Dernière mise à jour : 2026-01-13 01:08:01
🔄 Actualiser maintenantRésumé IA : arXiv:2601.05256v1 Announce Type: new Abstract: Inland water monitoring is vital for safeguarding public health and ecosystems, enabling timely interventions to mitigate risks. Existing methods often address isolated sub-problems such as cyanobacteria, chlorophyll, or other quality indicators separ...
Résumé IA : arXiv:2601.05298v1 Announce Type: new Abstract: Additive manufacturing (AM) relies critically on understanding and extrapolating process-property relationships; however, existing data-driven approaches remain limited by fragmented knowledge representations and unreliable extrapolation under sparse ...
Résumé IA : arXiv:2601.05302v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as autonomous agents in strategic and social interactions. Although recent studies suggest that assigning personality traits to LLMs can influence their behavior, how personality steering affects coop...
Résumé IA : arXiv:2601.05330v1 Announce Type: new Abstract: Predicting enzyme-substrate interactions has long been a fundamental problem in biochemistry and metabolic engineering. While existing methods could leverage databases of expert-curated enzyme-substrate pairs for models to learn from known pair intera...
Résumé IA : arXiv:2601.05376v1 Announce Type: new Abstract: Persona conditioning can be viewed as a behavioral prior for large language models (LLMs) and is often assumed to confer expertise and improve safety in a monotonic manner. However, its effects on high-stakes clinical decision-making remain poorly cha...
Résumé IA : arXiv:2601.05296v1 Announce Type: new Abstract: The pervasive "memory wall" bottleneck is significantly amplified in modern large-scale Mixture-of-Experts (MoE) architectures. MoE's inherent architectural sparsity leads to sparse arithmetic compute and also introduces substantial activation memory ...
Résumé IA : arXiv:2601.05300v1 Announce Type: new Abstract: Reasoning oriented large language models often expose explicit "thinking" as long, turn-global traces at the start of every response, either always on or toggled externally at inference time. While useful for arithmetic, programming, and problem solvi...
Résumé IA : arXiv:2601.05304v1 Announce Type: new Abstract: Neuro-symbolic reasoning systems face fundamental challenges in maintaining semantic coherence while satisfying physical and logical constraints. Building upon our previous work on Ontology Neural Networks, we present an enhanced framework that integr...
Résumé IA : arXiv:2601.05352v1 Announce Type: new Abstract: Federated learning (FL) has emerged as a transformative distributed learning paradigm, enabling multiple clients to collaboratively train a global model under the coordination of a central server without sharing their raw training data. While FL offer...
Résumé IA : arXiv:2601.05353v1 Announce Type: new Abstract: Accurate forecasting of blood glucose from CGM is essential for preventing dysglycemic events, thus enabling proactive diabetes management. However, current forecasting models treat blood glucose readings captured using CGMs as a numerical sequence, e...