While autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a “closed-world” limitation: they attempt to fix bugs from scratch or solely using information from the current repository, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragmented nature of real-world issue-tracking data. In this paper, we introduce MemGovern, a framework designed to govern and transform raw GitHub data into actionable experiential memory for agents. MemGovern employs experience governance to convert human experience into agent-friendly experience cards and introduces an agentic experience search strategy that enables logic-driven retrieval of human expertise. By producing 135K governed experience cards, MemGovern achieves a significant performance boost, improving resolution rates on the SWE-bench benchmark by 4.65%. As a plug-in approach, MemGovern provides a solution for agent-friendly memory infrastructure.

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.

Model merging is an effective technique for composing the capabilities of a multilingual model and a reasoning model. It has achieved promising generalization in multilingual reasoning tasks by aligning feature spaces of different models. However, the merged single model often fails to address the conflicts between source models, leading to suboptimal performance. In other words, the one-size-fits-all merging strategy may not align with the characteristics of different inputs which may require prioritizing certain models over others. To this end, we propose a Steerable Model Merging (ST-Merge) framework to modulate the contribution of each source model. To realize this idea, we introduce a gated cross-attention mechanism to weight or filter the two attended source models in an adaptive manner. Extensive experiments demonstrate that ST-Merge consistently outperforms multiple strong baselines on four multilingual reasoning benchmarks across 21 different languages.