Search: “papers”

Exploring the System 1 Thinking Capability of Large Reasoning Models

model-releasesarxiv-cs-cl

arXiv:2504.10368v4 Announce Type: replace Abstract: This paper explores the system 1 thinking capability of Large Reasoning Models (LRMs), the intuitive ability to respond efficiently with minimal tok

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Last-Iterate Convergence of General Parameterized Policies in Constrained MDPs

DGX agent

arXiv:2408.11513v2 Announce Type: replace Abstract: This paper focuses on learning a Constrained Markov Decision Process (CMDP) via general parameterized policies. We propose a Primal-Dual based Regul

safetyarxiv-cs-lg

4 May 2026

Learning the Helmholtz equation operator with DeepONet for non-parametric 2D geometries

arXiv:2605.00760v1 Announce Type: new Abstract: This paper deals with solving the 2D Helmholtz equation on non-parametric domains, leveraging a physics-informed neural operator network based on the De

local-aiarxiv-cs-lg

Let ViT Speak: Generative Language-Image Pre-training

arXiv:2605.00809v1 Announce Type: new Abstract: In this paper, we present extbf{Gen}erative extbf{L}anguage-extbf{I}mage extbf{P}re-training (GenLIP), a minimalist generative pretraining framework for

researcharxiv-cs-cv

Position: agentic AI orchestration should be Bayes-consistent

arXiv:2605.00742v1 Announce Type: cross Abstract: LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which to

agentsarxiv-cs-lg

Recovering Hidden Reward in Diffusion-Based Policies

arXiv:2605.00623v1 Announce Type: new Abstract: This paper introduces EnergyFlow, a framework that unifies generative action modeling with inverse reinforcement learning by parameterizing a scalar ene

safetyarxiv-cs-ro

A Grid-Aware Agent-Based Model for Analyzing Electric Vehicle Charging Systems

arXiv:2604.27849v1 Announce Type: new Abstract: This paper presents a configurable, grid-aware Agent-Based Model (ABM) for the systematic analysis of electric vehicle (EV) charging systems under confi

agentsarxiv-cs-ai

Attractor FCM

arXiv:2604.27947v1 Announce Type: cross Abstract: In this paper an attractor FCM is created, tested, and analyzed. This FCM is neither a hebbian based nor agentic, nor a hybrid; it rather is a gradien

local-aiarxiv-cs-ai

AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data

arXiv:2604.27266v1 Announce Type: new Abstract: This paper introduces AutoREC, an open-source Python package for developing reinforcement learning (RL) agents to automatically generate equivalent circ

agentsarxiv-cs-lg

Continuous-time q-learning for mean-field control with common noise, part-I: Theoretical foundations

arXiv:2604.27372v1 Announce Type: cross Abstract: This paper investigates the continuous-time counterpart of the Q-function for entropy-regularized mean-field control (MFC) with controlled common nois

Cross-Lingual Response Consistency in Large Language Models: An ILR-Informed Evaluation of Claude Across Six Languages

model-releasesarxiv-cs-cl

arXiv:2604.27137v1 Announce Type: new Abstract: This paper introduces a systematic evaluation framework grounded in the Interagency Language Roundtable (ILR) Skill Level Descriptions and applies it to

Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA

arXiv:2604.27994v1 Announce Type: new Abstract: Learned driving agents often degrade when deployed in unseen environments. This paper studies a deliberately bounded instance of that problem in the CAR

safetyarxiv-cs-ro

Effective Prompt Pool Learning for Continual Category Discovery

arXiv:2407.19001v3 Announce Type: replace Abstract: This paper studies effective prompt pool learning for Continual Category Discovery (CCD), a challenging open-world setting where a model must discov

local-aiarxiv-cs-cv

Exploring Interaction Paradigms for LLM Agents in Scientific Visualization

model-releasesarxiv-cs-ai

arXiv:2604.27996v1 Announce Type: new Abstract: This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks, where users generate vi

Flying by Inference: Active Inference World Models for Adaptive UAV Swarms

arXiv:2604.27935v1 Announce Type: new Abstract: This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-

agentsarxiv-cs-ro

Hardware

GraphMend: Code Transformations for Fixing Graph Breaks in PyTorch 2

arXiv:2509.16248v3 Announce Type: replace-cross Abstract: This paper presents GRAPHMEND, a high-level compiler technique that eliminates FX graph breaks in PyTorch 2 programs. Although PyTorch 2 intro

hardwarearxiv-cs-lg

How Hard Is Continuous Clustering? Lower Bounds from the Existential Theory of the Reals

arXiv:2604.26972v1 Announce Type: cross Abstract: This paper studies the computational difficulty of clustering problems that are defined directly on a continuous probability density. Rather than work

