-Euclidean nature. In this paper, we develop a principled framework for co

safetyarxiv-cs-lg

Hijacking online reviews: sparse manipulation and behavioral buffering in popularity-biased rating systems

arXiv:2604.13049v1 Announce Type: cross Abstract: Online reviews and recommendation systems help users navigate overwhelming choice, but they are vulnerable to self-reinforcing distortions. This paper

agentsarxiv-cs-ai

Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning

arXiv:2407.00809v3 Announce Type: replace Abstract: This paper introduces the Kernel Neural Operator (KNO), a provably convergent operator-learning architecture that utilizes compositions of deep kern

LeapAlign: Post-Training Flow Matching Models at Any Generation Step by Building Two-Step Trajectories

arXiv:2604.15311v1 Announce Type: new Abstract: This paper focuses on the alignment of flow matching models with human preferences. A promising way is fine-tuning by directly backpropagating reward gr

safetyarxiv-cs-cv

MEBench: A Novel Benchmark for Understanding Mutual Exclusivity Bias in Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2505.20122v2 Announce Type: replace Abstract: This paper introduces MEBench, a novel benchmark for evaluating mutual exclusivity (ME) bias, a cognitive phenomenon observed in children during wor

Modeling LLM Unlearning as an Asymmetric Two-Task Learning Problem

arXiv:2604.14808v1 Announce Type: new Abstract: Machine unlearning for large language models (LLMs) aims to remove targeted knowledge while preserving general capability. In this paper, we recast LLM

safetyarxiv-cs-cl

NLP needs Diversity outside of 'Diversity'

arXiv:2604.14595v1 Announce Type: new Abstract: This position paper argues that recent progress with diversity in NLP is disproportionately concentrated on a small number of areas surrounding fairness

safetyarxiv-cs-cl

NTIRE 2026 Challenge on Video Saliency Prediction: Methods and Results

arXiv:2604.14816v1 Announce Type: new Abstract: This paper presents an overview of the NTIRE 2026 Challenge on Video Saliency Prediction. The goal of the challenge participants was to develop automati

researcharxiv-cs-cv

Portfolio Optimization Proxies under Label Scarcity and Regime Shifts via Bayesian and Deterministic Students under Semi-Supervised Sandwich Training

arXiv:2604.14206v1 Announce Type: new Abstract: This paper proposes a machine learning assisted portfolio optimization framework designed for low data environments and regime uncertainty. We construct

Prism: Symbolic Superoptimization of Tensor Programs

arXiv:2604.15272v1 Announce Type: cross Abstract: This paper presents Prism, the first symbolic superoptimizer for tensor programs. The key idea is sGraph, a symbolic, hierarchical representation that

Style Amnesia: Investigating Speaking Style Degradation and Mitigation in Multi-Turn Spoken Language Models

arXiv:2512.23578v3 Announce Type: replace Abstract: In this paper, we show that when spoken language models (SLMs) are instructed to speak in a specific speaking style at the beginning of a multi-turn

researcharxiv-cs-cl

The Fourth Challenge on Image Super-Resolution (imes4) at NTIRE 2026: Benchmark Results and Method Overview

model-releasesarxiv-cs-cv

arXiv:2604.14558v1 Announce Type: new Abstract: This paper presents the NTIRE 2026 image super-resolution (imes4) challenge, one of the associated competitions of the NTIRE 2026 Workshop at CVPR 2026.

TokenLight: Precise Lighting Control in Images using Attribute Tokens

arXiv:2604.15310v1 Announce Type: new Abstract: This paper presents a method for image relighting that enables precise and continuous control over multiple illumination attributes in a photograph. We

researcharxiv-cs-cv

Trajectory Planning for a Multi-UAV Rigid-Payload Cascaded Transportation System Based on Enhanced Tube-RRT*

arXiv:2604.15074v1 Announce Type: new Abstract: This paper presents a two-stage trajectory planning framework for a multi-UAV rigid-payload cascaded transportation system, aiming to address planning c

safetyarxiv-cs-ro

Universal hidden monotonic trend estimation with contrastive learning

arXiv:2210.09817v3 Announce Type: replace Abstract: In this paper, we describe a universal method for extracting the underlying monotonic trend factor from time series data. We propose an approach rel

Unsupervised feature selection using Bayesian Tucker decomposition

arXiv:2604.14949v1 Announce Type: cross Abstract: In this paper, we proposed Bayesian Tucker decomposition (BTuD) in which residual is supposed to obey Gaussian distribution analogous to linear regres

VeruSAGE: A Study of Agent-Based Verification for Rust Systems

arXiv:2512.18436v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown impressive capability to understand and develop code. However, their capability to rigorously reason a

A Comparative Study of Dynamic Programming and Reinforcement Learning in Finite Horizon Dynamic Pricing

arXiv:2604.14059v1 Announce Type: cross Abstract: This paper provides a systematic comparison between Fitted Dynamic Programming (DP), where demand is estimated from data, and Reinforcement Learning (

Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version

arXiv:2604.13147v1 Announce Type: cross Abstract: This paper studies continuous-time stochastic control problems whose controlled states are fully non-Markovian and depend on unknown model parameters.

CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation

arXiv:2604.13946v1 Announce Type: cross Abstract: Automated code generation remains a persistent challenge in software engineering, as conventional multi-agent frameworks are often constrained by stat

Depth-Aware Image and Video Orientation Estimation

arXiv:2604.13995v1 Announce Type: new Abstract: This paper introduces a novel approach for image and video orientation estimation by leveraging depth distribution in natural images. The proposed metho

safetyarxiv-cs-cv

Evaluating the Evaluator: Problems with SemEval-2020 Task 1 for Lexical Semantic Change Detection

arXiv:2604.13232v1 Announce Type: new Abstract: This discussion paper re-examines SemEval-2020 Task 1, the most influential shared benchmark for lexical semantic change detection, through a three-part

HUANet: Hard-Constrained Unrolled ADMM for Constrained Convex Optimization

arXiv:2604.13179v1 Announce Type: cross Abstract: This paper presents HUANet, a constrained deep neural network architecture that unrolls the iterations of the Alternating Direction Method of Multipli

Hybrid Retrieval for COVID-19 Literature: Comparing Rank Fusion and Projection Fusion with Diversity Reranking

arXiv:2604.13728v1 Announce Type: cross Abstract: We present a hybrid retrieval system for COVID-19 scientific literature, evaluated on the TREC-COVID benchmark (171,332 papers, 50 expert queries). Th

LongVideo-R1: Smart Navigation for Low-cost Long Video Understanding

model-releasesarxiv-cs-cv

arXiv:2602.20913v2 Announce Type: replace Abstract: This paper addresses the critical and underexplored challenge of long video understanding with low computational budgets. We propose LongVideo-R1, a

Lossless Prompt Compression via Dictionary-Encoding and In-Context Learning: Enabling Cost-Effective LLM Analysis of Repetitive Data

arXiv:2604.13066v1 Announce Type: new Abstract: In-context learning has established itself as an important learning paradigm for Large Language Models (LLMs). In this paper, we demonstrate that LLMs c

MixAtlas: Uncertainty-aware Data Mixture Optimization for Multimodal LLM Midtraining

researchapple-ml-research

This paper was accepted at the Workshop on Navigating and Addressing Data Problems for Foundation Models (NADPFM) at ICLR 2026. Principled domain reweighting can substantially improve sample efficienc

RANDPOL: Parameter-Efficient End-to-End Quadruped Locomotion via Randomized Policy Learning

arXiv:2505.19054v2 Announce Type: replace Abstract: Modern learning-based locomotion controllers typically rely on fully trainable deep neural networks with a large number of parameters. This paper st

Restless Bandits with Individual Penalty Constraints: A New Near-Optimal Index Policy and How to Learn It

arXiv:2604.04101v2 Announce Type: replace Abstract: This paper investigates the Restless Multi-Armed Bandit (RMAB) framework under individual penalty constraints to address resource allocation challen

safetyarxiv-cs-lg

Sandpile Economics: Theory, Identification, and Evidence

arXiv:2604.13890v1 Announce Type: cross Abstract: Why do capitalist economies recurrently generate crises whose severity is disproportionate to the size of the triggering shock? This paper proposes a

agentsarxiv-cs-lg

Simulation-Based Optimisation of Batting Order and Bowling Plans in T20 Cricket

arXiv:2604.13861v1 Announce Type: new Abstract: This paper develops a unified Markov Decision Process (MDP) framework for optimising two recurring in-match decisions in T20 cricket namely batting orde

Structure- and Stability-Preserving Learning of Port-Hamiltonian Systems

arXiv:2604.13297v1 Announce Type: cross Abstract: This paper investigates the problem of data-driven modeling of port-Hamiltonian systems while preserving their intrinsic Hamiltonian structure and sta

Text-as-Signal: Quantitative Semantic Scoring with Embeddings, Logprobs, and Noise Reduction

arXiv:2604.13056v1 Announce Type: new Abstract: This paper presents a practical pipeline for turning text corpora into quantitative semantic signals. Each news item is represented as a full-document e

Tutorials

Tokenizing Semantic Segmentation with Run Length Encoding

arXiv:2602.21627v3 Announce Type: replace Abstract: This paper presents a new unified approach to semantic segmentation in both images and videos by using language modeling to output the masks as sequ

tutorialsarxiv-cs-cv

Towards Multi-Object-Tracking with Radar on a Fast Moving Vehicle: On the Potential of Processing Radar in the Frequency Domain

arXiv:2604.14013v1 Announce Type: cross Abstract: We promote in this paper the processing of radar data in the frequency domain to achieve higher robustness against noise and structural errors, especi

agentsarxiv-cs-cv

Utilizing Inpainting for Keypoint Detection for Vision-Based Control of Robotic Manipulators

arXiv:2604.13309v1 Announce Type: new Abstract: In this paper we present a novel visual servoing framework to control a robotic manipulator in the configuration space by using purely natural visual fe

researcharxiv-cs-ro

A Bipartite Graph Approach to U.S.-China Cross-Market Return Forecasting

arXiv:2603.10559v2 Announce Type: replace Abstract: This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-ove

A General Model for Deepfake Speech Detection: Diverse Bonafide Resources or Diverse AI-Based Generators

arXiv:2603.27557v2 Announce Type: replace-cross Abstract: In this paper, we analyze two main factors of Bonafide Resource (BR) or AI-based Generator (AG) which affect the performance and the generalit

Applications

A Geometric Algebra-informed NeRF Framework for Generalizable Wireless Channel Prediction

arXiv:2604.11983v1 Announce Type: cross Abstract: In this paper, we propose the geometric algebra-informed neural radiance fields (GAI-NeRF), a novel framework for wireless channel prediction that lev

applicationsarxiv-cs-lg

Bilevel Late Acceptance Hill Climbing for the Electric Capacitated Vehicle Routing Problem

arXiv:2604.13013v1 Announce Type: new Abstract: This paper tackles the Electric Capacitated Vehicle Routing Problem (E-CVRP) through a bilevel optimization framework that handles routing and charging