AutoSND is proposed, a three stage tree search framework for complete network dismantling and achieves better search performance and stability and discovers more competitive and structurally interpretable network disman- tling programs.
Abstract
Network dismantling is fundamental to analyzing the robustness and vulnerability of complex systems, yet practical heuristics must balance effectiveness and computational efficiency, and are usually designed manually by researchers. Existing large language model based automatic heuristic design methods can generate and screen candidates, yet they have difficulty further transforming candidate quality or failure states during execution into structural-level guid- ance for subsequent generation. We propose AutoSND, a three stage tree search framework for complete network dismantling pro- grams. Stage I broadly explores from simple heuristics and archives execution evidence. Stage II compiles candidate records into struc- tural policies concerning local signals, neighborhood access, and state update ranges. Stage III continues tree search conditioned on these policies and obtains the final quality prioritized and speed prioritized candidates, AutoSND-Q/S. Experiments on 12 real world networks and 3 large real world networks show that AutoSND achieves better search performance and stability and discovers more competitive and structurally interpretable network disman- tling programs. The final candidates form an interpretable structure that uses residual degree as the backbone, adjusts node order with bounded local signals, and restricts the state update range. Code is available at https://github.com/MirrorNew/AutoSND.
Results suggest that causal-aware reasoning and stability-oriented design can improve the effectiveness of LLM-based APR, a causality-guided multi-agent repair framework that improves the repair stage of existing LLM-based localization pipelines.
Lei Yuan, Shaohua Liu, Yu Wang et al.· Empirical Software Engineeri...· 0 citations
LLM-MetaAS is proposed, a semantic-statistical framework for AutoML execution policy selection that improves overall AutoML performance and selects policies closer to the oracle than fixed strategies, random selection, and the native Auto-sklearn 2.0 selector.
Zhihuan Peng, Pincheng Liu, Yong Li et al.· Applied Sciences· 0 citations
StructFix is proposed, a structure-aware APR framework that grounds masked patch generation in Code Property Graphs (CPGs), and explicitly coupling structural dependencies with masked generation improves repair effectiveness and enables transfer across datasets.
Mengtian Cui, Yang-Fan Liu, Zhibo Lu et al.· International Conference on...· 0 citations
Repository-level code repair generates rich tool traces, but most LLM agents discard this data and keep restarting from a fixed debugging loop. We present Evolutionary Self-Debugging Agents (ESDA), which mines tool traces into structured failure signatures and uses them to maintain a strategy bank of reusable debugging policies. Policies are stored as modular prompt genomes with typed slots, enabling slot-level reuse, mutation, and crossover as new tasks arrive. A cost-aware ranking objective prioritizes strategies that are likely to succeed in the first few evaluator calls under tight budgets. On RepoBench, ESDA solves 58.4% of tasks within the first two evaluator calls and reduces median wall-clock time by 3.0x compared to strong baselines. We further analyze transfer across languages and build systems and find that mining failure signatures yields consistent gains under distribution shift.
Shuang Cao, Rui Li· Proceedings of the 32nd ACM...· 0 citations
Experiments show that AP-REASONER outperforms baseline subsamplers on structure-sensitive downstream tasks and enables controllable recovery of alternative protein conformations, highlighting the value of modeling MSA subsampling as a controllable optimization problem, where factor-graph reasoning offers an effective alternative to heuristic selection.
Zhangzhi Xiong, Minzhang Li, Hao Yu et al.· 0 citations
Self-supervised automated program repair (APR) leverages project-specific perturbations to generate training data and uses test execution diagnostics to guide patch generation. In practice, however, diagnostics are heterogeneous (e.g., exception messages, stack traces, assertion diffs, and dynamic execution signals) and must fit within a strict context budget. Naive concatenation either truncates critical evidence or amplifies noisy artifacts, especially for deep bugs where the failure symptom is far from the root cause. We present TraceStructRepair, a diagnostic structuring and budgeting approach for execution-aware self-supervised APR. TraceStructRepair (1) extracts a compact set of execution diagnostics from a single failing test, including exception type and message, stack trace frames, assertion diffs, and optionally dynamically loaded classes; (2) normalizes and ranks diagnostic elements to reduce redundancy and framework noise; and (3) assembles a field-aware representation under a fixed token budget with per-field caps and lexicographic priority rules. We implement TraceStructRepair on top of the SelfAPR pipeline and evaluate it on Defects4J using a project-wise heldout protocol. Beyond end-to-end repair outcomes, we analyze robustness under noisy fault localization and component ablations. We release artifacts to facilitate replication and future work on execution-aware, budget-constrained APR.
Pan Lu, Dongcheng Li, W. E. Wong· Annual International Compute...· 0 citations