arXiv:2605.06177v1 Announce Type: new
Abstract: Building a deep research agent today is an exercise in glue code: the same backbone evaluated on the same benchmark can report different accuracies in different papers because harness and tool registry all differ, and integrating a new foundation model into a comparable evaluation surface costs weeks of model-specific engineering. We call this the per-paper engineering tax and release BioMedArena, an open-source toolkit that not only alleviates it but also provides an arena for fair comparison of different foundation models when evaluating them as deep-research agents. BioMedArena decouples six layers of biomedical agent evaluation — benchmark loading, tool exposure, tool selection, execution mode, context management, and scoring — and exposes 147 biomedical benchmarks and 75 biomedical tools across 9 functional families. Adding a new model, benchmark, or tool reduces to registering a few-line provider adapter. We further provide 6 agent harnesses with 6 context-management strategies, which provide 12 backbones with competitive research capabilities and significantly improved performance, achieving state-of-the-art (SOTA) results on 8 representative biomedical benchmarks, with an average lift of +15.03 percentage points over prior SOTA. The toolkit, configurations, and per-task traces are available at https://github.com/AI-in-Health/BioMedArena
Rationale and methods of the MOVI-HIIT! cluster-randomized controlled trial: an avatar-guided virtual platform for classroom activity breaks and its impact on cognition, adiposity, and fitness in preschoolers
IntroductionClassroom-based active breaks (ABs) have been shown to reduce sedentary time and increase physical activity in primary school children; however, evidence regarding their effects on