AblationBench: Evaluating Automated Planning of Ablations in Empirical AI Research

Researchers introduce AblationBench, a benchmark suite for evaluating language model agents on ablation planning tasks in AI research. The study finds that frontier LMs achieve only 45% accuracy on average, significantly below human performance, highlighting challenges in automating scientific research methodologies.

AblationBench addresses a critical gap in evaluating AI agents' scientific contributions by testing their ability to design ablation experiments—a fundamental methodology in empirical research. The benchmark includes two complementary tasks: AuthorAblation (83 instances) for proposing experiments during paper writing and ReviewerAblation (350 instances) for identifying missing ablations during review. This work matters because automating scientific research requires agents capable of rigorous experimental design, not merely code generation or literature synthesis.

The research reflects growing interest in using language models as research assistants. As AI systems increasingly participate in scientific workflows, establishing evaluation frameworks prevents low-quality automation from degrading research standards. The inverse performance trend between author and reviewer tasks suggests that model grounding differs significantly based on task context—authors work from method sections while reviewers analyze complete papers, revealing how information availability shapes agent performance.

The 45% accuracy ceiling demonstrates that current frontier models lack the reasoning depth needed for sophisticated experimental planning. Chain-of-thought prompting outperforming agent-based approaches indicates that simpler prompting strategies better handle scientific reasoning than complex agentic frameworks. This has implications for researchers developing AI lab assistants: current capabilities suit supporting human scientists rather than replacing research judgment.

Future development requires models that better understand causal relationships between experimental variables and can identify confounds in methodology. The publicly released benchmark provides researchers a standardized evaluation framework, potentially accelerating progress in scientific AI agents. Success on these tasks represents a meaningful milestone toward trustworthy automated research contribution.

• →AblationBench tests LM agents on ablation planning with 433 total instances across author and reviewer perspectives
• →Current frontier models achieve only 45% accuracy, falling significantly below human performance on ablation identification
• →Chain-of-thought prompting outperforms agent-based approaches for scientific reasoning tasks in this domain
• →Inverse performance trends between tasks reveal that model grounding depends critically on available information context
• →Open-source benchmark and code enable community progress on evaluating scientific contributions from AI agents