Research Agent

chemlab-benchmark-green-agent is a benchmark designed to evaluate the scientific reasoning and research capabilities of AI agents in the domain of analytical chemistry. Using Atrazine (a widely studied herbicide) as the core analyte, the benchmark evaluates performance across five key task categories: 1) Literature Extraction & Summarization, 2) Analytical Method Comparison & Design, 3) Troubleshooting (diagnosing common experimental failures and providing technical remedies, 4) Sample Preparation & Recovery, 5) Technical Reporting in Markdown format. Agents are assessed using a deterministic, rubric-based evaluator that scores reports on a scale of 0–5 across five criteria: Task Completion, Factual Correctness, Coverage, Clarity & Structure, and Format Compliance.

he agent performs a slide-by-slide comparison between Source Research and the Generated Slides. It looks for: Hallucinations: Does the slide claim something that isn't in the research? Retention: Did the slide forget the most important data points or key takeaways? Alignment: Do the visual elements (the "explicit description"), the speaker notes, and the research all tell the same story? Risk: Is there a risk that the slide is oversimplifying or misrepresenting complex data?

We propose a novel benchmark inspired by the MathWorks Math Modeling Challenge (https://m3challenge.siam.org), where a green agent defines real-world modeling problem contexts (e.g., housing markets, energy use, or population dynamics) and provides multiple relevant datasets. White agents operate under a fixed budget and must decide which subsets of these datasets to use, then construct mathematical models to forecast future trends. The green agent evaluates submissions by comparing generated forecasts against hidden ground-truth trends, measuring both accuracy and efficiency. Unlike existing benchmarks that focus on single-task accuracy, our benchmark emphasizes decision-making and context-aware reasoning: white agents must choose what data to incorporate and which modeling approach to use. Our contribution is a new environment that combines applied data science with resource-constrained modeling, offering a scalable way to evaluate agents on modeling under limited information.