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On Pareto Optimality for Parametric Choice Bandits

arXiv:2501.19277v4 Announce Type: replace-cross Abstract: We study online assortment optimization under stochastic choice when a decision maker simultaneously values cumulative revenue performance and

On the Hybrid Nature of ABPMS Process Frames and its Implications on Automated Process Discovery

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DGX agent

arXiv:2604.22455v1 Announce Type: new Abstract: A core component of any AI-Augmented Business Process Management System (ABPMS) is the process frame, which gives the system process-awareness and defin

agentsarxiv-cs-ai

27 Apr 2026

On the Properties of Feature Attribution for Supervised Contrastive Learning

arXiv:2604.22540v1 Announce Type: cross Abstract: Most Neural Networks (NNs) for classification are trained using Cross-Entropy as a loss function. This approach requires the model to have an explicit

safetyarxiv-cs-ai

One Shot Learning for Edge Detection on Point Clouds

arXiv:2604.22354v1 Announce Type: new Abstract: Each scanner possesses its unique characteristics and exhibits its distinct sampling error distribution. Training a network on a dataset that includes d

tutorialsarxiv-cs-cv

Online Distributional Regression

arXiv:2407.08750v4 Announce Type: replace-cross Abstract: Large-scale streaming data are common in modern machine learning applications and have led to the development of online learning algorithms. M

applicationsarxiv-cs-lg

Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe

arXiv:2604.22676v1 Announce Type: new Abstract: Graph neural networks achieve strong node-classification accuracy, but their learned message passing entangles ego attributes, neighborhood smoothing, h

Optimal Lower Bounds for Online Multicalibration

arXiv:2601.05245v2 Announce Type: replace Abstract: We prove tight lower bounds for online multicalibration, establishing an information-theoretic separation from marginal calibration. In the general

Optimal Question Selection from a Large Question Bank for Clinical Field Recovery in Conversational Psychiatric Intake

model-releasesarxiv-cs-ai

arXiv:2604.22067v1 Announce Type: cross Abstract: Psychiatric intake is a sequential, high-stakes information-gathering process in which clinicians must decide what to ask, in what order, and how to i

Optimal sequential decision-making for error propagation mitigation in digital twins

arXiv:2604.22168v1 Announce Type: new Abstract: Here, we explore the problem of error propagation mitigation in modular digital twins as a sequential decision process. Building on a companion study th

OREN: Octree Residual Network for Real-Time Euclidean Signed Distance Mapping

arXiv:2510.18999v2 Announce Type: replace-cross Abstract: Reconstructing signed distance functions (SDFs) from point cloud data benefits many robot autonomy capabilities, including localization, mappi

researcharxiv-cs-ai

Outcome Rewards Do Not Guarantee Verifiable or Causally Important Reasoning

arXiv:2604.22074v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) on chain-of-thought reasoning has become a standard part of language model post-training recipes.

researcharxiv-cs-cl

Pack only the essentials: Adaptive dictionary learning for kernel ridge regression

arXiv:2604.22386v1 Announce Type: cross Abstract: One of the major limits of kernel ridge regression (KRR) is that storing and manipulating the kernel matrix K_n for n samples requires O(n^2) space, w

PAGaS: Pixel-Aligned 1DoF Gaussian Splatting for Depth Refinement

arXiv:2604.22129v1 Announce Type: new Abstract: Gaussian Splatting (GS) has emerged as an efficient approach for high-quality novel view synthesis. While early GS variants struggled to accurately mode

researcharxiv-cs-cv

Parameter-Efficient Conditioning for Material Generalization in Graph-Based Simulators

arXiv:2511.05456v2 Announce Type: replace Abstract: Graph network-based simulators (GNS) have demonstrated strong potential for learning particle-based physics (such as fluids, deformable solids, and

PASR: Pose-Aware 3D Shape Retrieval from Occluded Single Views

arXiv:2604.22658v1 Announce Type: new Abstract: Single-view 3D shape retrieval is a fundamental yet challenging task that is increasingly important with the growth of available 3D data. Existing appro

tutorialsarxiv-cs-cv

Performance Anomaly Detection in Athletics: A Benchmarking System with Visual Analytics

arXiv:2604.21953v1 Announce Type: new Abstract: Anti-doping programs rely on biological testing to detect performance-enhancing drugs, but such testing costs over $800 per sample and is limited by sho

