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A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations

arXiv:2411.06278v4 Announce Type: replace-cross Abstract: We propose a scalable preconditioned primal-dual hybrid gradient algorithm for solving partial differential equations (PDEs). We multiply the

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A neural signed configuration distance function for path planning of picking manipulators

DGX agent

arXiv:2502.16205v3 Announce Type: replace Abstract: Picking manipulators are task specific robots, with fewer degrees of freedom compared to general-purpose manipulators, and are heavily used in indus

researcharxiv-cs-ro

26 May 2026

A Principled Self-Referenced Early Stopping Approach for Deep Image Prior

arXiv:2605.25299v1 Announce Type: cross Abstract: Recently, Deep Image Prior (DIP) has demonstrated strong capabilities for solving inverse imaging problems (IIPs) by optimizing a randomly initialized

A reality check on the AI jobs hysteria

Haven’t you heard? White-collar jobs are going away, decimated by AI. Waves of layoffs in the tech sector (most recently at Coinbase and Meta and Cisco) are said to presage what will soon come for all

researchmit-tech-review

A Reinforcement Learning Inspired Latent Yield Based Adaptive Algorithm Switching Mechanism

arXiv:2605.24436v1 Announce Type: cross Abstract: Selecting the most suitable algorithm for a given problem instance remains a challenging task, particularly in online or dynamic environments where pr

A Signal-Language Foundation Model for Broad-Spectrum Cardiovascular Assessment from Routine Electrocardiography

arXiv:2605.25446v1 Announce Type: new Abstract: Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often restricted to common arrhythmias and may generalize po

Abduction-Deduction Entanglement: Domain Generalization via Representation Transplants

arXiv:2605.25156v1 Announce Type: cross Abstract: Prediction models trained under the source distribution do not generalize well to a different target distribution. A valid inference about an unseen d

Accelerated Dynamic Importance Weighting with Versatile Divergence-Minimizing Estimators

arXiv:2605.25499v1 Announce Type: new Abstract: Importance weighting (IW) is a golden solver for joint distribution shift, where the joint distributions differ between the training and test data. To s

Accelerating Bayesian inverse design in computational fluid dynamics using neural operators

arXiv:2605.26059v1 Announce Type: cross Abstract: Bayesian inverse design provides a principled framework for inferring aerodynamic geometries from sparse flow observations while quantifying uncertain

Accelerating Long-Tail Generation in Synchronous RLHF Training via Adaptive Tensor Parallelism

arXiv:2605.23945v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) has become a key post-training paradigm for improving model quality. However, the synchronous three-st

AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust

arXiv:2605.24301v1 Announce Type: new Abstract: Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority, expanding the envelope of possible aggressive man

researcharxiv-cs-ro

Active Query Synthesis for Preference Learning

arXiv:2605.26072v1 Announce Type: new Abstract: Efficient learning of user preferences is crucial for many modern decision making systems but typically requires costly labeled data. Active learning re

Adaptive Preconditioners Trigger Loss Spikes in Adam

arXiv:2506.04805v2 Announce Type: replace Abstract: Loss spikes commonly emerge during neural network training with the Adam optimizer across diverse architectures and scales, yet their underlying mec

Adversarial Network Imagination: Causal LLMs and Digital Twins for Proactive Telecom Mitigation

arXiv:2602.13203v2 Announce Type: replace-cross Abstract: Telecommunication networks experience complex failures such as fiber cuts, traffic overloads, and cascading outages. Existing monitoring and d

Adversarial Orthogonal Disentanglement for LVLM Hallucination Mitigation

arXiv:2605.25377v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have advanced multimodal understanding, yet their reliability is limited by hallucination, where generated conten

Affinity Graph Connectivity in Convex Clustering

arXiv:2605.24673v1 Announce Type: cross Abstract: We generalize finite-sample bounds for convex clustering to the setting where affinity weights appearing in the objective correspond to a general conn

AI as Equalizer or Amplifier? Task Complexity as the Moderating Factor for Human Expertise in Hybrid Intelligence Systems

arXiv:2512.10961v2 Announce Type: replace-cross Abstract: A growing body of empirical research suggests that generative AI narrows performance gaps between novice and expert workers on routine tasks--

