Xinzhiyuan Report
Editors: LRST 好困
【Xinzhiyuan Guide】The future paradigm of scientific research, "Autonomous Generalist Scientist" (AGS), will combine AI and robotics technology to achieve full-process automation from literature review to experimental operation and paper writing. AGS is expected to break through human limitations in scientific research, accelerate the process of scientific discovery, and may trigger a revolution in scientific paradigms.
Can you imagine that at the future Nobel Prize ceremony, the person standing on the podium is not a human scientist, but a robot?
Imagine a laboratory with no human researchers, only AI systems and robots tirelessly analyzing data, designing experiments, operating instruments, discovering laws, writing papers, and even proposing breakthrough theories that change scientific paradigms.
This is not a scene from a science fiction movie, but a possible future picture of scientific research.
Recently, scientists from top international research institutions including the University of Toronto, Italian Institute of Technology, Tsinghua University, Zhejiang University, Rutgers University, Harvard University, Georgia Institute of Technology, and University College London published a forward-looking paper that deeply explored how AI and robotic scientists could subvert the traditional paradigm of scientific research and for the first time proposed that scientific discovery might follow a brand new Scaling Law.
Paper link: https://arxiv.org/pdf/2503.22444
The figure above shows the evolution path of scientific discovery paradigms, from traditional human-centric research to collaborative research between humans and AI/robots, and finally to the realization of Autonomous Generalist Scientist (AGS). This evolution process is not only an upgrade of research tools but also a revolutionary change in the methodology of scientific discovery.
With the development of AGS systems, scientific research will break through two major boundaries:
Physical boundaries – Robotic scientists can conduct experiments in extreme environments (such as space, deep sea, high radiation areas) that humans cannot directly perform;
Knowledge boundaries – AI can integrate cross-disciplinary knowledge, break down professional barriers, and discover connections and patterns that are difficult for humans to perceive.
This shift in the scientific research paradigm may fundamentally change the way and speed of scientific knowledge production, similar to the Industrial Revolution to handicrafts or computers to information processing.
When the AI Brain Meets the Robot Body
The Dual World of Virtual and Physical Scientific Research
The research of current AI scientists is in a stage of vigorous development, but most exist as agents, focusing primarily on programming-related disciplines such as machine learning research and bioinformatics analysis. These systems demonstrate excellent capabilities in the virtual world but cannot interact with the physical world.
Taking DeepMind's AI Scientist and OpenAI's systems as examples, they excel at tasks such as data analysis, pattern recognition, and hypothesis generation, some even capable of autonomously designing and executing computational experiments.
"The AI Scientist" developed by Lu et al. demonstrated how AI systems can achieve automation of scientific discovery through large-scale pre-training and code generation capabilities. The system can parse problems, generate research plans, execute computational code, and even analyze results and draw conclusions.
However, these AI systems have significant limitations. They are primarily confined within predefined computational domains. While they can execute algorithms, optimize parameters, and analyze data, they lack comprehensive "computer-using proficiencies." Human researchers can seamlessly switch between various computing environments, whereas current AI systems cannot replicate this generality. They struggle to navigate complex scientific literature databases and cannot handle various heterogeneous interfaces, authentication requirements, and organizational structures.
The biggest limitation of AI systems is the complete lack of physical experimental capabilities, which fundamentally restricts their scope of scientific research, excluding a large number of empirical science fields that require direct interaction with physical phenomena.
Furthermore, they cannot effectively utilize the professional scientific software ecosystem, including computational modeling environments, analysis tools, and simulation frameworks, all of which require meticulous configuration and cross-platform integration.
This limitation is particularly evident in fields like biology, medicine, and engineering, where research often requires manual experimentation and precise physical manipulation.
On the other hand, most robots in current laboratories are custom-built for specific tasks, with limited flexibility. They can operate efficiently within specific parameters but are often helpless in the face of experimental anomalies, unexpected behavior, or equipment failure.
Existing robots execute predefined sequences of programs and rarely possess the ability for experimental improvisation or protocol adaptation. Despite progress in the field of robot learning, the generalization capabilities of existing systems in different experimental environments remain limited.
