AuthorMix: Modular Authorship Style Transfer via Layer-wise Adapter Mixing
arXiv:2603.23069v1 Announce Type: new Abstract: The task of authorship style transfer involves rewriting text in the style of a target author while preserving the meaning of the original text. Existing style transfer methods train a single model on large corpora...
HGNet: Scalable Foundation Model for Automated Knowledge Graph Generation from Scientific Literature
arXiv:2603.23136v1 Announce Type: new Abstract: Automated knowledge graph (KG) construction is essential for navigating the rapidly expanding body of scientific literature. However, existing approaches struggle to recognize long multi-word entities, often fail to generalize across domains, and typically overlook the...
Why AI-Generated Text Detection Fails: Evidence from Explainable AI Beyond Benchmark Accuracy
arXiv:2603.23146v1 Announce Type: new Abstract: The widespread adoption of Large Language Models (LLMs) has made the detection of AI-Generated text a pressing and complex challenge. Although many detection systems report high benchmark accuracy, their reliability in real-world settings remains uncertain,...
ImplicitRM: Unbiased Reward Modeling from Implicit Preference Data for LLM alignment
arXiv:2603.23184v1 Announce Type: new Abstract: Reward modeling represents a long-standing challenge in reinforcement learning from human feedback (RLHF) for aligning language models. Current reward modeling is heavily contingent upon experimental feedback data with high collection costs. In this work, we...
Decoding AI Authorship: Can LLMs Truly Mimic Human Style Across Literature and Politics?
arXiv:2603.23219v1 Announce Type: new Abstract: Amidst the rising capabilities of generative AI to mimic specific human styles, this study investigates the ability of state-of-the-art large language models (LLMs), including GPT-4o, Gemini 1.5 Pro, and Claude Sonnet 3.5, to emulate the...
I Came, I Saw, I Explained: Benchmarking Multimodal LLMs on Figurative Meaning in Memes
arXiv:2603.23229v1 Announce Type: new Abstract: Internet memes represent a popular form of multimodal online communication and often use figurative elements to convey layered meaning through the combination of text and images. However, it remains largely unclear how multimodal large language...
Beyond Hard Constraints: Budget-Conditioned Reachability For Safe Offline Reinforcement Learning
arXiv:2603.22292v1 Announce Type: new Abstract: Sequential decision making using Markov Decision Process underpins many realworld applications. Both model-based and model free methods have achieved strong results in these settings. However, real-world tasks must balance reward maximization with safety constraints, often...
Scaling Attention via Feature Sparsity
arXiv:2603.22300v1 Announce Type: new Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce this cost along the sequence axis through local windows, kernel approximations, or token-level sparsity, but these approaches consistently...
Latent Semantic Manifolds in Large Language Models
arXiv:2603.22301v1 Announce Type: new Abstract: Large Language Models (LLMs) perform internal computations in continuous vector spaces yet produce discrete tokens -- a fundamental mismatch whose geometric consequences remain poorly understood. We develop a mathematical framework that interprets LLM hidden states...
Research on Individual Trait Clustering and Development Pathway Adaptation Based on the K-means Algorithm
arXiv:2603.22302v1 Announce Type: new Abstract: With the development of information technology, the application of artificial intelligence and machine learning in the field of education shows great potential. This study aims to explore how to utilize K-means clustering algorithm to provide...
CN-Buzz2Portfolio: A Chinese-Market Dataset and Benchmark for LLM-Based Macro and Sector Asset Allocation from Daily Trending Financial News
arXiv:2603.22305v1 Announce Type: new Abstract: Large Language Models (LLMs) are rapidly transitioning from static Natural Language Processing (NLP) tasks including sentiment analysis and event extraction to acting as dynamic decision-making agents in complex financial environments. However, the evolution of LLMs...
Full waveform inversion method based on diffusion model
arXiv:2603.22307v1 Announce Type: new Abstract: Seismic full-waveform inversion is a core technology for obtaining high-resolution subsurface model parameters. However, its highly nonlinear characteristics and strong dependence on the initial model often lead to the inversion process getting trapped in local...
UniFluids: Unified Neural Operator Learning with Conditional Flow-matching
arXiv:2603.22309v1 Announce Type: new Abstract: Partial differential equation (PDE) simulation holds extensive significance in scientific research. Currently, the integration of deep neural networks to learn solution operators of PDEs has introduced great potential. In this paper, we present UniFluids, a...
A Multi-Modal CNN-LSTM Framework with Multi-Head Attention and Focal Loss for Real-Time Elderly Fall Detection
arXiv:2603.22313v1 Announce Type: new Abstract: The increasing global aging population has intensified the demand for reliable health monitoring systems, particularly those capable of detecting critical events such as falls among elderly individuals. Traditional fall detection approaches relying on single-modality acceleration...
Emergency Preemption Without Online Exploration: A Decision Transformer Approach
arXiv:2603.22315v1 Announce Type: new Abstract: Emergency vehicle (EV) response time is a critical determinant of survival outcomes, yet deployed signal preemption strategies remain reactive and uncontrollable. We propose a return-conditioned framework for emergency corridor optimization based on the Decision Transformer...
