We present the first optimistic concurrent B-skiplist, built on optimistic lock coupling (OLC). OLC replaces the multi-cache-line RW lock with a compact, single-cache-line lock: readers validate a version number without writing to shared memory, avoiding cache invalidations and retrying only on a detected conflict. Although OLC was thought poorly suited to skiplists, where a single per-element lock may span structurally unrelated levels, the B-skiplist's blocked layout places each level (and associated lock) in its own node. We exploit the B-skiplist's upfront knowledge of promotion height with a hybrid top-down scheme that traverses upper levels optimistically and locks only from the promotion level down, so most inserts never dirty a cache line. On 128 threads, the proposed optimistic B-skiplist achieves $1.2-10\times$ higher throughput over state-of-the-art concurrent skiplist implementations. Furthermore, the optimistic B-skiplist achieves $0.8-4.3\times$ the throughput compared to high-performance optimistic B+-tree implementations based on RW locks and OLC. Finally, the optimistic B-skiplist achieves up to $13\times$ lower latency than concurrent skiplists and tree-based indices, and is within $1.6\times$ of the latency of the OLC-based B+-tree.
Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, David Ha
AI is increasingly playing a pivotal role in transforming how scientific discoveries are made. We introduce The AI Scientist-v2, an end-to-end agentic system capable of producing the first entirely AI generated peer-review-accepted workshop paper. This system iteratively formulates scientific hypotheses, designs and executes experiments, analyzes and visualizes data, and autonomously authors scientific manuscripts. Compared to its predecessor (v1, Lu et al., 2024 arXiv:2408.06292), The AI Scientist-v2 eliminates the reliance on human-authored code templates, generalizes effectively across diverse machine learning domains, and leverages a novel progressive agentic tree-search methodology managed by a dedicated experiment manager agent. Additionally, we enhance the AI reviewer component by integrating a Vision-Language Model (VLM) feedback loop for iterative refinement of content and aesthetics of the figures. We evaluated The AI Scientist-v2 by submitting three fully autonomous manuscripts to a peer-reviewed ICLR workshop. Notably, one manuscript achieved high enough scores to exceed the average human acceptance threshold, marking the first instance of a fully AI-generated paper successfully navigating a peer review. This accomplishment highlights the growing capability of AI in conducting all aspects of scientific research. We anticipate that further advancements in autonomous scientific discovery technologies will profoundly impact human knowledge generation, enabling unprecedented scalability in research productivity and significantly accelerating scientific breakthroughs, greatly benefiting society at large. We have open-sourced the code at https://github.com/SakanaAI/AI-Scientist-v2 to foster the future development of this transformative technology. We also discuss the role of AI in science, including AI safety.