This table clearly shows the significant differences in virtual versus physical operational needs across different scientific fields. From natural sciences to social sciences, each field requires a combination of virtual analysis and physical experimentation, but in varying proportions.
For example, physics research needs range from theoretical modeling (virtual) to precise instrument operation (physical); chemistry research relies on molecular modeling and reaction prediction (virtual), while requiring actual synthesis and characterization (physical); biology requires a combination of bioinformatics analysis and laboratory operations. The V/P ratio on the right side of the table shows the relative dependence of different disciplines on computational versus experimental methods, which intuitively explains why scientific research needs the combination of AI and robots – a single system cannot meet the needs of the complete research process.
This dual need for virtual and physical operations highlights the necessity of combining the cognitive capabilities of AI agents with the physical manipulation capabilities of robots.
Scientific research encompasses a dual landscape of virtual and physical operations, both crucial for comprehensive scientific exploration.
Architecture and Operation of Autonomous Generalist Scientists (AGS)
Facing this challenge, researchers proposed the concept of an Autonomous Generalist Scientist (AGS), seamlessly integrating the cognitive capabilities of AI agents with the physical manipulation capabilities of robots to create a system capable of autonomously managing the entire research lifecycle.
The AGS system is composed of five core functional modules, enhancing its capabilities through integrated interaction and reflection mechanisms. As shown in the figure, these modules are:
1. Literature Review: This module autonomously conducts comprehensive research analysis by simulating human interaction with academic databases and journal platforms. Unlike systems relying on APIs, it can navigate various digital environments to search, access, and manage relevant literature, even bypassing subscription barriers. This enables AGS to access the latest research findings that are difficult for traditional AI systems to reach.
2. Proposal Generation: Following literature analysis, this module formulates comprehensive research proposals, clarifying precise problem statements, clear objectives, and innovative hypotheses to advance the field. It develops detailed methodological frameworks and experimental protocols, optimized for virtual simulations and physical implementation, establishing a clear research roadmap.
3. Experiment Execution: This module coordinates the experimental phase of the research process, covering precise planning, resource optimization, and trial execution across both virtual and physical environments. Equipped with advanced robotics and AI technology, the system performs physical manipulations, collects empirical data, and conducts virtual experiments. Furthermore, it dynamically optimizes experimental design through continuous analysis of real-time results and feedback.
4. Paper Preparation: Upon experiment completion, this module synthesizes the findings into a manuscript ready for publication. It performs comprehensive data analysis, interprets results, and formulates substantive conclusions. The system organizes documentation according to standard academic conventions and conducts internal quality assessments, engaging in peer review mechanisms to ensure academic rigor and publication readiness.
5. Reflection and Feedback: This module goes beyond traditional research workflows to enable system-wide continuous improvement. It establishes communication channels between functional components for real-time adjustments while incorporating external input from human collaborators and simulated peer evaluations. Through systematic analysis of feedback, the system optimizes hypotheses, methods, and experimental approaches, ensuring research remains responsive to emerging developments and maximizes the ultimate impact and quality of scientific output.
The AGS brain is the core of the entire system, and its working principle is shown in the figure below:
The working framework of the AGS brain includes two loop systems: the outer loop and the inner loop. The outer loop manages the overall task flow, including sensing environmental information, processing thoughts, acquiring knowledge, and executing actions; the inner loop is responsible for the system's self-reflection and optimization. This dual-loop design allows AGS to continuously improve its reasoning and decision-making capabilities.
In the perception stage, the system collects various forms of information input; the thinking stage involves memory retrieval, knowledge integration, and learning, forming a deep understanding of the problem; the action stage translates the system's decisions into specific operations, including algorithm execution in virtual environments and experimental operations in physical environments.
Meanwhile, the inner loop, through self-reflection mechanisms combined with reasoning methods like Chain of Thought and Tree of Thought, continuously evaluates and improves the system's reasoning process and decision quality. This design enables the AGS system not only to complete designated tasks but also to evolve through accumulated experience, enhancing its ability to solve complex scientific problems.
