arXiv:2602.06960v2 Announce Type: replace-cross Abstract: Large reasoning models achieve strong performance by scaling inference-time chain-of-thought, but this paradigm suffers from…
arXiv:2602.08272v1 Announce Type: cross Abstract: Reinforcement Learning (RL) has emerged as a crucial method for training or fine-tuning large language…
arXiv:2602.08221v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) often struggles with knowledge conflicts, where model-internal parametric knowledge overrides retrieved evidence,…
arXiv:2602.07081v1 Announce Type: cross Abstract: This paper introduces a generalized federated prompt-tuning framework for practical scenarios where local datasets are…
arXiv:2602.06807v1 Announce Type: cross Abstract: We address the over-constrained planning problem in semi-static environments. The planning objective is to find…
arXiv:2602.00169v2 Announce Type: replace-cross Abstract: The convergence of artificial intelligence and materials science presents a transformative opportunity, but achieving true…
arXiv:2508.01309v2 Announce Type: replace-cross Abstract: The scarcity and high cost of high-quality domain-specific question-answering (QA) datasets limit supervised fine-tuning of…
arXiv:2602.05735v2 Announce Type: replace-cross Abstract: In the era of large foundation models, the quality of embeddings has become a central…
arXiv:2602.05885v2 Announce Type: replace-cross Abstract: High-quality kernel is critical for scalable AI systems, and enabling LLMs to generate such code…
arXiv:2602.06900v1 Announce Type: cross Abstract: Bayesian optimal experimental design (BOED) seeks to maximize the expected information gain (EIG) of experiments.…