PermaFrost-Attack: Stealth Pretraining Seeding(SPS) for planting Logic Landmines During LLM Training

arXiv:2604.22117v1 Announce Type: cross Abstract: Aligned large language models(LLMs) remain vulnerable to adversarial manipulation, and their dependence on web-scale pretraining creates a subtle but

safetyarxiv-cs-ai

PL-MTEB: Polish Massive Text Embedding Benchmark

model-releasesarxiv-cs-cl

arXiv:2405.10138v2 Announce Type: replace Abstract: In this paper, we introduce the Polish Massive Text Embedding Benchmark (PL-MTEB), a comprehensive benchmark for text embeddings in the Polish langu

Pliable rejection sampling

arXiv:2604.22385v1 Announce Type: cross Abstract: Rejection sampling is a technique for sampling from difficult distributions. However, its use is limited due to a high rejection rate. Common adaptive

tutorialsarxiv-cs-lg

Point & Grasp: Flexible Selection of Out-of-Reach Objects Through Probabilistic Cue Integration

arXiv:2604.22491v1 Announce Type: cross Abstract: Selecting out-of-reach objects is a fundamental task in mixed reality (MR). Existing methods rely on a single cue or deterministically fuse multiple c

researcharxiv-cs-ro

PoLO: Proof-of-Learning and Proof-of-Ownership at Once with Chained Watermarking

arXiv:2505.12296v2 Announce Type: replace-cross Abstract: Our evaluation shows that PoLO achieves extbf{99%} watermark detection accuracy for ownership verification, while preserving data privacy and

researcharxiv-cs-ai

PoseFM: Relative Camera Pose Estimation Through Flow Matching

arXiv:2604.22350v1 Announce Type: new Abstract: Monocular visual odometry (VO) is a fundamental computer vision problem with applications in autonomous navigation, augmented reality and more. While de

agentsarxiv-cs-cv

Pre-trained Large Language Models Learn Hidden Markov Models In-context

arXiv:2506.07298v3 Announce Type: replace-cross Abstract: Hidden Markov Models (HMMs) are foundational tools for modeling sequential data with latent Markovian structure, yet fitting them to real-worl

tutorialsarxiv-cs-ai

Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation

arXiv:2502.17011v2 Announce Type: replace-cross Abstract: Financial bond yield forecasting is challenging due to data scarcity, nonlinear macroeconomic dependencies, and evolving market conditions. In

applicationsarxiv-cs-cl

Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization

arXiv:2604.22345v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong implicit personalization ability, yet most existing approaches treat this behavior as a black box, relying o

researcharxiv-cs-cl

PreMoE: Proactive Inference for Efficient Mixture-of-Experts

arXiv:2505.17639v3 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models offer dynamic computation, but are typically deployed as static full-capacity models, missing opportunities for depl

Presenting DiaData for Research on Type 1 Diabetes

arXiv:2508.09160v2 Announce Type: replace Abstract: Type 1 diabetes (T1D) is an autoimmune disorder that leads to the destruction of insulin-producing cells, resulting in insulin deficiency, as to why

Local Ai

Preserve Support, Not Correspondence: Dynamic Routing for Offline Reinforcement Learning

arXiv:2604.22229v1 Announce Type: cross Abstract: One-step offline RL actors are attractive because they avoid backpropagating through long iterative samplers and keep inference cheap, but they still

local-aiarxiv-cs-ai

Privacy Leakage via Output Label Space and Differentially Private Continual Learning

arXiv:2411.04680v5 Announce Type: replace Abstract: Differential privacy (DP) is a formal privacy framework that enables training machine learning (ML) models while protecting individuals' data. As po

PrivSTRUCT: Untangling Data Purpose Compliance of Privacy Policies in Google Play Store

arXiv:2604.22157v1 Announce Type: cross Abstract: Existing research typically treats privacy policies as flat, uniform text, extracting information without regard for the document's logical hierarchy.

tutorialsarxiv-cs-ai

PrivUn: Unveiling Latent Ripple Effects and Shallow Forgetting in Privacy Unlearning

arXiv:2604.22076v1 Announce Type: cross Abstract: Large language models (LLMs) often memorize private information during training, raising serious privacy concerns. While machine unlearning has emerge

researcharxiv-cs-cl

Protect the Brain When Treating the Heart: A Convolutional Neural Network for Detecting Emboli

arXiv:2604.22258v1 Announce Type: cross Abstract: Gaseous microemboli (GME) represent a common complication of cardiac structural interventions across both surgical and transcatheter approaches. Trans

researcharxiv-cs-ai

PSI: A Benchmark for Human Interpretation and Response in Traffic Interactions

model-releasesarxiv-cs-ai

arXiv:2112.02604v3 Announce Type: replace-cross Abstract: Accurately modeling pedestrian intention and understanding driver decision-making processes are critical for the development of safe and socia

