← Back to all news
arXiv Machine Learning By Rupesh Raj Karn, Johann Knechtel, Ozgur Sinanoglu

ReDIL-GNN: Resynthesis Domain Incremental Learning for Circuit Graph Neural Networks

Read the original on arXiv Machine Learning →

ReDIL-GNN is a framework for resynthesis domain‑incremental learning in circuit graph neural networks. It adapts a fixed prediction or representation head as new synthesis styles appear and evaluates retention across all previously seen domains. The method introduces the Resynthesis Adaptability Index (RAI), a pre‑adaptation score that combines adaptation need, source‑equivalence recoverability, structural coverage, and update compatibility to decide whether to adapt, reuse, or defer updates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Oct 3

Repurposing Obsolete Representations for Post-Deployment Adaptation

Deep Repurposing (DR) is a post‑hoc framework that adapts deep neural networks when parts of their output space become obsolete after deployment. DR estimates the latent geometry of obsolete and retained regions, removes components that support obsolete outputs, and reallocates retained-compatible evidence through an analytic repair map without gradient updates. The method yields repaired predictions and representations that eliminate obsolete outputs while preserving or improving retained accuracy, and it adapts up to 60× faster than competing unlearning methods.

By Daniel Bethell, Charmaine Barker, Simos Gerasimou
fine-tuningbenchmarks
More like this →
arXiv AI
2d ago

Certified Circuits: Stability Guarantees for Mechanistic Circuits

Certified Circuits introduces a framework that wraps any black‑box circuit discovery algorithm with randomized data subsampling to provide provable stability guarantees. By certifying that inclusion decisions over circuit components are invariant to bounded edit‑distance perturbations of the concept dataset, unstable components are abstained from, producing more compact and accurate mechanistic explanations. Across ResNet, ViT, and GPT‑2 on vision and language tasks, Certified Circuits achieves up to 56% higher accuracy and up to 80% fewer components, maintaining reliability where baseline methods degrade.

By Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz, Jonas Fischer
llmssafety
More like this →