A graph neural network based chemical mechanism reduction method for combustion applications
arXiv:2603.22318v1 Announce Type: new Abstract: Direct numerical simulations of turbulent reacting flows involving millions of grid points and detailed chemical mechanisms with hundreds of species and thousands of reactions are computationally prohibitive. To address this challenge, we present two data-driven...
Sparsely-Supervised Data Assimilation via Physics-Informed Schr\"odinger Bridge
arXiv:2603.22319v1 Announce Type: new Abstract: Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while respecting physical constraints. While full-grid low-fidelity (LF) simulations provide informative priors in...
Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation
arXiv:2603.22320v1 Announce Type: new Abstract: While climate models provide insights for climate decision-making, their use is constrained by significant computational and technical demands. Although machine learning (ML) emulators offer a way to bypass the high computational costs, their effective use...
AEGIS: An Operational Infrastructure for Post-Market Governance of Adaptive Medical AI Under US and EU Regulations
arXiv:2603.22322v1 Announce Type: new Abstract: Machine learning systems deployed in medical devices require governance frameworks that ensure safety while enabling continuous improvement. Regulatory bodies including the FDA and European Union have introduced mechanisms such as the Predetermined Change Control Plan...
A Multi-Task Targeted Learning Framework for Lithium-Ion Battery State-of-Health and Remaining Useful Life
arXiv:2603.22323v1 Announce Type: new Abstract: Accurately predicting the state-of-health (SOH) and remaining useful life (RUL) of lithium-ion batteries is crucial for ensuring the safe and efficient operation of electric vehicles while minimizing associated risks. However, current deep learning methods are...
Hybrid Associative Memories
arXiv:2603.22325v1 Announce Type: new Abstract: Recurrent neural networks (RNNs) and self-attention are both widely used sequence-mixing layers that maintain an internal memory. However, this memory is constructed using two orthogonal mechanisms: RNNs compress the entire past into a fixed-size state,...
A Direct Classification Approach for Reliable Wind Ramp Event Forecasting under Severe Class Imbalance
arXiv:2603.22326v1 Announce Type: new Abstract: Decision support systems are essential for maintaining grid stability in low-carbon power systems, such as wind power plants, by providing real-time alerts to control room operators regarding potential events, including Wind Power Ramp Events (WPREs)....
Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression
arXiv:2603.22328v1 Announce Type: new Abstract: Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their trustworthiness through reliable estimates of predictive confidence remains a critical challenge. This issue arises in scenarios where the likelihood...
Conformal Risk Control for Safety-Critical Wildfire Evacuation Mapping: A Comparative Study of Tabular, Spatial, and Graph-Based Models
arXiv:2603.22331v1 Announce Type: new Abstract: Every wildfire prediction model deployed today shares a dangerous property: none of these methods provides formal guarantees on how much fire spread is missed. Despite extensive work on wildfire spread prediction using deep learning, no...
Large Language Models for Missing Data Imputation: Understanding Behavior, Hallucination Effects, and Control Mechanisms
arXiv:2603.22332v1 Announce Type: new Abstract: Data imputation is a cornerstone technique for handling missing values in real-world datasets, which are often plagued by missingness. Despite recent progress, prior studies on Large Language Models-based imputation remain limited by scalability challenges, restricted...
Graph Signal Processing Meets Mamba2: Adaptive Filter Bank via Delta Modulation
arXiv:2603.22333v1 Announce Type: new Abstract: State-space models (SSMs) offer efficient alternatives to attention with linear-time recurrence. Mamba2, a recent SSM-based language model, uses selective input gating and a multi-head structure, enabling parallel computation and strong benchmark performance. However, its multi-head...
Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits
arXiv:2603.22339v1 Announce Type: new Abstract: Chinchilla Approach 2 is among the most widely used methods for fitting neural scaling laws. Its parabolic approximation introduces systematic biases in compute-optimal allocation estimates, even on noise-free synthetic data. Applied to published Llama 3...
First-Mover Bias in Gradient Boosting Explanations: Mechanism, Detection, and Resolution
arXiv:2603.22346v1 Announce Type: new Abstract: We isolate and empirically characterize first-mover bias -- a path-dependent concentration of feature importance caused by sequential residual fitting in gradient boosting -- as a specific mechanistic cause of the well-known instability of SHAP-based feature...
COMPASS-Hedge: Learning Safely Without Knowing the World
arXiv:2603.22348v1 Announce Type: new Abstract: Online learning algorithms often faces a fundamental trilemma: balancing regret guarantees between adversarial and stochastic settings and providing baseline safety against a fixed comparator. While existing methods excel in one or two of these regimes,...
WIST: Web-Grounded Iterative Self-Play Tree for Domain-Targeted Reasoning Improvement
arXiv:2603.22352v1 Announce Type: new Abstract: Recent progress in reinforcement learning with verifiable rewards (RLVR) offers a practical path to self-improvement of language models, but existing methods face a key trade-off: endogenous self-play can drift over iterations, while corpus-grounded approaches rely...