Breakthroughs, discoveries, and DIY tips sent every weekday. A Canadian kid is proof that major scientific discoveries don't always have to come from grizzled researchers with fancy equipment. Two years ago, then-8-year-old Lucas Atchison went on a family trip to Point Farms Provincial Park in Ontario. Armed with a metal detector he had just received as a birthday present, Atchison dutifully scanned the area, hoping to hear that coveted "beep." Eagerly digging into the site, Lucas uncovered a metal spike, which his father initially dismissed as something used to tie up boats.

Terry Pratchett was fond of saying that million-to-one chances crop up nine times out of ten. On the face of it, this sentence is mathematically absurd, but in the fantasy world of Pratchett's Discworld books, powered by the magic of narratives, it makes perfect sense. Of course heroes are always going to face incredible odds, and of course they are almost always going to overcome them, because that is what heroes do.

We study the problem of Active Simple Hypothesis Testing (ASHT) whe re an agent is faced with the problem of choosing between m different simple hypotheses after observing T samples. At the end of T samples, it has to output one of the m hypothesis. The distinguishing difference from the usual hypothes is testing scenario is the ability to choose one of K actions and observe the corresponding sample for that action. Th is ability to control the samples in this way makes the problem more interesting and difficult compared to the usual hypothesis testing with no control over the sample generation. The performance of the agent is meas ured in terms of the error probability its decision incurs. The above theoretical problem is a model for many practica l scenarios-A cosmetic drug trial often involve a testing period where the outcome of interest is to identify the best product after the trial period, choosing a channel from a set of channels before commencing communications, placeme nt of sensors in certain set of positions so as to minimize signal error. Any situation which require a period of testing b efore committing to a final decision with only certain fixed budget of samples (that is an inability to request additio nal samples) can be modeled effectively using ASHT and its more general version - Fixed Budget Best Arm Identific ation (FB-BAI). We intend to study the ASHT problem in the large deviation setting with the quantity of interest being the minimax error exponent over the hypotheses, that is, the worst case er ror exponent over the hypotheses.

Abstract: Automated experimentation has the potential to revolutionize scientific discovery, but its effectiveness depends on well - defined optimization targets, which are often uncertain or probabilistic in real - world settings. In this work, we demonstrate the appli cation of Multi - Objective Bayesian Optimization ( MOBO) to balance multiple, competing rewards in autonomous experimentation. Using scanning probe microscopy ( SPM) imaging, one of the most widely used and foundational SPM modes, we show that MOBO can optimize imaging parameters to enhance measurement quality, reproducibility, and efficiency. A key advantage of this approach is the ability to compute and analyze the Pareto front, which not only guides optimization but also provides physical insights into the trade - offs between different objectives. Additionally, MOBO offers a natural framework for human - in - the - loop decision - making, enabling researchers to fine - tune ex perimental trade - offs based on domain expertise. By standardizing high - quality, reproducible measurements and integrating human input into AI - driven optimization, this work highlights MOBO as a powerful tool for advancing autonomous scientific discovery. I. Introduction Automated scientific discovery is rapidly emerging as a transformative research paradigm, reshaping experimental methodologies through the integration of automated instrumentation, AI - driven decision - making, and multi - tool workflows [1, 2] . By enabling autonomous hypothesis testing, adaptive experimentation, and real - time optimization, these systems have the potential to significantly accelerate discoveries across various scientific domains [18 - 21] . A fundamental requirement for active discovery workflows is the definition of optimization targets or reward functions that drive the iterative learning process [18] . These reward functions form the foundation of autonomous workflows, guiding experimental decisions and facilitating interoperability among multiple tools in complex research environments.

Scientific discovery is poised for rapid advancement through advanced robotics and artificial intelligence. Current scientific practices face substantial limitations as manual experimentation remains time-consuming and resource-intensive, while multidisciplinary research demands knowledge integration beyond individual researchers' expertise boundaries. Here, we envision an autonomous generalist scientist (AGS) concept combines agentic AI and embodied robotics to automate the entire research lifecycle. This system could dynamically interact with both physical and virtual environments while facilitating the integration of knowledge across diverse scientific disciplines. By deploying these technologies throughout every research stage -- spanning literature review, hypothesis generation, experimentation, and manuscript writing -- and incorporating internal reflection alongside external feedback, this system aims to significantly reduce the time and resources needed for scientific discovery. Building on the evolution from virtual AI scientists to versatile generalist AI-based robot scientists, AGS promises groundbreaking potential. As these autonomous systems become increasingly integrated into the research process, we hypothesize that scientific discovery might adhere to new scaling laws, potentially shaped by the number and capabilities of these autonomous systems, offering novel perspectives on how knowledge is generated and evolves. The adaptability of embodied robots to extreme environments, paired with the flywheel effect of accumulating scientific knowledge, holds the promise of continually pushing beyond both physical and intellectual frontiers.

Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they largely explore variants of existing codebases or similarly constrained design spaces, and (2) they produce large volumes of research artifacts (such as automatically generated papers and code) that are typically evaluated using conference-style paper review with limited evaluation of code. In this work we introduce CodeScientist, a novel ASD system that frames ideation and experiment construction as a form of genetic search jointly over combinations of research articles and codeblocks defining common actions in a domain (like prompting a language model). We use this paradigm to conduct hundreds of automated experiments on machine-generated ideas broadly in the domain of agents and virtual environments, with the system returning 19 discoveries, 6 of which were judged as being both at least minimally sound and incrementally novel after a multi-faceted evaluation beyond that typically conducted in prior work, including external (conference-style) review, code review, and replication attempts. Moreover, the discoveries span new tasks, agents, metrics, and data, suggesting a qualitative shift from benchmark optimization to broader discoveries.

DETR-based methods, which use multi-layer transformer decoders to refine object queries iteratively, have shown promising performance in 3D indoor object detection. However, the scene point features in the transformer decoder remain fixed, leading to minimal contributions from later decoder layers, thereby limiting performance improvement. Recently, State Space Models (SSM) have shown efficient context modeling ability with linear complexity through iterative interactions between system states and inputs. Inspired by SSMs, we propose a new 3D object DEtection paradigm with an interactive STate space model (DEST). In the interactive SSM, we design a novel state-dependent SSM parameterization method that enables system states to effectively serve as queries in 3D indoor detection tasks. In addition, we introduce four key designs tailored to the characteristics of point cloud and SSM: The serialization and bidirectional scanning strategies enable bidirectional feature interaction among scene points within the SSM. The inter-state attention mechanism models the relationships between state points, while the gated feed-forward network enhances inter-channel correlations. To the best of our knowledge, this is the first method to model queries as system states and scene points as system inputs, which can simultaneously update scene point features and query features with linear complexity. Extensive experiments on two challenging datasets demonstrate the effectiveness of our DEST-based method. Our method improves the GroupFree baseline in terms of AP50 on ScanNet V2 (+5.3) and SUN RGB-D (+3.2) datasets. Based on the VDETR baseline, Our method sets a new SOTA on the ScanNetV2 and SUN RGB-D datasets.

The integration of Agentic AI into scientific discovery marks a new frontier in research automation. These AI systems, capable of reasoning, planning, and autonomous decision-making, are transforming how scientists perform literature review, generate hypotheses, conduct experiments, and analyze results. This survey provides a comprehensive overview of Agentic AI for scientific discovery, categorizing existing systems and tools, and highlighting recent progress across fields such as chemistry, biology, and materials science. We discuss key evaluation metrics, implementation frameworks, and commonly used datasets to offer a detailed understanding of the current state of the field. Finally, we address critical challenges, such as literature review automation, system reliability, and ethical concerns, while outlining future research directions that emphasize human-AI collaboration and enhanced system calibration. The rapid advancements of Large Language Models (LLMs) (Touvron et al., 2023; Anil et al., 2023; Achiam et al., 2023) have opened a new era in scientific discovery, with Agentic AI systems (Kim et al., 2024; Guo et al., 2023; Wang et al., 2024; Abramovich et al., 2024) emerging as powerful tools for automating complex research workflows. Unlike traditional AI, Agentic AI systems are designed to operate with a high degree of autonomy, allowing them to independently perform tasks such as hypothesis generation, literature review, experimental design, and data analysis. These systems have the potential to significantly accelerate scientific research, reduce costs, and expand access to advanced tools across various fields, including chemistry, biology, and materials science. Recent efforts have demonstrated the potential of LLM-driven agents in supporting researchers with tasks such as literature reviews, experimentation, and report writing. Prominent frameworks, including LitSearch (Ajith et al., 2024), ResearchArena (Kang & Xiong, 2024), SciLitLLM (Li et al., 2024c), CiteME (Press et al., 2024), ResearchAgent (Baek et al., 2024) and Agent Laboratory (Schmidgall et al., 2025), have made strides in automating general research workflows, such as citation management, document discovery, and academic survey generation. However, these systems often lack the domain-specific focus and compliance-driven rigor essential for fields like biomedical domain, where the structured assessment of literature is critical for evidence synthesis.

This paper provides a unifying view of optimal kernel hypothesis testing across the MMD two-sample, HSIC independence, and KSD goodness-of-fit frameworks. Minimax optimal separation rates in the kernel and $L^2$ metrics are presented, with two adaptive kernel selection methods (kernel pooling and aggregation), and under various testing constraints: computational efficiency, differential privacy, and robustness to data corruption. Intuition behind the derivation of the power results is provided in a unified way accross the three frameworks, and open problems are highlighted.

We only observe their transformed versions h(\mathbf{x_s i}) and g(\mathbf{x_t i}), for some known function class h(\cdot) and g(\cdot) . Our goal is to perform a statistical test checking if P_{\rm source} P_{\rm target} while removing the distortions induced by the transformations. This problem is closely related to concepts underlying numerous domain adaptation algorithms, and in our case, is motivated by the need to combine clinical and imaging based biomarkers from multiple sites and/or batches, where this problem is fairly common and an impediment in the conduct of analyses with much larger sample sizes. We develop a framework that addresses this problem using ideas from hypothesis testing on the transformed measurements, where in the distortions need to be estimated {\it in tandem} with the testing. We derive a simple algorithm and study its convergence and consistency properties in detail, and we also provide lower-bound strategies based on recent work in continuous optimization.