Applications

Interaction Forces and Internal Loads in Parallel Manipulators with Actuation Redundancy

arXiv:2604.27095v1 Announce Type: new Abstract: This paper discusses null-space wrench components in parallel manipulators. We examine the adaptation of the two most common characterizations of these

applicationsarxiv-cs-ro

LA-Pose: Latent Action Pretraining Meets Pose Estimation

arXiv:2604.27448v1 Announce Type: new Abstract: This paper revisits camera pose estimation through the lens of self-supervised pretraining, focusing on inverse-dynamics pretraining as a scalable alter

safetyarxiv-cs-cv

Learning to Aggregate Zero-Shot LLM Agents for Corporate Disclosure Classification

arXiv:2603.20965v2 Announce Type: replace-cross Abstract: This paper studies whether a lightweight supervised aggregator can combine diverse zero-shot large language model outputs into a stronger down

Limit Theorems for Stochastic Gradient Descent in High-Dimensional Single-Layer Networks

arXiv:2511.02258v2 Announce Type: replace-cross Abstract: This paper studies the high-dimensional scaling limits of online stochastic gradient descent (SGD). Building on the recent work of Ben Arous,

Mechanized Foundations of Structural Governance: Machine-Checked Proofs for Governed Intelligence

arXiv:2604.27289v1 Announce Type: new Abstract: We present five results in the theory of structural governance for cognitive workflow systems. Three are mechanized in Coq 8.19 using the Interaction Tr

safetyarxiv-cs-ai

Online semi-supervised perception: Real-time learning without explicit feedback

arXiv:2604.27562v1 Announce Type: new Abstract: This paper proposes an algorithm for real-time learning without explicit feedback. The algorithm combines the ideas of semi-supervised learning on graph

Optimal Stop-Loss and Take-Profit Parameterization for Autonomous Trading Agent Swarm

arXiv:2604.27150v1 Announce Type: new Abstract: Autonomous crypto trading systems often spend most of their design effort on finding entries, while exits are left to fixed rules that are rarely tested

agentsarxiv-cs-ai

Physically-Informed Fuzzy Clustering of Vertical Sounding Ionograms

arXiv:2604.27721v1 Announce Type: cross Abstract: This paper presents a physically-informed fuzzy clustering of vertical sounding ionograms for automatically separating the ionogram into tracks suitab

researcharxiv-cs-cv

Supercharging Agenda Setting Research: The ParlaCAP Dataset of 28 European Parliaments and a Scalable Multilingual LLM-Based Classification

arXiv:2602.16516v2 Announce Type: replace Abstract: This paper introduces ParlaCAP, a large-scale dataset for analyzing parliamentary agenda setting across Europe, and proposes a cost-effective method

safetyarxiv-cs-cl

Think it, Run it: Autonomous ML pipeline generation via self-healing multi-agent AI

arXiv:2604.27096v1 Announce Type: new Abstract: The purpose of our paper is to develop a unified multi-agent architecture that automates end-to-end machine learning (ML) pipeline generation from datas

agentsarxiv-cs-ai

Applications

Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure

arXiv:2604.26983v1 Announce Type: cross Abstract: This paper presents a novel value-aware approach to product recommendation that simultaneously addresses the high dimensionality and sparsity of user-

applicationsarxiv-cs-lg

Why Mean Pooling Works: Quantifying Second-Order Collapse in Text Embeddings

arXiv:2604.27398v1 Announce Type: new Abstract: For constructing text embeddings, mean pooling, which averages token embeddings, is the standard approach. This paper examines whether mean pooling actu

researcharxiv-cs-cl

2D and 3D Grasp Planners for the GET Asymmetrical Gripper

arXiv:2604.26212v1 Announce Type: new Abstract: In this paper, we introduce GET-2D-1.0, a fast grasp planner for the GET asymmetrical gripper that operates from a single-view RGB-D image, using the Fe

researcharxiv-cs-ro

A Diffeomorphism Groupoid and Algebroid Framework for Discontinuous Image Registration

arXiv:2603.11806v2 Announce Type: replace-cross Abstract: In this paper, we propose a novel mathematical framework for piecewise diffeomorphic image registration that involves discontinuous sliding mo

researcharxiv-cs-cv

Adversarial Robustness of NTK Neural Networks

arXiv:2604.25965v1 Announce Type: cross Abstract: Deep learning models are widely deployed in safety-critical domains, but remain vulnerable to adversarial attacks. In this paper, we study the adversa