Evolution and Synergistic Advantages of Robotic Scientists
The development history of robotic scientists shows an evolution trend from dedicated systems to general platforms, as shown in the figure below:
From Robot Scientist in 2004 to Adam in 2009, and then to Mobile robotic chemist in 2019, robotic scientists have undergone nearly 20 years of development. Early systems like Robot Scientist and Adam mainly focused on single disciplinary fields (such as biology), had limited capabilities, and required extensive human guidance.
In recent years, with technological advancements, we have seen the emergence of more general systems, such as Coscientist (2023) and ORGANA (2024). These systems are beginning to integrate AI and robotic capabilities, showing greater potential for autonomy.
This table provides a detailed comparison of the capabilities of various current AI scientist and robotic scientist systems. It shows that most existing systems are still single-domain, focusing on specific disciplines. For example, Adam (2009) is mainly used in biology, PaperRobot (2019) focuses on biomedicine, and AI Scientist (2024) focuses on machine learning. Currently, only a few systems like Coscientist can combine API search and physical experiments, but they still have significant limitations. Future AGS systems are expected to achieve comprehensive breakthroughs in all aspects and become true generalist scientists.
AI agents and robots show distinct complementary advantages in research tasks. As shown in the table above, agents are good at performing tasks such as computer usage, programming, data analysis, and writing in virtual environments, making them particularly suitable for fields like computer science, mathematics, and bioinformatics; while robots play a role in physical and virtual environments, capable of creating and using tools and performing complex physical manipulations, suitable for fields like medicine, biology, chemistry, and space exploration.
Combining the two can achieve a synergistic effect of "1+1>2". AI agents can plan experimental procedures, analyze data, and generate hypotheses, while robots are responsible for performing physical experiments, collecting samples, and operating equipment. This division of labor makes the research process more efficient and avoids the limitations that single systems cannot overcome. For example, in the field of drug discovery, AI can predict potential molecular structures and interactions, while robots can synthesize these molecules and test their actual effects, mutually verifying and complementing each other.
New Scaling Laws for Scientific Discovery
Breaking the Inherent Limitations of Human Scientific Research
Traditional scientific research faces multiple limitations inherent in human nature. First is the limitation of human resources - the global growth rate of researchers is limited and unevenly distributed.
Even in countries with the highest density of researchers, the number per million population is only around a few thousand.
Secondly, there is a time limit - human researchers need rest, entertainment, and family life, with limited time available for focused research each day, usually not exceeding 8-10 hours, and energy and creativity fluctuate over time.
More challenging are cognitive and professional limitations.
Modern scientific research requires processing increasingly large and multidimensional data that often exceeds human cognitive capacity.
An individual researcher is often proficient in only a narrow field and finds it difficult to integrate cross-disciplinary knowledge.
Even top scientists find it challenging to be proficient in multiple fields like physics, chemistry, biology, and computer science simultaneously, leading to knowledge silo effects that hinder cross-disciplinary innovation.
Furthermore, communication barriers in research collaboration are a major challenge. Researchers from different disciplines use different terminology, methodologies, and modes of thinking, which makes effective communication difficult.
These collaborative efforts frequently encounter significant obstacles, including differences in disciplinary cultures, specific methodologies, and the substantial time and resources required for cross-domain coordination.
These persistent barriers diminish the ability for effective communication, conceptual synthesis, and the establishment of coherent research paradigms.
In contrast, AI scientists and robotic scientists have significant advantages:
First is the advantage of scale - AI and robotic systems can be replicated on a large scale at a much lower cost than training human scientists. Once developed successfully, they can be rapidly deployed in hundreds of thousands or millions of instances, significantly expanding the scale of research. Second is the capability for continuous work - AI and robots do not need rest and can work 24/7 uninterruptedly, greatly improving research efficiency. This continuity makes long-term experiment monitoring and data collection more reliable.
In terms of knowledge integration, AI systems particularly excel.
Trained on vast corpora spanning different fields, these models exhibit remarkable proficiency in applying multidisciplinary knowledge, thereby significantly enhancing scientific research.
The inherent capability of generative AI to navigate and bridge different knowledge domains makes it particularly well-suited for interdisciplinary research.
Furthermore, AI and robotic systems have excellent memory capacity and knowledge storage – they can store and quickly retrieve virtually unlimited amounts of information without forgetting details or historical experimental results. In terms of cross-disciplinary integration capability, they can seamlessly connect concepts and methods from different fields, discovering associations that human researchers might overlook.
