Stochastic Sequential Decision Making over Expanding Networks with Graph Filtering
arXiv:2603.19501v1 Announce Type: new Abstract: Graph filters leverage topological information to process networked data with existing methods mainly studying fixed graphs, ignoring that graphs often expand as nodes continually attach with an unknown pattern. The latter requires developing filter-based decision-making...
Understanding the Theoretical Foundations of Deep Neural Networks through Differential Equations
arXiv:2603.18331v1 Announce Type: new Abstract: Deep neural networks (DNNs) have achieved remarkable empirical success, yet the absence of a principled theoretical foundation continues to hinder their systematic development. In this survey, we present differential equations as a theoretical foundation for...
From Weak Cues to Real Identities: Evaluating Inference-Driven De-Anonymization in LLM Agents
arXiv:2603.18382v1 Announce Type: new Abstract: Anonymization is widely treated as a practical safeguard because re-identifying anonymous records was historically costly, requiring domain expertise, tailored algorithms, and manual corroboration. We study a growing privacy risk that may weaken this barrier: LLM-based...
NeuroGame Transformer: Gibbs-Inspired Attention Driven by Game Theory and Statistical Physics
arXiv:2603.18761v1 Announce Type: new Abstract: Standard attention mechanisms in transformers are limited by their pairwise formulation, which hinders the modeling of higher-order dependencies among tokens. We introduce the NeuroGame Transformer (NGT) to overcome this by reconceptualizing attention through a dual...
Beyond Accuracy: An Explainability-Driven Analysis of Harmful Content Detection
arXiv:2603.18015v1 Announce Type: new Abstract: Although automated harmful content detection systems are frequently used to monitor online platforms, moderators and end users frequently cannot understand the logic underlying their predictions. While recent studies have focused on increasing classification accuracy, little...
How LLMs Distort Our Written Language
arXiv:2603.18161v1 Announce Type: new Abstract: Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that LLMs not only alter the voice and tone of human writing,...
PowerFlow: Unlocking the Dual Nature of LLMs via Principled Distribution Matching
arXiv:2603.18363v1 Announce Type: new Abstract: Unsupervised Reinforcement Learning from Internal Feedback (RLIF) has emerged as a promising paradigm for eliciting the latent capabilities of Large Language Models (LLMs) without external supervision. However, current methods rely on heuristic intrinsic rewards, which...
Adaptive Decoding via Test-Time Policy Learning for Self-Improving Generation
arXiv:2603.18428v1 Announce Type: new Abstract: Decoding strategies largely determine the quality of Large Language Model (LLM) outputs, yet widely used heuristics such as greedy or fixed temperature/top-p decoding are static and often task-agnostic, leading to suboptimal or inconsistent generation quality...
WASD: Locating Critical Neurons as Sufficient Conditions for Explaining and Controlling LLM Behavior
arXiv:2603.18474v1 Announce Type: new Abstract: Precise behavioral control of large language models (LLMs) is critical for complex applications. However, existing methods often incur high training costs, lack natural language controllability, or compromise semantic coherence. To bridge this gap, we propose...
The Truncation Blind Spot: How Decoding Strategies Systematically Exclude Human-Like Token Choices
arXiv:2603.18482v1 Announce Type: new Abstract: Standard decoding strategies for text generation, including top-k, nucleus sampling, and contrastive search, select tokens based on likelihood, restricting selection to high-probability regions. Human language production operates differently: tokens are chosen for communicative appropriateness rather...
A Comparative Empirical Study of Catastrophic Forgetting Mitigation in Sequential Task Adaptation for Continual Natural Language Processing Systems
arXiv:2603.18641v1 Announce Type: new Abstract: Neural language models deployed in real-world applications must continually adapt to new tasks and domains without forgetting previously acquired knowledge. This work presents a comparative empirical study of catastrophic forgetting mitigation in continual intent classification....
Automatic detection of Gen-AI texts: A comparative framework of neural models
arXiv:2603.18750v1 Announce Type: new Abstract: The rapid proliferation of Large Language Models has significantly increased the difficulty of distinguishing between human-written and AI generated texts, raising critical issues across academic, editorial, and social domains. This paper investigates the problem of...
Progressive Training for Explainable Citation-Grounded Dialogue: Reducing Hallucination to Zero in English-Hindi LLMs
arXiv:2603.18911v1 Announce Type: new Abstract: Knowledge-grounded dialogue systems aim to generate informative, contextually relevant responses by conditioning on external knowledge sources. However, most existing approaches focus exclusively on English, lack explicit citation mechanisms for verifying factual claims, and offer limited...
Taming Epilepsy: Mean Field Control of Whole-Brain Dynamics
arXiv:2603.18035v1 Announce Type: new Abstract: Controlling the high-dimensional neural dynamics during epileptic seizures remains a significant challenge due to the nonlinear characteristics and complex connectivity of the brain. In this paper, we propose a novel framework, namely Graph-Regularized Koopman Mean-Field...
Probabilistic Federated Learning on Uncertain and Heterogeneous Data with Model Personalization
arXiv:2603.18083v1 Announce Type: new Abstract: Conventional federated learning (FL) frameworks often suffer from training degradation due to data uncertainty and heterogeneity across local clients. Probabilistic approaches such as Bayesian neural networks (BNNs) can mitigate this issue by explicitly modeling uncertainty,...
