ReDIL-GNN框架解决电路图神经网络在逻辑重综合下的领域增量学习问题
ReDIL-GNN: Resynthesis Domain Incremental Learning for Circuit Graph Neural Networks
这个研究很实用，解决了电路图神经网络在逻辑重综合场景下的领域增量学习问题，提出的RAI评分系统可以指导模型更新，避免盲目更新。
ReDIL-GNN框架通过引入Resynthesis Adaptability Index（RAI）评分，在电路图神经网络（GNN）面临逻辑重综合导致的领域偏移时，能够评估新综合风格是否值得更新。该框架比较了多种方法，包括LwF、Online EWC等，并发现RAI能有效区分需要更新的领域和不需要更新的领域，从而指导模型更新策略。
ReDIL-GNN: Resynthesis Domain Incremental Learning for Circuit Graph Neural Networks
Logic resynthesis preserves circuit functionality while changing gate vocabulary, topology, and structural statistics, creating domain shift for circuit graph neural networks (GNNs) without changing task labels. To study this setting, we introduce ReDIL-GNN, a resynthesis domain-incremental learning framework that adapts a fixed prediction or representation head as new synthesis styles arrive and evaluates retention on all previously observed domains. Because not every shift should be adapted blindly, ReDIL-GNN further introduces the Resynthesis Adaptability Index (RAI), a pre-adaptation score that combines adaptation need, source-equivalence recoverability, structural coverage, and update compatibility. We evaluate supervised hardware-security tasks and representation-learning models using task-native metrics for classifiers and source-equivalence retrieval metrics for embedding models, comparing naive fine-tuning with LwF, Online EWC, MAS, ER, A-GEM, DER++, ER+LwF, and equivalence-guided replay. Across the studied pipelines, RAI separates unsupported shifts from promising updates, ranging from 0.001 for a structurally uncovered GNN-RE ABC-rewrite shift to 0.824 for the best original-only GNN-RE adaptation case. In practice, ReDIL-GNN turns resynthesis-aware circuit learning into a deployment control loop: RAI screens each new synthesis flow before update, guiding whether to reuse the current model, apply retention-aware adaptation, or defer adaptation until the shift is better supported.