While large language models show promise in medical applications, achieving expert-level clinical reasoning efficiently remains challenging due to the need for massive amounts of manually labeled data and large-scale models. To address this challenge, we propose Clinical-Oriented Reinforcement Learning (CORL), the first fully open-source, end-to-end reinforcement learning training pipeline in the clinical reasoning domain, incorporating a Reasoning-Oriented Data Strategy (RODS) based on topological synthesis, CoT cold-start, and two-stage reinforcement learning. Through CORL, we trained the Fleming-R1 series of models. Among them, Fleming-R1-7B significantly outperforms models of comparable size while approaching or even surpassing certain 32B and 72B models. Fleming-R1-32B achieves near-parity with GPT-4o and outperforms the strongest open-source alternatives up to 671B in MedXpertQA. This demonstrates that in clinical reasoning field, a meticulously designed training pipeline holds greater importance than scaling model size alone. Data and Models are available at https://github.com/UbiquantAI/Fleming-R1 and https://huggingface.co/collections/IQuestLab/fleming.

Failure attribution, i.e., identifying the responsible agent and decisive step of a failure, is particularly challenging in LLM-based multi-agent systems (MAS) due to their natural-language reasoning, nondeterministic outputs, and intricate interaction dynamics. A reliable benchmark is therefore essential to guide and evaluate attribution techniques. Yet existing benchmarks rely on partially observable traces that capture only agent outputs, omitting the inputs and context that developers actually use when debugging. We argue that attribution should be studied under full execution observability, aligning with real-world developer-facing scenarios where complete traces, rather than only outputs, are accessible for diagnosis. To this end, we introduce TraceElephant, a benchmark designed for failure attribution with full execution traces and reproducible environments. We then systematically evaluate failure attribution techniques across various configurations. Specifically, full traces improve attribution accuracy by up to 76.5% over a partial-observation counterpart, confirming that missing inputs obscure many failure causes. TraceElephant provides a foundation for follow-up failure attribution research, promoting evaluation practices that reflect real-world debugging and supporting the development of more transparent MASs.