Harness plays a critical role in large language model agent performance, and building a high-performing harness requires substantial expert effort. Therefore, recent research has increasingly explored harness self-evolution, which iteratively proposes, evaluates, and improves harnesses using historical trial experience. However, accumulated historical experience does not always translate into stable search guidance, and performance often fluctuates substantially across evolution iterations, making it difficult to reliably discover high-performing harnesses under a limited evolution budget. We identify two limitations in how existing harness self-evolution methods leverage historical experience: (1) Lack of dynamic reassessment of whether historical experience remains valid for the current harness, and (2) Lack of explicit mechanisms for translating valid historical experience into actionable search directions. To address these limitations, we propose a new harness self-evolution method, named DREvo, which integrates function-level evidence anchoring, state-dependent evidence recalibration, and role-conditioned search intent distillation to determine which historical evidence remains valid and where the harness should evolve next. Under limited evolution budgets, DREvo exhibits smoother evolution trajectories, achieves the highest accuracy on all five benchmarks, and delivers average gains of 16.2% and 14.2% over the evaluated baselines on domain reasoning and agentic tasks, respectively.
Graph-based indexes have become the dominant approach to approximate nearest neighbor search (ANNS) over high-dimensional data and play a crucial role in real-world applications such as retrieval-augmented generation, recommendation systems, and vector databases. Despite extensive progress in static graph construction and search, efficient in-place deletion remains challenging because obsolete vectors must be removed without allowing stale incoming edges to consume search capacity or expensive graph-wide maintenance to interrupt online services, e.g., retrieval-augmented generation (RAG) and recommendation platforms. To address this problem, we propose MERIT (MST-based Efficient Repair with In-place updaTes), an in-place update framework with three core techniques: (1) bounded search-based recovery that combines a deleted vertex's outgoing neighbors with its readily searchable in-neighbors, (2) $k_r$-Minimum Spanning Tree (MST) local repair that promotes local connectivity while retaining multiple routing choices for graph search, and (3) versioned-edge invalidation that immediately filters all stale incoming edges to the deleted vertex and progressively removes them as adjacency lists are rewritten. Its integration with the hierarchical HNSW index and the single-layer Vamana index demonstrates applicability across distinct graph structures. Extensive experiments on multiple real-world datasets show that MERIT processes deletion at nearly the cost of inserting one vector, achieves up to $3.02\times$--$18.87\times$ faster deletion than state-of-the-art (SOTA) methods, and keeps search recall stable or even improves it as deletions accumulate.
