Self-evolution methods enhance code generation through iterative ”generate-verify-refine” cycles, yet existing approaches suffer from low exploration efficiency, failing to discover algorithmically superior solutions within limited budgets. We identify the root cause as the lack of systematic controllability: execution feedback is not reliably converted into targeted search decisions across route planning, edit granularity, and experience reuse. We propose Controlled Self-Evolution (CSE), a framework that couples three components into an integrated control architecture. Diversified Planning Initialization provides route diversity through structurally distinct algorithmic strategies. Modular Evolution provides feedback attribution and module-level editing by decomposing programs into functional modules and applying targeted mutation and compositional crossover. Hierarchical Evolution Memory maintains search state within tasks and distills reusable optimization patterns across tasks. Experiments on EffiBench-X demonstrate that CSE consistently outperforms all baselines across various LLM backbones. Further analysis reveals non-additive synergy among control layers, confirming that CSE’s effectiveness arises from component coupling rather than independent contributions. The code and evaluation implementation are publicly available at https://github.com/QuantaAlpha/EvoControl.

Hallucination detection in clinical large vision-language models requires cross-verifying heterogeneous evidence, including patient text and images for faithfulness, and medical knowledge for factuality. However, existing pipelines either verify these dimensions in isolation or conflate them into a uniform process, potentially causing evidence misalignment that limits detection precision. Consequently, we posit that the essence of black-box hallucination detection is an evidence routing problem and propose MedF²Route, an Evidence-Routed Graph Verification Framework for both Faithfulness and Factuality hallucination detection. Specifically, it first transforms free-text clinical queries and model responses into structured graphs, partitioning the response graph into independent subgraphs to separate patient-specific grounding from knowledge-based diagnostic inference. An evidence-aware routing and verification mechanism then dynamically dispatches response triplets in these subgraphs to their dedicated heterogeneous evidence sources for targeted verification. Experiments on our MedF²Bench diagnostic benchmark show that our framework outperforms state-of-the-art baselines, notably improving detection precision. Overall, MedF²Route enables professional and precise detection of clinical hallucinations, underpinning the trustworthiness of clinical LVLMs. The code and data are available at https://github.com/Agentic-CliniAI/MedF2Route.