Neural Operators for Multi-Task Control and Adaptation
arXiv:2604.03449v1 Announce Type: new Abstract: Neural operator methods have emerged as powerful tools for learning mappings between infinite-dimensional function spaces, yet their potential in optimal control remains largely unexplored. We focus on multi-task control problems, whose solution is a mapping...
Beauty in the Eye of AI: Aligning LLMs and Vision Models with Human Aesthetics in Network Visualization
arXiv:2604.03417v1 Announce Type: new Abstract: Network visualization has traditionally relied on heuristic metrics, such as stress, under the assumption that optimizing them leads to aesthetic and informative layouts. However, no single metric consistently produces the most effective results. A data-driven...
Neural Global Optimization via Iterative Refinement from Noisy Samples
arXiv:2604.03614v1 Announce Type: new Abstract: Global optimization of black-box functions from noisy samples is a fundamental challenge in machine learning and scientific computing. Traditional methods such as Bayesian Optimization often converge to local minima on multi-modal functions, while gradient-free methods...
When Do Hallucinations Arise? A Graph Perspective on the Evolution of Path Reuse and Path Compression
arXiv:2604.03557v1 Announce Type: new Abstract: Reasoning hallucinations in large language models (LLMs) often appear as fluent yet unsupported conclusions that violate either the given context or underlying factual knowledge. Although such failures are widely observed, the mechanisms by which decoder-only...
Evolutionary Search for Automated Design of Uncertainty Quantification Methods
arXiv:2604.03473v1 Announce Type: new Abstract: Uncertainty quantification (UQ) methods for large language models are predominantly designed by hand based on domain knowledge and heuristics, limiting their scalability and generality. We apply LLM-powered evolutionary search to automatically discover unsupervised UQ methods...
A Numerical Method for Coupling Parameterized Physics-Informed Neural Networks and FDM for Advanced Thermal-Hydraulic System Simulation
arXiv:2604.02663v1 Announce Type: new Abstract: Severe accident analysis using system-level codes such as MELCOR is indispensable for nuclear safety assessment, yet the computational cost of repeated simulations poses a significant bottleneck for parametric studies and uncertainty quantification. Existing surrogate models...
Prism: Policy Reuse via Interpretable Strategy Mapping in Reinforcement Learning
arXiv:2604.02353v1 Announce Type: cross Abstract: We present PRISM (Policy Reuse via Interpretable Strategy Mapping), a framework that grounds reinforcement learning agents' decisions in discrete, causally validated concepts and uses those concepts as a zero-shot transfer interface between agents trained with...
Cross-subject Muscle Fatigue Detection via Adversarial and Supervised Contrastive Learning with Inception-Attention Network
arXiv:2604.02670v1 Announce Type: new Abstract: Muscle fatigue detection plays an important role in physical rehabilitation. Previous researches have demonstrated that sEMG offers superior sensitivity in detecting muscle fatigue compared to other biological signals. However, features extracted from sEMG may vary...
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
arXiv:2604.02651v1 Announce Type: new Abstract: Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world scenarios. Learning from extremely large graphs requires distributed training, and mini-batching with sampling is a popular approach for parallelizing...
Complex-Valued GNNs for Distributed Basis-Invariant Control of Planar Systems
arXiv:2604.02615v1 Announce Type: new Abstract: Graph neural networks (GNNs) are a well-regarded tool for learned control of networked dynamical systems due to their ability to be deployed in a distributed manner. However, current distributed GNN architectures assume that all nodes...
Self-Directed Task Identification
arXiv:2604.02430v1 Announce Type: new Abstract: In this work, we present a novel machine learning framework called Self-Directed Task Identification (SDTI), which enables models to autonomously identify the correct target variable for each dataset in a zero-shot setting without pre-training. SDTI...
Differentiable Symbolic Planning: A Neural Architecture for Constraint Reasoning with Learned Feasibility
arXiv:2604.02350v1 Announce Type: cross Abstract: Neural networks excel at pattern recognition but struggle with constraint reasoning -- determining whether configurations satisfy logical or physical constraints. We introduce Differentiable Symbolic Planning (DSP), a neural architecture that performs discrete symbolic reasoning while...
A Comprehensive Framework for Long-Term Resiliency Investment Planning under Extreme Weather Uncertainty for Electric Utilities
arXiv:2604.02504v1 Announce Type: new Abstract: Electric utilities must make massive capital investments in the coming years to respond to explosive growth in demand, aging assets and rising threats from extreme weather. Utilities today already have rigorous frameworks for capital planning,...
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling
arXiv:2112.07874v2 Announce Type: cross Abstract: We examine the extent to which, in principle, linguistic graph representations can complement and improve neural language modeling. With an ensemble setup consisting of a pretrained Transformer and ground-truth graphs from one of 7 different...
