DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively

While previous AI Scientist systems can generate novel findings, they often lack the focus to produce scientifically valuable contributions that address pressing human-defined challenges. We introduce DeepScientist, a system designed to overcome this by conducting goal-oriented, fully autonomous scientific discovery over month-long timelines. It formalizes discovery as a Bayesian Optimization problem, operationalized through a hierarchical evaluation process consisting of "hypothesize, verify, and analyze". Leveraging a cumulative Findings Memory, this loop intelligently balances the exploration of novel hypotheses with exploitation, selectively promoting the most promising findings to higher-fidelity levels of validation. Consuming over 20,000 GPU hours, the system generated about 5,000 unique scientific ideas and experimentally validated approximately 1100 of them, ultimately surpassing human-designed state-of-the-art (SOT A) methods on three frontier AI tasks by 183.7%, 1.9%, and 7.9%. This work provides the first large-scale evidence of an AI achieving discoveries that progressively surpass human SOT A on scientific tasks, producing valuable findings that genuinely push the frontier of scientific discovery.Figure 1: Comparison of research progress timelines for AI text detection on the RAID (Dugan et al., 2024). The right panel shows that DeepScientist achieves progress in two weeks that is comparable to three years of human research (Su et al.; Bao et al., a;b; Hu et al., 2023) (left panel). All zero-shot methods, including the system-generated T -Detect, TDT, and P A-Detect, uniformly adopt Falcon-7B (Almazrouei et al., 2023) as the base model. Additionally, all methods produced by DeepScientist demonstrate higher throughput than the previous SOT A method, Binoculars (Hans et al., 2024). 1 Scientific discovery is inherently a process of continuous exploration and trial-and-error, where vast amounts of time and effort are invested to push the boundaries of human knowledge forward by a small step. This principle of persistent, incremental advancement is visible across the history of technology. For example, the decades-long optimization of semiconductor manufacturing has seen the feature size of transistors systematically reduced from micrometers to single-digit nanometers (Moore, 1965). Similarly, the efficiency of photovoltaic cells has been continuously advanced over half a century, with myriad material and architectural innovations pushing conversion rates from nascent single-digit percentages ever closer to their theoretical limits (Green, 1993). These historical trajectories underscore a process where human scientists engage in decades of goal-directed, iterative work to advance the SoT A artifacts continuously. Recently, the emergence of Large Language Models (LLMs) has propelled automated scientific discovery, where LLM-based AI Scientist systems take the lead in exploration (Xie et al., 2025b).