VeRA: Verified Reasoning Data Augmentation at Scale
arXiv:2602.13217v1 Announce Type: new Abstract: The main issue with most evaluation schemes today is their "static" nature: the same problems are reused repeatedly, allowing for memorization, format exploitation, and eventual saturation. To measure genuine AI progress, we need evaluation that...
X-Blocks: Linguistic Building Blocks of Natural Language Explanations for Automated Vehicles
arXiv:2602.13248v1 Announce Type: new Abstract: Natural language explanations play a critical role in establishing trust and acceptance of automated vehicles (AVs), yet existing approaches lack systematic frameworks for analysing how humans linguistically construct driving rationales across diverse scenarios. This paper...
K-Search: LLM Kernel Generation via Co-Evolving Intrinsic World Model
arXiv:2602.19128v1 Announce Type: new Abstract: Optimizing GPU kernels is critical for efficient modern machine learning systems yet remains challenging due to the complex interplay of design factors and rapid hardware evolution. Existing automated approaches typically treat Large Language Models (LLMs)...
Characterizing MARL for Energy Control: A Multi-KPI Benchmark on the CityLearn Environment
arXiv:2602.19223v1 Announce Type: new Abstract: The optimization of urban energy systems is crucial for the advancement of sustainable and resilient smart cities, which are becoming increasingly complex with multiple decision-making units. To address scalability and coordination concerns, Multi-Agent Reinforcement Learning...
Truncated Step-Level Sampling with Process Rewards for Retrieval-Augmented Reasoning
arXiv:2602.23440v1 Announce Type: new Abstract: Training large language models to reason with search engines via reinforcement learning is hindered by a fundamental credit assignment problem: existing methods such as Search-R1 provide only a sparse outcome reward after an entire multi-step...
LiTS: A Modular Framework for LLM Tree Search
arXiv:2603.00631v1 Announce Type: new Abstract: LiTS is a modular Python framework for LLM reasoning via tree search. It decomposes tree search into three reusable components (Policy, Transition, and RewardModel) that plug into algorithms like MCTS and BFS. A decorator-based registry...
AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment
arXiv:2603.03686v1 Announce Type: new Abstract: Automated design of chemical formulations is a cornerstone of materials science, yet it requires navigating a high-dimensional combinatorial space involving discrete compositional choices and continuous geometric constraints. Existing Large Language Model (LLM) agents face significant...
Design Behaviour Codes (DBCs): A Taxonomy-Driven Layered Governance Benchmark for Large Language Models
arXiv:2603.04837v1 Announce Type: new Abstract: We introduce the Dynamic Behavioral Constraint (DBC) benchmark, the first empirical framework for evaluating the efficacy of a structured, 150-control behavioral governance layer, the MDBC (Madan DBC) system, applied at inference time to large language...
On Multi-Step Theorem Prediction via Non-Parametric Structural Priors
arXiv:2603.04852v1 Announce Type: new Abstract: Multi-step theorem prediction is a central challenge in automated reasoning. Existing neural-symbolic approaches rely heavily on supervised parametric models, which exhibit limited generalization to evolving theorem libraries. In this work, we explore training-free theorem prediction...
Causally Robust Reward Learning from Reason-Augmented Preference Feedback
arXiv:2603.04861v1 Announce Type: new Abstract: Preference-based reward learning is widely used for shaping agent behavior to match a user's preference, yet its sparse binary feedback makes it especially vulnerable to causal confusion. The learned reward often latches onto spurious features...
Generating Realistic, Protocol-Compliant Maritime Radio Dialogues using Self-Instruct and Low-Rank Adaptation
arXiv:2603.04423v1 Announce Type: new Abstract: VHF radio miscommunication remains a major safety risk in maritime operations, with human factors accounting for over 58% of recorded incidents in Europe between 2014 and 2023. Despite decades of operational use, VHF radio communications...
Learning Unified Distance Metric for Heterogeneous Attribute Data Clustering
arXiv:2603.04458v1 Announce Type: new Abstract: Datasets composed of numerical and categorical attributes (also called mixed data hereinafter) are common in real clustering tasks. Differing from numerical attributes that indicate tendencies between two concepts (e.g., high and low temperature) with their...
Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding
arXiv:2603.03312v1 Announce Type: cross Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain constrained by three fundamental limitations: Semantic Bias (mode collapse into generic templates), Signal Neglect (hallucination based on...
Combating data scarcity in recommendation services: Integrating cognitive types of VARK and neural network technologies (LLM)
arXiv:2603.03309v1 Announce Type: new Abstract: Cold start scenarios present fundamental obstacles to effective recommendation generation, particularly when dealing with users lacking interaction history or items with sparse metadata. This research proposes an innovative hybrid framework that leverages Large Language Models...
Local Shapley: Model-Induced Locality and Optimal Reuse in Data Valuation
arXiv:2603.03672v1 Announce Type: new Abstract: The Shapley value provides a principled foundation for data valuation, but exact computation is #P-hard due to the exponential coalition space. Existing accelerations remain global and ignore a structural property of modern predictors: for a...