Most importantly, AI and robotic scientists are highly reproducible – successful experimental methods and findings can be immediately shared with other systems, ensuring maximum utilization of research results and avoiding duplication of effort.
The Knowledge Flywheel and Breaking Dual Boundaries
One of the most revolutionary concepts introduced by the AGS system is the "knowledge flywheel" effect. This concept describes a self-accelerating cycle of knowledge production: each scientific discovery paves the way for subsequent research, creating more discoveries, which in turn further accelerates the research process, forming an exponential growth curve.
Traditionally, this process has been limited by the number of human researchers, their cognitive abilities, and their specialized knowledge. However, with the introduction of AI and robotic scientists, this flywheel could spin at an unprecedented speed.
The operation of the knowledge flywheel in the AGS system can be understood as a multi-level self-reinforcing cycle:
First, the AGS system conducts large-scale parallel research, simultaneously generating new discoveries in multiple fields;
Second, these discoveries are instantly integrated into the system's knowledge base, providing a foundation for subsequent research;
Then, the system uses the enhanced knowledge base to design more complex and targeted research, leading to more breakthrough discoveries; finally, these new discoveries in turn strengthen the knowledge base, accelerating the entire cycle.
This process will break through two key boundaries: physical boundaries and knowledge boundaries.
In terms of physical boundaries, embodied robots can conduct research in extreme environments.
Traditionally, human scientists cannot work for long periods in environments such as space, deep sea, high radiation areas, or extreme temperatures. Embodied robots can overcome these limitations. For example, robots can establish research stations on the surface of the moon or Mars for long-term monitoring and experimentation; they can collect samples and data at deep-sea hydrothermal vents for extended periods; they can manipulate individual molecules or atoms on a micro-scale. These capabilities allow scientific research to expand into previously inaccessible areas.
In terms of knowledge boundaries, AI can integrate and process cross-disciplinary knowledge far beyond human capacity.
It can simultaneously master knowledge from multiple fields such as physics, chemistry, biology, medicine, and engineering, and establish connections between these fields. This ability to integrate knowledge across domains may lead to the birth of entirely new disciplines or solve complex problems that have long been constrained by single-discipline approaches. Furthermore, due to the scale advantage of AI and robotic systems, the growth of scientific discovery and knowledge, as well as the reach of knowledge, will also surpass human limits.
This figure shows the historical trends of global research output and the number of researchers, as well as the expected development curve after the introduction of AGS. Historical data shows that the number of human researchers and scientific output have grown relatively linearly, mainly limited by population and education system capacity.
However, with the introduction of AGS systems, this relationship may undergo a fundamental change. The predicted curve shows that AGS systems may lead to exponential growth in research output, and the number of researchers (both human and AGS) will also significantly increase.
The core of this transformation lies in breaking through the two key limiting factors of traditional scientific knowledge production: the number of researchers and the dispersion of knowledge. AGS systems can be replicated on a large scale, far exceeding the growth potential of human researchers, while overcoming the loss of research efficiency caused by knowledge dispersion.
Due to the inherent limitations in the number of human researchers, co-scientists and AGS systems will introduce new scaling laws for scientific discovery.
With the widespread adoption of AGS systems, we may see a completely new era of scientific research. Not only will the speed of research accelerate, but more importantly, entirely new research directions and breakthroughs will emerge, potentially exceeding the imagination of current human scientists.
Embodied robots adaptable to extreme environments, coupled with the flywheel effect of scientific knowledge accumulation, are expected to continuously break through both physical and intellectual boundaries.
Managing Research Output from Non-Human Scientists
Challenges for the Traditional Academic System and the aiXiv Concept
With the rise of AI and robotic scientists, the traditional academic publishing system will face unprecedented challenges. The research speed of AGS systems will far exceed that of human scientists, potentially generating a massive amount of research output in a short period. The review cycle of traditional journals usually takes months, sometimes even up to a year, and this speed is clearly unable to adapt to the needs of the AGS era.
Even preprint servers like arXiv, while accelerating the initial sharing of research results, still face problems such as limited review resources and difficulty handling the exponential growth in submissions.
