Author: Denis Avetisyan
A new framework empowers artificial intelligence to not only conduct experiments, but to autonomously discover and refine the goals of scientific inquiry itself.
This review details the SAGA framework, an LLM-based approach to automated experimentation and multi-objective optimization designed to mitigate reward hacking and accelerate scientific discovery.
While automated agents show promise in scientific discovery, their reliance on pre-defined objectives often limits exploration and invites unintended consequences. This work, ‘Accelerating Scientific Discovery with Autonomous Goal-evolving Agents’, introduces the Scientific Autonomous Goal-evolving Agent (SAGA), a bi-level framework that systematically discovers and optimizes objectives alongside solution optimization. By automating objective formulation, SAGA demonstrates substantial improvements across diverse applications-from antibiotic design to materials discovery-overcoming limitations of fixed reward structures. Could this approach unlock a new era of truly autonomous scientific innovation, where agents not only solve problems, but also define them?
Beyond Manual Limits: The Promise of Automated Discovery
Scientific progress, while historically reliant on meticulous observation and hypothesis testing, frequently encounters bottlenecks due to the sheer scale and complexity of modern research questions. The conventional approach demands significant time, funding, and specialized expertise, creating a resource-intensive process that can limit the scope of inquiry. Furthermore, the inherent subjectivity of human researchers introduces potential biases-stemming from pre-conceived notions, selective data interpretation, or even unconscious preferences-that can inadvertently skew results and hinder objective discovery. This reliance on human intuition, while valuable, can also restrict exploration to familiar avenues, potentially overlooking novel solutions within the vast landscape of possibilities. Consequently, the pace of scientific advancement is often constrained not by a lack of data, but by the limitations of traditional, manually-driven methodologies.
The escalating complexity of modern scientific challenges demands a shift towards automated discovery frameworks. Traditional research methods, while foundational, often struggle to efficiently navigate the exponentially growing volume of data and potential hypotheses. These automated systems aren’t intended to replace scientists, but rather to augment their capabilities by rapidly screening possibilities, identifying promising avenues of investigation, and even designing experiments. By leveraging machine learning and artificial intelligence, these frameworks can explore vast solution spaces – parameter combinations, material compositions, or genetic sequences, for example – far beyond the scope of manual exploration. This acceleration of the discovery process promises breakthroughs in fields ranging from drug development and materials science to climate modeling and fundamental physics, ultimately enabling researchers to address previously intractable problems with unprecedented speed and scale.
SAGA: An Iterative Framework for Scientific Solution Generation
The SAGA framework operates through repeated cycles of goal definition, solution generation, and analytical evaluation. Initially, a high-level scientific objective is specified, which may be relatively broad in scope. This objective then undergoes iterative refinement; the results of each analytical phase – typically involving computational modeling and data assessment – inform adjustments to the initial goal or constraints. These adjustments create a feedback loop, progressively narrowing the search space and improving the precision with which SAGA addresses the underlying scientific problem. This process continues until pre-defined convergence criteria are met, or a satisfactory solution is identified.
SAGA’s solution generation is driven by an LLM-based Evolutionary Algorithm (EA). This EA operates by iteratively refining a population of candidate solutions through processes analogous to natural selection. The Large Language Model (LLM) is utilized to evaluate the fitness of each candidate and to generate novel variations via mutation and recombination. These operations are guided by the specific scientific goal and constraints, allowing the algorithm to explore a diverse solution space. The LLM’s ability to understand and manipulate complex data representations facilitates the creation of candidates exhibiting a wide range of properties, increasing the likelihood of identifying optimal or near-optimal solutions.
The SAGA framework’s LLM-based Evolutionary Algorithm initiates its solution search using data from the Materials Project database. This database provides a substantial and pre-characterized population of materials, including their compositions, crystal structures, and calculated properties. Utilizing this existing data as a starting point significantly accelerates the evolutionary process by providing a diverse set of viable candidates, rather than relying on random initializations. The database entries serve as the ‘parents’ for the algorithm, which then applies LLM-driven mutation and crossover operations to generate subsequent generations of candidate solutions for evaluation.
Rigorous Evaluation: Quantifying Scientific Merit
Candidate evaluation within the SAGA framework is a multi-stage process designed to quantify how well proposed solutions meet pre-defined objectives. This assessment utilizes a scoring function that incorporates key performance indicators (KPIs) established during the initial goal definition phase. Each candidate solution is subjected to rigorous testing and analysis against these KPIs, resulting in a numerical score representing its overall performance. Solutions failing to meet minimum threshold values for critical objectives are automatically excluded from further consideration. The evaluation process is iterative, with results informing refinement of both candidate solutions and the objective functions themselves, ensuring a tight feedback loop between design and performance.
MPRA Prediction, utilized within the SAGA framework, quantitatively assesses the functional impact of genetic variants on enhancer activity. This method leverages massively parallel reporter assays (MPRAs) to measure the effect of numerous sequence variations on transcriptional output. Briefly, a library of DNA sequences, each containing a distinct variant, is inserted upstream of a reporter gene in cells. The resulting expression levels of the reporter gene are then quantified, providing a direct measure of enhancer activity for each variant. Statistical analysis of these data enables the prediction of how specific sequence changes affect gene regulation, informing the evaluation of candidate solutions within SAGA by correlating sequence features with functional performance.
Paret Front Analysis, utilized within SAGA, identifies a set of solutions that represent the optimal trade-offs between multiple, potentially conflicting objectives. This process involves evaluating the solution space and defining the non-dominated front – the set of solutions where improving one objective necessitates worsening at least one other. Solutions on the Paret front are not strictly “better” than others; instead, they represent the best possible performance given the inherent trade-offs. The analysis provides decision-makers with a range of optimal options, allowing selection based on prioritized objectives and acceptable compromises, rather than a single, potentially suboptimal, solution.
