arXiv:2606.13602v1 Announce Type: new Abstract: We introduce EpiBench, a verifiable benchmark for short-horizon epigenomics analysis. EpiBench evaluates whether agents can make well-defined analysis decisions from realistic workflow states and return deterministically gradable answers. The benchmark includes 106 evaluations across CUT\&Tag/CUT\&RUN, ATAC-seq, ChIP-seq, and DNA methylation workflows. Across 5,088 valid trajectories from 16 model-harness pairs, no system passed a majority of attempts: GPT-5.5 / Pi led at 45.0\% (143/318 attempts; 95\% confidence interval (CI), 36.3--53.7), follo
The release of EpiBench highlights the current limitations of leading AI models in complex scientific domain-specific tasks, appearing as AI agent development accelerates.
This benchmark reveals that despite advances in general AI capabilities, practical application in highly specialized fields like epigenomics still requires significant improvement, impacting the timeline for fully autonomous scientific discovery.
The development of verifiable benchmarks offers a standardized method to assess AI agent performance, allowing for targeted improvements rather than relying on broad, qualitative evaluations.
- · AI model developers focused on scientific reasoning
- · Biotech companies leveraging AI for drug discovery
- · Domain-specific AI startups
- · Generalist AI models without specialized training
- · Companies relying on unverified AI analysis
- · Early adopters expecting immediate full automation
AI agents are shown to struggle with complex, verifiable scientific decisions, indicating a gap between current capabilities and full autonomy.
This performance gap will drive increased investment in specialized AI architectures and training methodologies tailored for scientific research.
The pursuit of 'verifiable evaluation' will lead to more robust, auditable AI systems, accelerating trust and adoption in critical scientific and industrial applications.
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Read at arXiv cs.AI