Author: Denis Avetisyan
A new framework empowers AI agents to not just use tools, but to autonomously develop and refine the skills needed for genuine scientific exploration.
CASCADE enables cumulative agentic skill creation through autonomous development and evolution, demonstrating potential for self-improving scientific discovery.
Current large language model agents are hampered by reliance on predefined tools, limiting adaptability in complex scientific endeavors. This limitation motivates the development of CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution, a novel framework enabling agents to autonomously acquire and refine skills via continuous learning and self-reflection. We demonstrate that CASCADE significantly enhances performance on materials science and chemistry research tasks-achieving a 93.3% success rate-and facilitates integration into autonomous experimentation workflows. Could this approach represent a crucial step toward scalable, AI-assisted scientific discovery and collaborative research environments?
The Inevitable Bottleneck: Beyond Tools, Towards Autonomy
Contemporary large language models largely function within a constrained framework known as the ‘LLM + Tool Use Paradigm’. This approach centers on providing the model with access to specific, pre-defined tools – such as calculators, search engines, or specialized databases – and instructing it on how to utilize these tools for given tasks. However, this reliance introduces significant limitations; the model’s capabilities are fundamentally bound by the scope of available tools and the precision of the instructions provided. Consequently, these systems exhibit limited adaptability when confronted with novel situations or tasks that fall outside their programmed parameters, requiring substantial human intervention to both define appropriate tools and accurately interpret the resulting outputs. The current paradigm, while demonstrating impressive feats of information processing, represents a bottleneck in the pursuit of genuinely autonomous scientific discovery.
The current reliance on the ‘LLM + Tool Use Paradigm’ presents a considerable obstacle to accelerating scientific discovery. While large language models demonstrate proficiency when given explicit instructions and access to pre-defined tools, the process remains heavily dependent on human researchers to not only formulate the research questions and select appropriate tools, but also to meticulously interpret the often-complex outputs. This necessitates substantial human effort in translating model responses into meaningful scientific insights, effectively creating a bottleneck where progress is limited by the speed of human analysis rather than the computational power of the AI. Consequently, the potential for automated hypothesis generation and data-driven discovery is hampered, as the system requires constant human guidance to bridge the gap between computation and genuine understanding.
The prevailing strategy of simply increasing the size of large language models (LLMs) is reaching its limits as a path toward genuinely scientific artificial intelligence. While scaling enhances performance on existing tasks, it fails to address the fundamental need for AI systems to learn new skills independently – to move beyond pre-programmed responses and develop novel approaches to problem-solving. True scientific advancement demands an ability to formulate hypotheses, design experiments, and interpret data without constant human direction; this necessitates a paradigm shift toward autonomous skill acquisition, where AI systems can proactively identify knowledge gaps, seek out relevant information, and build capabilities beyond their initial training. Such a system wouldn’t merely process data, but actively do science, potentially accelerating discovery in ways currently unimaginable.
CASCADE: Cultivating Skill, Not Simply Deploying Tools
The CASCADE framework operationalizes the ‘LLM + Skill Acquisition Paradigm’ by moving beyond static LLM responses to enable agents to iteratively improve performance on tasks. This is achieved through a cycle of action, observation, and reflection, where the LLM doesn’t simply provide a solution, but engages with an environment, receives feedback on its actions, and then uses that feedback to refine its approach. Crucially, this refinement isn’t pre-programmed; the agent actively analyzes its performance – identifying errors, understanding their causes, and adjusting its strategy – effectively learning from experience. This continuous loop of interaction and self-assessment allows the agent to acquire and refine skills dynamically, without requiring explicit retraining or human intervention.
CASCADE utilizes a dual-solver architecture consisting of DeepSolver and SimpleSolver to address varying problem complexities. DeepSolver is designed for tasks requiring autonomous problem-solving, employing iterative refinement and exploration to achieve solutions. Conversely, SimpleSolver functions as a rapid, knowledge-based system, efficiently addressing problems solvable through direct information retrieval and application of established knowledge. This allows CASCADE to prioritize computationally intensive, complex problems for DeepSolver while offloading simpler queries to SimpleSolver, optimizing overall efficiency and resource allocation.
CASCADE employs a dual-memory system to facilitate both immediate task performance and sustained skill development. Session-wise memory, a short-term buffer, stores information relevant to the current interaction, enabling contextual awareness and dynamic adaptation within a single problem-solving episode. Simultaneously, consolidated memory serves as a long-term repository, accumulating learned patterns, refined strategies, and generalized knowledge across multiple sessions. Data from session-wise memory is selectively distilled and transferred to consolidated memory, allowing the agent to build upon prior experience and improve its performance over time. This architecture distinguishes between retaining information crucial for the present task and preserving knowledge for future application, optimizing both immediate responsiveness and long-term learning capabilities.
Autonomous Experimentation: A System in Action
The CASCADE framework is currently operational in automated laboratory environments, fully integrating the processes of experimental design, data analysis, and iterative refinement. This automation is achieved through a closed-loop system where CASCADE proposes experiments, analyzes resulting data using machine learning algorithms, and subsequently adjusts experimental parameters for subsequent iterations without human intervention. The system manages the entire scientific workflow, from hypothesis generation to conclusion, enabling accelerated research cycles and increased throughput in materials science and chemistry applications. This autonomous operation reduces reliance on manual experimentation and allows for the exploration of a wider experimental space.