Furthermore, the traditional academic system faces unique challenges in evaluating AI-generated content.
How to ensure the accuracy, originality, and reliability of research results produced by AI and robotic scientists? The existing peer review mechanism primarily relies on human experts and may not be able to handle the large volume of AI-generated research output in a timely manner.
At the same time, the traditional academic evaluation system needs to re-examine aspects such as the recognition of research contributions, authorship rights, and the maintenance of scientific credibility.
Facing these challenges, researchers proposed the concept of establishing a new academic platform specifically designed for AI and robotic scientists – aiXiv.
The aiXiv platform aims to provide an open preprint server for research generated by autonomous systems, implementing a tiered review process specifically tailored for AI-driven discovery. This can ensure that AI-generated research adheres to principles of transparency and trustworthiness, address ethical considerations related to scientific communication involving non-human authors, and facilitate its potential submission to traditional journals.
As shown in the figure, the workflow of the aiXiv platform includes the following key steps:
1. Submission Phase: AI scientists and robotic scientists can submit two types of content to the platform - research proposals and full papers. These contents can cover a wide range of scientific fields.
2. Multi-layered Review: Submitted content undergoes a rigorous multi-layered review process, combining the strengths of human experts and AI/robot reviewers, evaluated based on criteria such as feasibility, innovation, logical coherence, and potential scientific impact.
3. Implementation and Development: Proposals published through aiXiv can serve as blueprints for further research, to be implemented by human researchers or other AI/robot scientists, leading to subsequent paper submissions that follow a similar review path.
4. Open Access: The platform provides public application programming interfaces (APIs) and user interfaces, facilitating human and AI reviewers to examine submitted and published proposals and papers, promoting a transparent and collaborative review environment.
5. Bridging Traditional Journals: For completed research published on aiXiv, the platform aims to streamline the subsequent submission process to traditional academic journals, potentially increasing the visibility and impact of AI-driven scientific advancements.
The design of the aiXiv platform considers the balance between scientific rigor and innovation promotion. On the one hand, it ensures the quality and reliability of published content through multi-layered review; on the other hand, it provides a rapid dissemination channel to accelerate the spread of scientific discoveries.
Establishing a platform like aiXiv has the potential to revolutionize scientific publishing, fostering innovation, upholding academic integrity, and ultimately accelerating the pace of scientific discovery.
Standards for Superintelligence
Grading the Capabilities of Autonomous Scientists
With the rapid development of AI and robotics technology, the scientific community has become deeply interested in how to assess the capabilities of these systems. The paper proposes a framework that classifies Autonomous Generalist Scientists (AGS) into different levels based on their autonomy, interaction with simulated and real-world environments, and overall research capabilities, providing a clear picture for understanding the evolution path of AGS.
This table details the six levels of autonomous scientists, from Level 0 (No AI) to Level 5 (Pioneer):
Level 0, No AI: At this fundamental level, scientific research is conducted entirely without significant reliance on generalized AI tools. Research relies exclusively on established methodological approaches and discipline-specific instrumentation. Scientists utilize specialized equipment and software tailored for particular domains, such as spectroscopic devices and analytical platforms in chemistry or statistical packages like SPSS and epidemiological modeling tools in public health. While highly effective within their designated fields, these traditional resources typically lack the capacity for seamless cross-disciplinary integration and require significant human expertise for interpretation and application.
Level 1, Tool-Assisted: This level marks the introduction of simple AI tools designed to assist researchers with specific, narrowly defined tasks. The AI is primarily driven by human scientists, providing basic functionalities such as API-driven data retrieval, automated text generation, and identification of simple cross-disciplinary connections.
Examples of systems at this level include tools like ChatGPT used for text assistance and basic machine learning models for data processing. While the AI can contribute by processing and summarizing information or offering suggestions in response to direct prompts, its capacity for independent action and initiative remains limited.
Level 2, Intelligent Assistant: At this stage, AI systems begin to function as sophisticated research assistants, capable of navigating and synthesizing knowledge from various domains. Under human supervision, these intelligent agents can autonomously perform web-based information gathering, execute virtual simulations, and integrate insights from diverse scientific disciplines.
