Accelerating Scientific Discovery with Autonomous Goal-evolving Agents

There has been unprecedented interest in developing agents that expand the boundary of scientific discovery, primarily by optimizing quantitative objective functions specified by scientists. However, for grand challenges in science, these objectives may only be imperfect proxies. We argue that automating objective function design is a central, yet unmet need for scientific discovery agents. In this work, we introduce the Scientific Autonomous Goal-evolving Agent (SAGA) to address this challenge. SAGA employs a bi-level architecture in which an outer loop of LLM agents analyzes optimization outcomes, proposes new objectives, and converts them into computable scoring functions, while an inner loop performs solution optimization under the current objectives. This bi-level design enables systematic exploration of the space of objectives and their trade-offs, rather than treating them as fixed inputs. We demonstrate the framework through a wide range of design applications, including antibiotics, nanobodies, functional DNA sequences, inorganic materials, and chemical processes. Notably, our experimental validation identifies a structurally novel hit with promising potency and safety profiles for E.

Introduction. Scientific discovery has been driven by human ingenuity through iterations of hypothesis, experimentation, and observation, but is increasingly bottlenecked by the vast space of hypotheses to explore and the high cost of experimental validation [1]. Recent advances in artificial intelligence (AI) agents based on large language models (LLMs) offer promising approaches to address these bottlenecks and accelerate scientific discovery [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]. Leveraging massive pretrained knowledge and general capabilities for information collection and reasoning, these AI agents can efficiently navigate large hypothesis spaces and reduce experimental costs by automating key aspects of the research process. For example, pipeline automation agents [2, 3] streamline specialized data analysis workflows, reducing the manual effort required Our work embarks on a different and more ambitious goal in scientific discovery: building agents to discover new hypotheses to complex scientific design challenges, such as better therapeutic molecules and new functional materials.

Discussion / Conclusion. Scientific discovery is often limited not only by the vastness of the hypothesis space, but also by the “creativity” of defining objectives that ultimately leads to new discovery. Fixed surrogate objectives can be incomplete, problem-specific, or vulnerable to misalignment and reward hacking, and are rarely sufficient to navigate open-ended discovery problems. SAGA, as a generalist agentic framework, address this challenge by iteratively evolving objectives and their realizations based on observed failure modes. By introducing an outer loop that proposes new objectives, implements executable scoring functions, analyzes outcomes, and selects final candidates, SAGA makes objective formulation a dynamic and autonomous discovery process.