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.

Large Vision-Language Models (LVLMs) commonly suffer from hallucinations, which appear as fabricating non-existent objects or outputting incorrect attributes and spatial relationships in images. Previous works empirically indicate that CLIP-style vision encoders tend to focus on primary objects while ignoring image details, which is one of the causes of hallucinations. To address this issue, we propose plug-and-play fine-grained visual experts (PLANE) comprising two components: LoRA visual experts operating on CLIP for multi-aspect image encoding, and an instruction-aware gate network for adaptive image feature fusion. During training, we construct a fine-grained image-caption pairs dataset, where images are described from three aspects: objects, attributes, and relations between objects. Image-caption pairs from different aspects are used to train the three fine-grained LoRA experts respectively. Aiming at comprehensively evaluate our method’s capabilities, we manually annotate a fine-grained hallucination evaluation benchmark for LVLMs which is GPT4-independent during evaluation phase (GIFT). We conduct experiments both on popular hallucination evaluation benchmarks and GIFT. The results show that our method effectively reduces hallucinations across models of different types and sizes while introducing minimal inference latency. Compared to advanced baselines, our model also demonstrates superior performance.

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.