ARTEMIS: A Neuro Symbolic Framework for Economically Constrained Market Dynamics
arXiv:2603.18107v1 Announce Type: new Abstract: Deep learning models in quantitative finance often operate as black boxes, lacking interpretability and failing to incorporate fundamental economic principles such as no-arbitrage constraints. This paper introduces ARTEMIS (Arbitrage-free Representation Through Economic Models and Interpretable...
BoundAD: Boundary-Aware Negative Generation for Time Series Anomaly Detection
arXiv:2603.18111v1 Announce Type: new Abstract: Contrastive learning methods for time series anomaly detection (TSAD) heavily depend on the quality of negative sample construction. However, existing strategies based on random perturbations or pseudo-anomaly injection often struggle to simultaneously preserve temporal semantic...
Gradient-Informed Temporal Sampling Improves Rollout Accuracy in PDE Surrogate Training
arXiv:2603.18237v1 Announce Type: new Abstract: Researchers train neural simulators on uniformly sampled numerical simulation data. But under the same budget, does systematically sampled data provide the most effective information? A fundamental yet unformalized problem is how to sample training data...
ALIGN: Adversarial Learning for Generalizable Speech Neuroprosthesis
arXiv:2603.18299v1 Announce Type: new Abstract: Intracortical brain-computer interfaces (BCIs) can decode speech from neural activity with high accuracy when trained on data pooled across recording sessions. In realistic deployment, however, models must generalize to new sessions without labeled data, and...
Approximate Subgraph Matching with Neural Graph Representations and Reinforcement Learning
arXiv:2603.18314v1 Announce Type: new Abstract: Approximate subgraph matching (ASM) is a task that determines the approximate presence of a given query graph in a large target graph. Being an NP-hard problem, ASM is critical in graph analysis with a myriad...
A Family of Adaptive Activation Functions for Mitigating Failure Modes in Physics-Informed Neural Networks
arXiv:2603.18328v1 Announce Type: new Abstract: Physics-Informed Neural Networks(PINNs) are a powerful and flexible learning framework that has gained significant attention in recent years. It has demonstrated strong performance across a wide range of scientific and engineering problems. In parallel, wavelets...
Mathematical Foundations of Deep Learning
arXiv:2603.18387v1 Announce Type: new Abstract: This draft book offers a comprehensive and rigorous treatment of the mathematical principles underlying modern deep learning. The book spans core theoretical topics, from the approximation capabilities of deep neural networks, the theory and algorithms...
Self-Tuning Sparse Attention: Multi-Fidelity Hyperparameter Optimization for Transformer Acceleration
arXiv:2603.18417v1 Announce Type: new Abstract: Sparse attention mechanisms promise to break the quadratic bottleneck of long-context transformers, yet production adoption remains limited by a critical usability gap: optimal hyperparameters vary substantially across layers and models, and current methods (e.g., SpargeAttn)...
Minimum-Action Learning: Energy-Constrained Symbolic Model Selection for Physical Law Identification from Noisy Data
arXiv:2603.16951v1 Announce Type: new Abstract: Identifying physical laws from noisy observational data is a central challenge in scientific machine learning. We present Minimum-Action Learning (MAL), a framework that selects symbolic force laws from a pre-specified basis library by minimizing a...
Contextual Preference Distribution Learning
arXiv:2603.17139v1 Announce Type: new Abstract: Decision-making problems often feature uncertainty stemming from heterogeneous and context-dependent human preferences. To address this, we propose a sequential learning-and-optimization pipeline to learn preference distributions and leverage them to solve downstream problems, for example risk-averse...
Binary Latent Protein Fitness Landscapes for Quantum Annealing Optimization
arXiv:2603.17247v1 Announce Type: new Abstract: We propose Q-BIOLAT, a framework for modeling and optimizing protein fitness landscapes in binary latent spaces. Starting from protein sequences, we leverage pretrained protein language models to obtain continuous embeddings, which are then transformed into...
Learning Permutation Distributions via Reflected Diffusion on Ranks
arXiv:2603.17353v1 Announce Type: new Abstract: The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and discrete, non-Euclidean structure. Recent permutation diffusion methods define forward...
Variational Kernel Design for Internal Noise: Gaussian Chaos Noise, Representation Compatibility, and Reliable Deep Learning
arXiv:2603.17365v1 Announce Type: new Abstract: Internal noise in deep networks is usually inherited from heuristics such as dropout, hard masking, or additive perturbation. We ask two questions: what correlation geometry should internal noise have, and is the implemented perturbation compatible...
Efficient Exploration at Scale
arXiv:2603.17378v1 Announce Type: new Abstract: We develop an online learning algorithm that dramatically improves the data efficiency of reinforcement learning from human feedback (RLHF). Our algorithm incrementally updates reward and language models as choice data is received. The reward model...
Auto-Unrolled Proximal Gradient Descent: An AutoML Approach to Interpretable Waveform Optimization
arXiv:2603.17478v1 Announce Type: new Abstract: This study explores the combination of automated machine learning (AutoML) with model-based deep unfolding (DU) for optimizing wireless beamforming and waveforms. We convert the iterative proximal gradient descent (PGD) algorithm into a deep neural network,...