AI-Driven Adaptive Adversaries and the Erosion of Cryptographic Trust in Public Key Systems

arXiv:2605.24542v1 Announce Type: cross Abstract: This paper examines the erosion of Public Key Cryptography (PKC) security under adaptive adversarial optimisation driven by artificial intelligence. T

AI-Driven Alpha Decay: Algorithmic Homogenization, Reflexive Signal Erosion, and the Paradox of Intelligent Markets

arXiv:2605.23905v1 Announce Type: cross Abstract: We show that AI-driven investment strategies are inherently self-defeating at scale. As AI adoption rises, three mutually reinforcing channels -- sign

Algometrics: Forecasting Under Algorithmic Feedback

arXiv:2605.23978v1 Announce Type: new Abstract: In algorithmic markets, predictive models become part of the data-generating process they aim to forecast. Once their outputs are converted into trades,

Algorithms with Polynomially-Improved Approximation Factors for the 2 rightarrow q Norm, and Applications

arXiv:2605.25303v1 Announce Type: cross Abstract: The 2 rightarrow q norm of a matrix X in R^{n imes d} is defined as lVert X rVert_{2 rightarrow q} = sup_{lVert v rVert_2 = 1} lVert Xv rVert_q. We gi

Aligning Molecular Graph Explanations with Chemical Identity via InChIfied Invariants

arXiv:2605.24742v1 Announce Type: new Abstract: Obtaining consistent explanations for machine learning on molecular graphs requires predictions and attributions to be aligned with chemical identity. H

All Leaks Count, Some Count More: Interpretable Temporal Contamination Detection and Mitigation in LLM Backtesting

arXiv:2602.17234v2 Announce Type: replace Abstract: Backtesting LLMs on resolved events assumes models reason only from pre-cutoff knowledge, yet pretrained models inevitably leak post-cutoff knowledg

AnatomiX, an Anatomy-Aware Grounded Multimodal Large Language Model for Chest X-Ray Interpretation

arXiv:2601.03191v3 Announce Type: replace-cross Abstract: Multimodal medical large language models have shown substantial progress in chest X-ray interpretation but continue to face challenges in spat

ANIMC: A Soft Framework for Auto-weighted Noisy and Incomplete Multi-view Clustering

arXiv:2011.10331v4 Announce Type: replace-cross Abstract: Multi-view clustering has wide applications in many image processing scenarios. In these scenarios, original image data often contain missing

Approximating the universal thermal climate index using sparse regression with orthogonal polynomials

arXiv:2508.11307v3 Announce Type: replace-cross Abstract: The Universal Thermal Climate Index (UTCI) is a measure of thermal comfort that quantifies how humans experience environmental conditions. Due

Artificial Effort

arXiv:2605.23920v1 Announce Type: cross Abstract: Real-effort tasks, in which participants perform cognitively costly activities whose outcomes depend on actual performance, are widely used in experim

Associations between echocardiographic traits and AI-ECG predictions of heart failure

arXiv:2605.24576v1 Announce Type: new Abstract: Artificial intelligence-enabled electrocardiography (AI-ECG) can detect heart failure (HF), including disease not captured by left ventricular ejection

ATWL: A Formal Language for Representing, Comparing, and Reusing Visual Analytics Workflows

arXiv:2605.25489v1 Announce Type: new Abstract: Visual analytics (VA) workflows are inherently complex, involving data transformation, feature engineering, visual representation, and human interpretat

Aurora Hunter: A Two-Stage Framework for Probabilistic Visibility Forecasting

arXiv:2605.24038v1 Announce Type: cross Abstract: Forecasting aurora borealis visibility matters for space weather research and aurora tourism. Visibility at a site and night depends on two distinct f

BackWeak: Backdooring Knowledge Distillation Simply with Weak Triggers and Fine-tuning

arXiv:2511.12046v2 Announce Type: replace-cross Abstract: Knowledge Distillation (KD) is essential for compressing large models, yet relying on pre-trained 'teacher' models downloaded from third-party

