Quality follows upgrading
Ever a pioneer, ever a student — learning through our mistakes and failures
전체 전문분석자료
arXiv:2604.02881v1 Announce Type: new Abstract: Weight-space model merging combines independently fine-tuned models without accessing original training data, offering a practical alternative to joint training. While …
arXiv:2604.02699v1 Announce Type: new Abstract: A previous study reported that E-Prime (English without the verb "to be") selectively altered reasoning in language models, with cross-model …
arXiv:2604.02556v1 Announce Type: new Abstract: Large language models (LLMs) have grown beyond the memory capacity of single GPU devices, necessitating quantization techniques for practical deployment. …
arXiv:2604.02482v1 Announce Type: new Abstract: This paper proposes a framework for Structural Extrapolated Data GEneration (SEDGE) based on suitable assumptions on the underlying data generating …
arXiv:2604.02633v1 Announce Type: new Abstract: Non-Exemplar Continual Graph Learning (NECGL) seeks to eliminate the privacy risks intrinsic to rehearsal-based paradigms by retaining solely class-level prototype …
arXiv:2604.03174v1 Announce Type: new Abstract: Large language models (LLMs) encode vast world knowledge in their parameters, yet they remain fundamentally limited by static knowledge, finite …
arXiv:2604.02438v1 Announce Type: new Abstract: The deployment of reinforcement learning (RL)-based controllers on physical systems is often limited by poor generalization to real-world scenarios, known …
arXiv:2604.02351v1 Announce Type: new Abstract: Machine learning models deployed in non-stationary environments are exposed to temporal distribution shift, which can erode predictive reliability over time. …
arXiv:2604.02971v1 Announce Type: new Abstract: Recent agentic search systems have made substantial progress by emphasising deep, multi-step reasoning. However, this focus often overlooks the challenges …
arXiv:2604.02423v1 Announce Type: new Abstract: Large language models exhibit sycophancy: the tendency to shift outputs toward user-expressed stances, regardless of correctness or consistency. While prior …
arXiv:2604.03110v1 Announce Type: new Abstract: Knowledge distillation is an effective technique for pre-trained language model compression. However, existing methods only focus on the knowledge distribution …
arXiv:2604.02476v1 Announce Type: new Abstract: This paper examines the role of threshold logic in understanding generative artificial intelligence. Threshold functions, originally studied in the 1960s …