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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 …
arXiv:2604.01762v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has emerged as a crucial paradigm for adapting large language models (LLMs) under constrained computational budgets. However, …
arXiv:2604.01595v1 Announce Type: new Abstract: Seizure detection from EEG signals is highly challenging due to complex spatiotemporal dynamics and extreme inter-patient variability. To model them, …
arXiv:2604.01308v1 Announce Type: new Abstract: Designing reliable integrated energy systems for industrial processes requires optimization and verification models across multiple fidelities, from architecture-level sizing to …
arXiv:2604.00842v1 Announce Type: new Abstract: Proactive agents that anticipate user needs and autonomously execute tasks hold great promise as digital assistants, yet the lack of …
arXiv:2604.00790v1 Announce Type: new Abstract: While large language models (LLMs) have demonstrated strong performance on complex reasoning tasks such as competitive programming (CP), existing methods …
arXiv:2604.00323v1 Announce Type: new Abstract: Online abuse has grown increasingly complex, spanning toxic language, harassment, manipulation, and fraudulent behavior. Traditional machine-learning approaches dependent on static …
arXiv:2604.00015v1 Announce Type: new Abstract: We present ASCAT (Arabic Scientific Corpus for Advanced Translation), a high-quality English-Arabic parallel benchmark corpus designed for scientific translation evaluation …
arXiv:2604.00174v1 Announce Type: new Abstract: This study investigates the relationship between the phonological and morphological structure of Polish words and their meanings using Distributional Semantics. …
arXiv:2604.01378v1 Announce Type: new Abstract: Offline reinforcement learning (RL) has received increasing attention for learning policies from previously collected data without interaction with the real …
arXiv:2604.00003v1 Announce Type: cross Abstract: This study evaluates the reliability of information extraction approaches from KRS documents using three strategies: LLM only, Hybrid Deterministic - …
arXiv:2604.01279v1 Announce Type: new Abstract: We introduce Sven (Singular Value dEsceNt), a new optimization algorithm for neural networks that exploits the natural decomposition of loss …