SAGA integrates the DiffCSP diffusion model to generate novel 3D crystal structures, effectively broadening the range of potential solutions considered during the design process. This probabilistic model is trained on known crystal structures and utilizes a diffusion process to create new, valid structures that adhere to fundamental crystallographic principles. By generating diverse structural hypotheses, DiffCSP circumvents limitations imposed by traditional design methods and enables exploration of a significantly expanded chemical space. The generated structures are then evaluated based on defined objectives, contributing to the identification of optimal candidates within the SAGA framework.
Beyond Automation: Impact and Future Directions
SAGA streamlines the evaluation process through automated analysis report generation, transforming raw experimental data into concise summaries of key findings and actionable recommendations. This feature doesn’t simply present results; it actively interprets them, identifying potential issues or areas for improvement within the designed sequences. By automatically synthesizing information from complex evaluations, SAGA reduces the time and expertise needed to understand performance, allowing researchers to quickly iterate on designs and focus on higher-level scientific questions. The system’s ability to pinpoint critical insights ensures that generated solutions are not only innovative but also demonstrably aligned with the intended scientific objectives, accelerating the pace of discovery.
The SAGA framework intelligently combines the strengths of two advanced artificial intelligence models to drive its automated scientific discovery process. GPT-5 serves as the central planner and analytical engine, dissecting complex research goals and formulating strategies to achieve them. This high-level planning is then translated into executable code by the Claude Code Agent, a specialized AI adept at generating functional, error-free programs. This synergistic approach allows SAGA to not only conceptualize experimental designs but also to automatically implement and test them, significantly accelerating the pace of scientific inquiry and allowing for exploration of a wider design space than traditional methods.
A core tenet of the SAGA framework lies in its proactive defense against reward hacking, a common vulnerability in AI systems where agents prioritize maximizing a reward signal over achieving the intended objective. Rather than simply optimizing for a numerical score, SAGA employs strategies to identify and neutralize potential loopholes within the reward function that could lead to unintended, yet technically “successful”, outcomes. This is achieved through a combination of diverse reward signals, adversarial testing, and constraints designed to align agent behavior with genuine scientific principles. By anticipating and mitigating these exploits, SAGA ensures that generated solutions – whether antibiotic designs or DNA sequences – are not merely optimized for the reward system, but truly reflect functional and scientifically valid improvements, leading to more reliable and impactful discoveries.
The SAGA framework represents a significant advancement in automated scientific discovery, demonstrably outperforming existing methods in both antibiotic development and functional DNA sequence design. Evaluations reveal a compelling success rate, with SAGA achieving up to a 92% pass rate in antibiotic candidate generation – a considerable improvement over baseline approaches. This capability extends to genetic engineering, where SAGA-designed enhancers exhibited a 16.3% performance boost in SKNSH cells and a 1.5% improvement in K562 cells, as rigorously validated through MPRA-based specificity assays. Notably, SAGA’s performance in functional DNA sequence design consistently matches or exceeds predictions based on MPRA scores across a variety of cell lines, suggesting a capacity to not only generate sequences, but to accurately anticipate their biological function.
The SAGA framework’s design prioritizes solution fidelity, ensuring generated outputs genuinely address the stated scientific goals. This isn’t simply about achieving high performance metrics; it’s about creating designs – whether for antibiotic discovery or DNA sequence optimization – that are functionally meaningful and biologically relevant. By integrating reward hacking mitigation strategies, the system actively prevents agents from prioritizing reward maximization over actual scientific validity. This focus on alignment ensures that improvements observed in evaluations, such as the up to 92% antibiotic pass rate or the 16.3% enhancement in SKNSH designs, reflect genuine progress toward the intended objectives, rather than clever exploitation of the evaluation process itself. Consequently, the resulting discoveries and designs demonstrate a higher degree of trustworthiness and translational potential.
The pursuit of scientific advancement, as demonstrated by the SAGA framework, often necessitates a distillation of complex phenomena into measurable objectives. This process, however, carries inherent risks – notably, reward hacking, where agents optimize for the letter of an objective rather than its intended spirit. As Henri Poincaré observed, “It is better to know little, but to know it well.” SAGA’s iterative refinement of objectives, its ability to detect and mitigate reward hacking, echoes this sentiment. The framework prioritizes a robust understanding of what is being optimized, favoring clarity over the sheer volume of experimentation. This focus aligns with a philosophy that true progress stems not from boundless exploration, but from precise, well-defined inquiry.
Where Do We Go From Here?
The pursuit of automated scientific discovery, as exemplified by frameworks like SAGA, inevitably exposes the inherent messiness of defining ‘progress’. The system demonstrably addresses the immediate problem of reward hacking – a symptom, not the disease. The true difficulty lies not in preventing agents from finding objectives, but in acknowledging that any objective, however elegantly derived, is a simplification. Each optimization represents a narrowing of possibility, a tacit exclusion of alternatives that may, in a fuller context, prove more fruitful.
Future work must confront this fundamental limitation. The field should shift focus from simply discovering objectives to evaluating their robustness – not against adversarial manipulation, but against the inevitable arrival of new information. A truly intelligent system will not cling to a locally optimal objective, but will recognize when its guiding principles require re-evaluation, even if that means abandoning previously ‘successful’ lines of inquiry.
The temptation to build ever more complex agents must be resisted. The elegance of SAGA lies in its iterative approach, but further layers of abstraction risk obscuring the very phenomena under investigation. Perhaps the greatest contribution of this work will not be the objectives it uncovers, but the questions it forces scientists to ask about the nature of inquiry itself.
Original article: https://arxiv.org/pdf/2512.21782.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-12-29 06:49