CASCADE leverages substantial computational resources, specifically the National Energy Research Scientific Computing Center (NERSC) and the Extreme Science and Engineering Discovery Environment (XSEDE), to address computationally intensive challenges within materials science and chemistry. These high-performance computing (HPC) facilities provide the necessary processing power and infrastructure for complex simulations, large-scale data analysis, and the iterative refinement cycles integral to autonomous scientific discovery. The utilization of NERSC and XSEDE enables CASCADE to handle problems exceeding the capacity of typical laboratory workstations or cloud-based virtual machines, facilitating investigations into novel materials and chemical processes.
DeepSolver leverages the Python programming language for its flexibility and extensive scientific computing libraries, enabling efficient data manipulation and algorithm implementation. Integration with OpenAI models, including GPT-5, O3, and GPT-5-mini, provides DeepSolver with advanced reasoning and problem-solving capabilities crucial for autonomous scientific inquiry. This combination facilitates a scalable platform, allowing for the processing of large datasets and the execution of complex simulations required for materials science and chemistry research. The use of these established technologies contributes to the framework’s robustness and adaptability to diverse scientific challenges.
The CASCADE framework demonstrates a 70% overall success rate on the SciSkillBench benchmark, indicating performance exceeding that of baseline models. Specifically, CASCADE achieves 100% Pass@3 accuracy on Level 0 tasks when utilizing models such as GPT-5, O3, and GPT-5-mini. Importantly, CASCADE exhibits a reduced rate of performance decline as the difficulty of SciSkillBench tasks increases, outperforming models including S&D, Native, and Claude Code in maintaining accuracy at higher levels of complexity.
The Inevitable Expansion: Beyond Assistance, Towards True Discovery
The CASCADE framework reimagines scientific progress through a collaborative partnership between researchers and artificial intelligence. Rather than replacing human expertise, CASCADE assumes responsibility for the repetitive and time-consuming aspects of experimentation and data analysis – tasks such as literature reviews, initial hypothesis testing, and data cleaning. This automation allows scientists to concentrate on the uniquely human elements of discovery: formulating novel research questions, designing ingenious experiments, interpreting complex results with nuanced understanding, and applying creative problem-solving skills. By offloading routine work, CASCADE effectively amplifies a scientist’s cognitive capacity, fostering an environment where innovation, rather than logistical hurdles, dictates the pace of scientific advancement and ultimately accelerating breakthroughs across disciplines.
The CASCADE framework distinguishes itself through a dynamic learning architecture, enabling continuous skill refinement in response to the ever-shifting landscape of scientific inquiry. Unlike traditional systems with fixed capabilities, CASCADE employs algorithms that analyze the outcomes of experiments – both successful and unsuccessful – to iteratively improve its experimental design, data interpretation, and even hypothesis generation. This self-improving capacity isn’t merely about increased efficiency; it fosters a crucial adaptability, allowing the framework to tackle novel challenges and unexpected results without requiring extensive reprogramming. By consistently honing its abilities through experience, CASCADE demonstrates resilience against the inherent uncertainties of scientific exploration and promises to maintain its effectiveness as research priorities and methodologies evolve, ultimately facilitating more robust and sustained discovery.
CASCADE’s potential to dramatically accelerate scientific discovery stems from its comprehensive automation of the experimental process. The framework isn’t merely about faster computation; it’s a holistic system capable of independently designing experiments, synthesizing materials, collecting and analyzing data, and iteratively refining hypotheses. This closed-loop automation promises to unlock breakthroughs in fields like materials science, where the search space for novel compounds is vast, and in chemistry, where complex reaction pathways often require extensive investigation. By removing bottlenecks associated with manual labor and human error, CASCADE enables researchers to explore a far greater number of possibilities, potentially leading to the identification of materials with unprecedented properties or the discovery of novel chemical reactions with significant applications. The system’s capacity to autonomously navigate this complex landscape promises not only quicker results but also the uncovering of solutions previously obscured by the limitations of traditional research methods.
The pursuit of autonomous skill acquisition, as detailed in CASCADE, echoes a fundamental truth about complex systems. The framework doesn’t build scientific capability; it cultivates an environment where capability emerges through iterative development and evolution. This mirrors the observation that “The most important thing in a complex system is the way it responds to surprise.” CASCADE, by design, embraces unexpected outcomes from its autonomous experimentation, allowing the agent to adapt and refine its skillset-a process far removed from pre-programmed instruction. The system doesn’t promise mastery, but resilience, and a capacity to learn from the inevitable failures inherent in pushing the boundaries of scientific discovery. It’s a prophecy of adaptation, not perfection.
What Lies Ahead?
The pursuit of autonomous scientific skill, as exemplified by CASCADE, is not a quest for perfect automation. It is, rather, an accelerated process of revealing the inherent fragility of any proposed system. The framework will inevitably encounter problems not foreseen in its initial design – and that is precisely as it should be. A system that never breaks is, functionally, dead; it has reached the limit of its capacity to learn. The real metric of success will not be the number of experiments completed, but the elegance with which the system fails, and the speed with which it adapts in response.
The current emphasis on benchmarks, while providing a necessary foothold, risks defining ‘skill’ too narrowly. True scientific progress demands a capacity for creative deviation, for pursuing lines of inquiry that appear, initially, unproductive. Future work must grapple with the challenge of embedding such ‘irrationality’ into an agentic framework, and accepting that the most valuable discoveries may arise from systematic error.
Ultimately, the goal is not to build a scientist, but to cultivate an ecosystem where scientific intuition can emerge. Perfection, in this context, leaves no room for people – or for the serendipity that often drives innovation. The next iteration will not be about more tools, but about designing for graceful degradation, and for the inevitable, and instructive, failures to come.
Original article: https://arxiv.org/pdf/2512.23880.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2026-01-01 20:35