BandVQ: Band-Wise Vector-Quantized EEG Foundation Model

arXiv:2605.24921v1 Announce Type: new Abstract: A central challenge in electroencephalography (EEG) foundation modeling is learning transferable representations across recordings with diverse tasks, m

Batch Normalization Amplifies Memorization and Privacy Risks

arXiv:2605.24420v1 Announce Type: cross Abstract: Batch Normalization (BN) is widely adopted to enable faster convergence and more stable training of deep neural networks. However, its impact on priva

Benchmarking non-conformity score functions in conformal prediction

arXiv:2605.24983v1 Announce Type: new Abstract: Conformal prediction is a useful and versatile alternative to model calibration in machine learning classification. It replaces single-class prediction

Better, Faster: Harnessing Self-Improvement in Large Reasoning Models

arXiv:2605.24998v1 Announce Type: new Abstract: Self-improvement training enables the large reasoning models (LRMs) to improve themselves by self-generating reasoning trajectories as training data wit

researcharxiv-cs-cl

Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Text

arXiv:2605.24546v1 Announce Type: new Abstract: Process modeling is a sub-domain of Business Process Management (BPM) focused on the translation of process artifacts into formal models. This task trad

Beyond Fixed Points: Superpolynomial Capacity of Asymmetric Hopfield Networks

arXiv:2605.24611v1 Announce Type: new Abstract: Classical Hopfield networks are limited to static patterns due to symmetric weights, whereas asymmetric networks can encode temporal sequences via limit

Beyond the Frontier: Stochastic Backtracking for Efficient Test-Time Scaling

arXiv:2605.25143v1 Announce Type: new Abstract: Test-time scaling improves language model reasoning by spending additional compute to explore multiple solution trajectories. The key challenge is to ma

BigMac: Breaking the Pareto Frontier of Compute and Memory in Multimodal LLM Training

arXiv:2605.25451v1 Announce Type: new Abstract: Training multimodal large language models (MLLMs) is challenged by both model and data heterogeneity. Existing systems redesign the training pipeline to

Bilevel Optimization of Synthetic Trajectories for Multi-Turn LLM Fine-Tuning

arXiv:2605.24743v1 Announce Type: cross Abstract: While LLMs excel at single-turn generation, they struggle with long-horizon, multi-turn interactions. Offline reinforcement learning (RL) offers a sca

Binding Visual Features Point by Point

arXiv:2605.25427v1 Announce Type: cross Abstract: Despite success on standard benchmarks, vision language models display persistent failures on tasks involving processing of multi-object scenes, inclu

Breaking the Chains of Probability: Neutrosophic Logic as a New Framework for Epistemic Uncertainty in Large Language Models

arXiv:2605.24053v1 Announce Type: new Abstract: Large Language Models (LLMs) are predominantly governed by probabilistic frameworks in which the sum of outcome probabilities is constrained to unity. T

Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms

arXiv:2401.11963v5 Announce Type: replace-cross Abstract: Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs) and Reinforcement Learning (RL) for optimization, ha

Bridging the Semantic-Action Gap in Visual Token Pruning for Efficient VLA Inference

arXiv:2511.16449v4 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have shown great potential for embodied AI by integrating visual perception, language understanding, and a

Byzantine-Robust Federated Learning with Learnable Aggregation Weights

arXiv:2511.03529v2 Announce Type: replace Abstract: Federated Learning (FL) enables clients to collaboratively train a global model without sharing their private data. However, the presence of malicio

CAffNet: Hard Constraint-Affine Neural Networks

arXiv:2605.24437v1 Announce Type: new Abstract: We present a novel framework for embedding hard constraint satisfaction into neural network (NN) architectures, specifically feedforward neural networks

Capture-Calibrate-Coach: A Graph-Based Framework for Knowledge Monitoring Estimation and Adaptive Feedback

arXiv:2605.25419v1 Announce Type: new Abstract: Effective learning support requires understanding not only what learners know but also how accurately they perceive their own understanding. This metaco

CARL-CXR: Continual Adapter-Based Routing for Task-Unknown Chest Radiograph Classification

arXiv:2602.15811v2 Announce Type: replace-cross Abstract: Clinical deployment of chest radiograph classifiers requires models that can be updated as new datasets become available without retraining on