Systems like OpenDevin and DeepResearch, which assist in data acquisition, analysis, and hypothesis generation, are representative of this level. However, substantial human oversight is still required to define the scope of their activities and interpret the resulting information.
Level 3, Collaborative Partner: AI systems at this level evolve into autonomous collaborative partners in scientific research, seamlessly integrating interaction with both virtual and physical environments. Equipped with advanced robotics, they can conduct experiments in fields such as biology, engineering, and medicine, performing precise manipulations in the physical world.
These systems are capable of autonomously executing complex interdisciplinary tasks but still collaborate with human scientists, leveraging each other's strengths. Advanced robotic platforms incorporating sensor data processing, semi-autonomous experimental execution, and integrated data analysis are key examples of this level.
Level 4, Autonomous Researcher: At this stage, AI and robots operate with significant independence, requiring minimal human guidance. These systems are capable of conducting advanced research in both simulated and real-world environments, employing autonomous information retrieval and synthesizing knowledge from a wide range of domains.
They can generate novel insights and propose innovative solutions by identifying and connecting data points from previously disparate fields. Artificial Generalist Intelligent Robots (AGIR) represent this category; they push the boundaries of interdisciplinary research while still benefiting from occasional human oversight or intervention for complex problem-solving or ethical considerations.
Level 5, Pioneer: The pinnacle level represents fully autonomous systems that surpass human capabilities in scientific research. Referred to as Artificial Superintelligent Robots (ASIR), these systems operate entirely independently across all environments—virtual, physical, and experimental—capable of conducting groundbreaking research without any human intervention. They not only synthesize cross-disciplinary knowledge but also innovate and formulate entirely new scientific principles.
Their work leads to unprecedented scientific discoveries, positioning them as front-running pioneers in AI-driven research. While acknowledging the inherent uncertainty in achieving Level 5 autonomy due to substantial technological, ethical, and practical challenges, this level serves as an ambitious long-term goal for the field, motivating continued exploration and innovation in autonomous scientific discovery.
This classification framework not only describes the current state but also provides a roadmap for the future development of AGS systems. Most current systems are at Level 1 and 2, with a few reaching some functions of Level 3. Achieving true Level 4 and 5 AGS systems is a long-term goal that requires breakthroughs in multiple technical fields.
This figure shows the historical and future development timeline of automated research. From the initial stage of human use of tools, to the current stage of knowledge providers and agents, and then to the future "human-level" and "superhuman" stages, automated research has undergone a long evolutionary process. Currently, we are in the stage of transitioning from "chatting" to "agents", and the next decade may see a shift towards true "robots" and "human-level" capabilities. Finally, possibly after 2030, we may witness the emergence of "superhuman" level autonomous scientists.
It is worth noting that these time predictions have a high degree of uncertainty, depending on the pace of progress in multiple technical fields, including large model capabilities, robotics technology, autonomous learning, and environmental adaptability, among others. However, the timeline provides a valuable reference framework to help us understand the development trajectory of this field.
Research Intelligence Beyond Humans
When we consider the standards for Artificial Superintelligence (ASI), an important perspective emerges: the ability for scientific discovery may be the best standard for assessing superintelligence, contrasting with intelligence assessment methods usually based on IQ tests or language generation capabilities.
Scientific discovery requires deep insight, creative thinking, complex reasoning, and cross-domain knowledge integration - these are hallmarks of true intelligence.
Reasons for scientific discovery capability as a standard for superintelligence include:
1. Complex Problem Solving: Scientific research involves solving extremely complex and often ill-defined problems, requiring exploratory thinking and innovative approaches.
2. Creative Hypothesis Generation: Proposing innovative hypotheses requires the system to possess the ability to go beyond existing knowledge boundaries, which is a core characteristic of true intelligence.
3. Integration of Multi-domain Knowledge: Scientific breakthroughs often occur at the intersection of different disciplines, requiring the integration and transformation of concepts from multiple knowledge domains.
4. Balance of Theory and Experimentation: Excellent scientific work requires the combination of theoretical reasoning and experimental verification, which is a manifestation of multimodal intelligence.