Skeleton-based Coherence Modeling in Narratives
arXiv:2604.02451v1 Announce Type: new Abstract: Modeling coherence in text has been a task that has excited NLP researchers since a long time. It has applications in detecting incoherent structures and helping the author fix them. There has been recent work...
Student-in-the-Loop Chain-of-Thought Distillation via Generation-Time Selection
arXiv:2604.02819v1 Announce Type: new Abstract: Large reasoning models achieve strong performance on complex tasks through long chain-of-thought (CoT) trajectories, but directly transferring such reasoning processes to smaller models remains challenging. A key difficulty is that not all teacher-generated reasoning trajectories...
Evaluating the Formal Reasoning Capabilities of Large Language Models through Chomsky Hierarchy
arXiv:2604.02709v1 Announce Type: new Abstract: The formal reasoning capabilities of LLMs are crucial for advancing automated software engineering. However, existing benchmarks for LLMs lack systematic evaluation based on computation and complexity, leaving a critical gap in understanding their formal reasoning...
Analytic Drift Resister for Non-Exemplar Continual Graph Learning
arXiv:2604.02633v1 Announce Type: new Abstract: Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype representations rather than raw graph examples for mitigating catastrophic forgetting. However, this design choice inevitably...
Re-analysis of the Human Transcription Factor Atlas Recovers TF-Specific Signatures from Pooled Single-Cell Screens with Missing Controls
arXiv:2604.02511v1 Announce Type: new Abstract: Public pooled single-cell perturbation atlases are valuable resources for studying transcription factor (TF) function, but downstream re-analysis can be limited by incomplete deposited metadata and missing internal controls. Here we re-analyze the human TF Atlas...
Learning the Signature of Memorization in Autoregressive Language Models
arXiv:2604.03199v1 Announce Type: new Abstract: All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded by the designer's intuition. We introduce the first transferable learned attack, enabled by the observation...
NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons
arXiv:2604.02972v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) have recently achieved remarkable success in complex reasoning tasks. However, closer scrutiny reveals persistent failure modes compromising performance and cost: I) Intra-step level, marked by calculation or derivation errors; II) Inter-step...
WGFINNs: Weak formulation-based GENERIC formalism informed neural networks'
arXiv:2604.02601v1 Announce Type: new Abstract: Data-driven discovery of governing equations from noisy observations remains a fundamental challenge in scientific machine learning. While GENERIC formalism informed neural networks (GFINNs) provide a principled framework that enforces the laws of thermodynamics by construction,...
Analysis of Optimality of Large Language Models on Planning Problems
arXiv:2604.02910v1 Announce Type: new Abstract: Classic AI planning problems have been revisited in the Large Language Model (LLM) era, with a focus of recent benchmarks on success rates rather than plan efficiency. We examine the degree to which frontier models...
Netflix must refund customers for years of price hikes, Italian court rules
Consumer group says it will sue if Netflix doesn't reduce current prices.
Care-Conditioned Neuromodulation for Autonomy-Preserving Supportive Dialogue Agents
arXiv:2604.01576v1 Announce Type: new Abstract: Large language models deployed in supportive or advisory roles must balance helpfulness with preservation of user autonomy, yet standard alignment methods primarily optimize for helpfulness and harmlessness without explicitly modeling relational risks such as dependency...
Beyond Symbolic Solving: Multi Chain-of-Thought Voting for Geometric Reasoning in Large Language Models
arXiv:2604.00890v1 Announce Type: new Abstract: Geometric Problem Solving (GPS) remains at the heart of enhancing mathematical reasoning in large language models because it requires the combination of diagrammatic understanding, symbolic manipulation and logical inference. In existing literature, researchers have chiefly...
Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine
arXiv:2604.01730v1 Announce Type: new Abstract: This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine. A physics-based component-level model is developed to generate training data and validate the controllers. A meta-heuristic extended dynamic mode decomposition is...
Coupled Query-Key Dynamics for Attention
arXiv:2604.01683v1 Announce Type: new Abstract: Standard scaled dot-product attention computes scores from static, independent projections of the input. We show that evolving queries and keys \emph{jointly} through shared learned dynamics before scoring - which we call \textbf{coupled QK dynamics} -...
Pseudo-Quantized Actor-Critic Algorithm for Robustness to Noisy Temporal Difference Error
arXiv:2604.01613v1 Announce Type: new Abstract: In reinforcement learning (RL), temporal difference (TD) errors are widely adopted for optimizing value and policy functions. However, since the TD error is defined by a bootstrap method, its computation tends to be noisy and...
Optimsyn: Influence-Guided Rubrics Optimization for Synthetic Data Generation
arXiv:2604.00536v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong downstream performance largely due to abundant supervised fine-tuning (SFT) data. However, high-quality SFT data in knowledge-intensive domains such as humanities, social sciences, medicine, law, and finance is scarce because...