Quality follows upgrading
Academic
arXiv:2604.05335v1 Announce Type: new Abstract: Achieving resilient and high-quality manufacturing requires reliable data-driven anomaly detection methods that are capable of addressing differences in behaviors among …
arXiv:2604.05438v1 Announce Type: new Abstract: Long-context generation is increasingly limited by decode-time key-value (KV) cache traffic, particularly when KV is offloaded beyond GPU memory. Query-aware …
arXiv:2604.05051v1 Announce Type: new Abstract: Patients are increasingly turning to large language models (LLMs) with medical questions that are complex and difficult to articulate clearly. …
arXiv:2604.05217v1 Announce Type: new Abstract: Neural language models process sequences of words, but the mathematical operations inside them are insensitive to the order in which …
arXiv:2604.05834v1 Announce Type: new Abstract: Multimodal contrastive learning is increasingly enriched by going beyond image-text pairs. Among recent contrastive methods, Symile is a strong approach …
arXiv:2604.05333v1 Announce Type: new Abstract: Skill usage has become a core component of modern agent systems and can substantially improve agents' ability to complete complex …
arXiv:2604.05624v1 Announce Type: new Abstract: Named Entity Recognition (NER) is a foundational NLP task, yet research in Yor\`ub\'a has been constrained by limited and domain-specific …
arXiv:2604.05655v1 Announce Type: new Abstract: This work characterizes large language models' chain-of-thought generation as a structured trajectory through representation space. We show that mathematical reasoning …
arXiv:2604.04988v1 Announce Type: new Abstract: Modern deployment often requires trading accuracy for efficiency under tight CPU and memory constraints, yet common compression proxies such as …
arXiv:2604.04983v1 Announce Type: new Abstract: We present Territory Paint Wars, a minimal competitive multi-agent reinforcement learning environment implemented in Unity, and use it to systematically …
arXiv:2604.05394v1 Announce Type: new Abstract: Physics-based character animation has become a fundamental approach for synthesizing realistic, physically plausible motions. While current data-driven deep reinforcement learning …
arXiv:2604.05002v1 Announce Type: new Abstract: Learning from weak or proxy supervision is common when ground-truth labels are unavailable, yet robustness under distribution shift remains poorly …