Researchers have unveiled LifeSciBench, a comprehensive new benchmark designed to evaluate the scientific reasoning and operational utility of agentic AI systems in life science research. Moving beyond narrow fact-retrieval or clean prediction tasks, the benchmark addresses the complex, iterative nature of real-world laboratory and clinical work. Developed by a consortium of 173 Ph.D.-trained life scientists and industry experts, LifeSciBench comprises 750 expert-authored tasks distributed across seven core research workflows and seven biological domains. These domains include evidence handling, experimental design, scientific reasoning, translation, and communication. Each task is structured as a realistic research request, featuring free-form scientific prompts, contextual artifacts, and granular rubrics. The evaluation framework incorporates over 19,000 distinct criteria, averaging 25 per task, to assess not only final answers but also the validity of the reasoning, appropriate caveats, and operational usefulness for expert decision-making. Validation of the benchmark involved an independent review panel of 453 scientists, 97 percent of whom held doctoral degrees with an average of 12 years of field experience. The panel confirmed that 96 percent of the tasks accurately reflect applied research challenges and require rigorous scientific judgment. To illustrate the benchmark depth, tasks frequently demand multi-step analysis and artifact interpretation, such as deconstructing regulatory submissions for gene therapies, critically evaluating surrogate endpoints, identifying assay confounders, and proposing statistically sound experimental designs. Initial evaluations using the benchmark demonstrate significant capability shifts in frontier models. Testing between GPT-5.5 and its successor, GPT-Rosalind, reveals marked improvements in scientific synthesis and translational communication. GPT-Rosalind raised pass rates from 56.3 percent to 71.1 percent in communication tasks and from 36.8 percent to 57.7 percent in drug development translation. GPT-Rosalind also outperformed its predecessor in handling uncertainty and generating expert-useful outputs. However, substantial performance gaps persist in artifact-heavy and operationally constrained workflows. Models struggle to extract precise data from complex figures, sequence files, or structural diagrams, with pass rates dropping significantly when artifacts are involved. Exact-output tasks, such as molecular construct design or numerical calculations, remain brittle, underscoring the difficulty of achieving the precision required for direct experimental application. The findings highlight a clear distinction between academic benchmark performance and operational research utility. While AI systems show rapid maturation in structuring evidence and drafting regulatory or scientific communications, they still lack the robustness needed for independent experimental design or precise data synthesis. Developers emphasize that LifeSciBench measures realistic task-level capability rather than downstream discovery acceleration. Future efforts will focus on deploying these models in live research environments to evaluate whether benchmark gains translate into measurable improvements in drug discovery pipelines, assay optimization, and long-term R&D efficiency.