ACE-Merging: Data-Free Model Merging with Adaptive Covariance Estimation
arXiv:2603.02945v1 Announce Type: new Abstract: Model merging aims to combine multiple task-specific expert models into a single model while preserving generalization across diverse tasks. However, interference among experts, especially when they are trained on different objectives, often leads to significant...
UniSkill: A Dataset for Matching University Curricula to Professional Competencies
arXiv:2603.03134v1 Announce Type: new Abstract: Skill extraction and recommendation systems have been studied from recruiter, applicant, and education perspectives. While AI applications in job advertisements have received broad attention, deficiencies in the instructed skills side remain a challenge. In this...
ATPO: Adaptive Tree Policy Optimization for Multi-Turn Medical Dialogue
arXiv:2603.02216v1 Announce Type: new Abstract: Effective information seeking in multi-turn medical dialogues is critical for accurate diagnosis, especially when dealing with incomplete information. Aligning Large Language Models (LLMs) for these interactive scenarios is challenging due to the uncertainty inherent in...
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction
arXiv:2603.02221v1 Announce Type: new Abstract: In healthcare tabular predictions, classical models with feature engineering often outperform neural approaches. Recent advances in Large Language Models enable the integration of domain knowledge into feature engineering, offering a promising direction. However, existing approaches...
Physics-Informed Neural Networks with Architectural Physics Embedding for Large-Scale Wave Field Reconstruction
arXiv:2603.02231v1 Announce Type: new Abstract: Large-scale wave field reconstruction requires precise solutions but faces challenges with computational efficiency and accuracy. The physics-based numerical methods like Finite Element Method (FEM) provide high accuracy but struggle with large-scale or high-frequency problems due...
High-order Knowledge Based Network Controllability Robustness Prediction: A Hypergraph Neural Network Approach
arXiv:2603.02265v1 Announce Type: new Abstract: In order to evaluate the invulnerability of networks against various types of attacks and provide guidance for potential performance enhancement as well as controllability maintenance, network controllability robustness (NCR) has attracted increasing attention in recent...
A Representation-Consistent Gated Recurrent Framework for Robust Medical Time-Series Classification
arXiv:2603.00067v1 Announce Type: new Abstract: Medical time-series data are characterized by irregular sampling, high noise levels, missing values, and strong inter-feature dependencies. Recurrent neural networks (RNNs), particularly gated architectures such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU),...
Scalable Gaussian process modeling of parametrized spatio-temporal fields
arXiv:2603.00290v1 Announce Type: new Abstract: We introduce a scalable Gaussian process (GP) framework with deep product kernels for data-driven learning of parametrized spatio-temporal fields over fixed or parameter-dependent domains. The proposed framework learns a continuous representation, enabling predictions at arbitrary...
Hereditary Geometric Meta-RL: Nonlocal Generalization via Task Symmetries
arXiv:2603.00396v1 Announce Type: new Abstract: Meta-Reinforcement Learning (Meta-RL) commonly generalizes via smoothness in the task encoding. While this enables local generalization around each training task, it requires dense coverage of the task space and leaves richer task space structure untapped....
Efficient Decoder Scaling Strategy for Neural Routing Solvers
arXiv:2603.00430v1 Announce Type: new Abstract: Construction-based neural routing solvers, typically composed of an encoder and a decoder, have emerged as a promising approach for solving vehicle routing problems. While recent studies suggest that shifting parameters from the encoder to the...
Dynamic Spatio-Temporal Graph Neural Network for Early Detection of Pornography Addiction in Adolescents Based on Electroencephalogram Signals
arXiv:2603.00488v1 Announce Type: new Abstract: Adolescent pornography addiction requires early detection based on objective neurobiological biomarkers because self-report is prone to subjective bias due to social stigma. Conventional machine learning has not been able to model dynamic functional connectivity of...
Terminology Rarity Predicts Catastrophic Failure in LLM Translation of Low-Resource Ancient Languages: Evidence from Ancient Greek
arXiv:2602.24119v1 Announce Type: new Abstract: This study presents the first systematic, reference-free human evaluation of large language model (LLM) machine translation (MT) for Ancient Greek (AG) technical prose. We evaluate translations by three commercial LLMs (Claude, Gemini, ChatGPT) of twenty...
Flowette: Flow Matching with Graphette Priors for Graph Generation
arXiv:2602.23566v1 Announce Type: new Abstract: We study generative modeling of graphs with recurring subgraph motifs. We propose Flowette, a continuous flow matching framework, that employs a graph neural network based transformer to learn a velocity field defined over graph representations...
Normalisation and Initialisation Strategies for Graph Neural Networks in Blockchain Anomaly Detection
arXiv:2602.23599v1 Announce Type: new Abstract: Graph neural networks (GNNs) offer a principled approach to financial fraud detection by jointly learning from node features and transaction graph topology. However, their effectiveness on real-world anti-money laundering (AML) benchmarks depends critically on training...
BTTackler: A Diagnosis-based Framework for Efficient Deep Learning Hyperparameter Optimization
arXiv:2602.23630v1 Announce Type: new Abstract: Hyperparameter optimization (HPO) is known to be costly in deep learning, especially when leveraging automated approaches. Most of the existing automated HPO methods are accuracy-based, i.e., accuracy metrics are used to guide the trials of...