Bi-Level Optimization for Contact and Motion Planning in Rope-Assisted Legged Robots

arXiv:2604.26910v1 Announce Type: new Abstract: This paper presents a planning pipeline framework for locomotion in rope-assisted robots climbing vertical surfaces. The proposed framework is formulate

researcharxiv-cs-ro

BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets

arXiv:2604.26048v1 Announce Type: new Abstract: This paper presents a principled and scalable framework for systematically generating complex Question Answering (QA) data. In the core of this framewor

researcharxiv-cs-cl

ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models

arXiv:2405.13729v3 Announce Type: replace-cross Abstract: In this paper, we study an under-explored but important factor of diffusion generative models, i.e., the combinatorial complexity. Data sample

Deep-testing: the case of dependence detection

arXiv:2604.26558v1 Announce Type: cross Abstract: Deep learning methods have proved highly effective for classification and image recognition problems. In this paper, we ask whether this success can b

Failure Modes of Maximum Entropy RLHF

arXiv:2509.20265v3 Announce Type: replace-cross Abstract: In this paper, we show that Simple Preference Optimization (SimPO) can be derived as Maximum Entropy Reinforcement Learning, providing a theor

researcharxiv-cs-cl

Graph Construction and Matching for Imperative Programs using Neural and Structural Methods

arXiv:2604.26578v1 Announce Type: cross Abstract: Reusing verification artefacts requires identifying structural and semantic similarities across programs and their specifications. In this paper, we f

Tutorials

HOI-aware Adaptive Network for Weakly-supervised Action Segmentation

arXiv:2604.26227v1 Announce Type: new Abstract: In this paper, we propose an HOI-aware adaptive network named AdaAct for weakly-supervised action segmentation. Most existing methods learn a fixed netw

tutorialsarxiv-cs-cv

Multi-Periodogram Velocity Estimation with Irregular Reference Signals for Robot-Aided ISAC

arXiv:2604.25974v1 Announce Type: new Abstract: This paper addresses velocity estimation within robot-aided integrated sensing and communications (ISAC), where mobile robots act as sensing nodes but c

researcharxiv-cs-ro

Multiple Additive Neural Networks for Structured and Unstructured Data

arXiv:2604.26888v1 Announce Type: new Abstract: This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework,

NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results

model-releasesarxiv-cs-cv

arXiv:2604.04135v2 Announce Type: replace Abstract: This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and

Point Cloud Registration via Probabilistic Self-Update Local Correspondence and Line Vector Sets

arXiv:2604.26318v1 Announce Type: new Abstract: Point cloud registration (PCR) is a fundamental task for integrating 3D observations in remote sensing applications. This paper proposes a fast and effe

local-aiarxiv-cs-cv

Quantum Gatekeeper: Multi-Factor Context-Bound Image Steganography with VQC Based Key Derivation on Quantum Hardware

arXiv:2604.26413v1 Announce Type: cross Abstract: This paper presents Quantum Gatekeeper, a context-bound image steganography framework where successful payload recovery depends on both cryptographic

Recent Advances in mm-Wave and Sub-THz/THz Oscillators for FutureG Technologies

arXiv:2604.26903v1 Announce Type: cross Abstract: This paper provides a concise yet comprehensive review of recent advancements in millimeter-wave (mm-wave) oscillators below 100 GHz and sub-terahertz

Rule-based High-Level Coaching for Goal-Conditioned Reinforcement Learning in Search-and-Rescue UAV Missions Under Limited-Simulation Training

arXiv:2604.26833v1 Announce Type: cross Abstract: This paper presents a hierarchical decision-making framework for unmanned aerial vehicle (UAV) missions motivated by search-and-rescue (SAR) scenarios

safetyarxiv-cs-ai

Semi-supervised learning with max-margin graph cuts

arXiv:2604.26818v1 Announce Type: new Abstract: This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels

Text-Utilization for Encoder-dominated Speech Recognition Models

arXiv:2604.26514v1 Announce Type: cross Abstract: This paper investigates efficient methods for utilizing text-only data to improve speech recognition, focusing on encoder-dominated models that facili