QDTraj: Exploration of Diverse Trajectory Primitives for Articulated Objects Robotic Manipulation

arXiv:2604.22551v1 Announce Type: cross Abstract: Thanks to the latest advances in learning and robotics, domestic robots are beginning to enter homes, aiming to execute household chores autonomously.

agentsarxiv-cs-ai

Quality-Driven Selective Mutation for Deep Learning

arXiv:2604.22640v1 Announce Type: cross Abstract: Mutants support testing and debugging in two roles: (i) as test goals and (ii) as substitutes for real faults. Hard-to-kill mutants provide better gui

QuantClaw: Precision Where It Matters for OpenClaw

arXiv:2604.22577v1 Announce Type: new Abstract: Autonomous agent systems such as OpenClaw introduce significant efficiency challenges due to long-context inputs and multi-turn reasoning. This results

agentsarxiv-cs-ai

Railway Artificial Intelligence Learning Benchmark (RAIL-BENCH): A Benchmark Suite for Perception in the Railway Domain

model-releasesarxiv-cs-cv

arXiv:2604.22507v1 Announce Type: new Abstract: Automated train operation on existing railway infrastructure requires robust camera-based perception, yet the railway domain lacks public benchmark suit

Read the Paper, Write the Code: Agentic Reproduction of Social-Science Results

arXiv:2604.21965v1 Announce Type: new Abstract: Recent work has used LLM agents to reproduce empirical social science results with access to both the data and code. We broaden this scope by asking: Ca

agentsarxiv-cs-ai

ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation

arXiv:2604.22169v1 Announce Type: cross Abstract: Generic group-based RL assumes that sampled rollout groups are already usable learning signals. We show that this assumption breaks down in sparse-hit

safetyarxiv-cs-ai

Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review

model-releasesarxiv-cs-cv

arXiv:2506.14831v3 Announce Type: replace Abstract: With the emergence of powerful data-driven methods in human trajectory prediction (HTP), gaining a finer understanding of multi-agent interactions l

Recognition Without Authorization: LLMs and the Moral Order of Online Advice

arXiv:2604.22143v1 Announce Type: cross Abstract: Large language models are increasingly used to mediate everyday interpersonal dilemmas, yet how their advisory defaults interact with the concentrated

safetyarxiv-cs-cl

RedVLA: Physical Red Teaming for Vision-Language-Action Models

arXiv:2604.22591v1 Announce Type: new Abstract: The real-world deployment of Vision-Language-Action (VLA) models remains limited by the risk of unpredictable and irreversible physical harm. However, w

safetyarxiv-cs-ro

Region Matters: Efficient and Reliable Region-Aware Visual Place Recognition

model-releasesarxiv-cs-cv

arXiv:2604.22390v1 Announce Type: new Abstract: Visual Place Recognition (VPR) determines a query image's geographic location by matching it against geotagged databases. However, existing methods stru

Regularized Meta-Learning for Improved Generalization

arXiv:2602.12469v2 Announce Type: replace Abstract: Deep ensemble methods often improve predictive performance, yet they suffer from three practical limitations: redundancy among base models that infl

Relaxation-Informed Training of Neural Network Surrogate Models

arXiv:2604.22746v1 Announce Type: cross Abstract: ReLU neural networks trained as surrogate models can be embedded exactly in mixed-integer linear programs (MILPs), enabling global optimization over t

Relay-Based Coordination for Energy-Efficient Multi-Robot Pickup and Delivery

arXiv:2509.14127v2 Announce Type: replace Abstract: We consider the problem of delivering multiple packages from a single depot to distinct goal locations using a homogeneous fleet of robots with limi

applicationsarxiv-cs-ro

ReLeVAnT: Relevance Lexical Vectors for Accurate Legal Text Classification

arXiv:2604.22292v1 Announce Type: cross Abstract: The classification of legal documents from an unstructured data corpus has several crucial applications in downstream tasks. Documents relevant to cou

applicationsarxiv-cs-ai

Reliability Auditing for Downstream LLM tasks in Psychiatry: LLM-Generated Hospitalization Risk Scores

model-releasesarxiv-cs-ai

arXiv:2604.22063v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly utilized in clinical reasoning and risk assessment. However, their interpretive reliability in critical