5. Long-term Planning and Flexible Adaptation: Scientific research requires formulating long-term research plans while flexibly adjusting direction based on new findings.
When AI systems can independently make breakthrough discoveries that surpass human scientists, we can truly discuss the realization of superintelligence. This is not merely a quantitative increase (processing more data or generating more papers), but a qualitative leap (proposing entirely new paradigms or theories).
Only achieving breakthrough progress in science can validate whether an AI has reached the level of superintelligence; this is the essential difference from Artificial General Intelligence.
From this perspective, the hallmark of super AI will be discoveries that redefine scientific fields, such as proposing new laws of physics, solving long-standing mathematical problems, or discovering entirely new treatments. These achievements require true original thinking, not merely the reorganization of existing knowledge.
However, this standard also raises a series of questions: How to verify new theories generated by AI? How to ensure the reliability of these discoveries? How to ensure scientific rigor while maintaining originality? These questions highlight the importance of establishing appropriate evaluation and verification mechanisms.
The emergence of super-scientific intelligence will also raise profound philosophical and ethical questions. If AI can make scientific discoveries that humans cannot understand, how do we verify their correctness? If a super AI proposes theories that challenge the human scientific paradigm, how should we respond? These questions touch upon the essence of science and the foundations of human cognition.
We envision that AGS systems can catalyze a transformative shift in scientific exploration, fostering more effective and innovative approaches capable of overcoming current barriers and ultimately advancing scientific progress in unprecedented ways.
This vision, while ambitious, is gradually moving from science fiction to reality with the rapid development of AI and robotics technology.
Conclusion: A New Era of Science and Intelligence
The integration of artificial intelligence and robotics is ushering in a new era of scientific research. The concept of the Autonomous Generalist Scientist (AGS) represents an unprecedented paradigm shift that will reshape the way, speed, and boundaries of knowledge discovery.
By integrating the cognitive capabilities of AI agents with the physical manipulation capabilities of robots, AGS systems are expected to overcome fundamental limitations in traditional scientific research, achieving full-process automation from literature review to hypothesis generation, experimental execution, and paper writing.
The new scaling law for scientific discovery discussed in this paper reveals a key insight: with the widespread deployment of AGS systems, scientific progress may follow a growth curve completely different from the human-dominated era.
This transformation not only means an acceleration of research speed but more importantly signifies the expansion of the boundaries of scientific exploration – from extreme environments to the microscopic world, from cross-disciplinary intersection to the construction of entirely new theories.
The acceleration of the knowledge flywheel effect will trigger an explosive growth in scientific discovery, propelling human civilization into a new era of knowledge explosion.
To adapt to this new paradigm, the academic ecosystem also needs to undergo corresponding adjustments. Platforms like aiXiv, specifically designed for AI and robotic scientists, will reshape the scientific evaluation system and knowledge dissemination model, ensuring a balance between scientific rigor and innovation.
At the same time, scientific discovery capability as a standard for assessing superintelligence provides a new perspective for understanding and developing advanced AI systems.
Importantly, AGS should not be seen as a replacement for human scientists but as powerful research partners. This collaborative relationship will combine the computational power, memory capacity, and cross-domain integration capabilities of AI with human creative thinking, intuition, and ethical judgment, collectively pushing the boundaries of scientific exploration.
As physicist Richard Feynman said: "The pleasure of science is in discovering how things work, not in proving what you already know."
AGS systems will provide humanity with unprecedented tools to explore the unknown, solve mysteries, and expand the boundaries of knowledge.
Future research directions include the practical implementation of AGS systems, performance evaluation, and social impact analysis. As technology advances, we need to continuously reflect on and adjust the design and application of AGS systems, ensuring they serve the common goals of human well-being and scientific progress. The development of social, ethical, and regulatory frameworks is equally important to ensure that this technological revolution brings opportunities rather than risks.
This is just the first step in exploring this exciting topic. We will delve deeper into the new scaling laws of scientific discovery and their impact on the research ecosystem, the technical realization paths of AI and robotic scientists, and the broader implications of this paradigm shift for society, the economy, and education. The new era of science and intelligence has begun, and let us look forward to where this journey will take human civilization.
References:
https://arxiv.org/pdf/2503.22444