Can Circuit Alignment Predict OOD Generalization?

arXiv:2609.31996 · cs.LG · Submitted 2026-09-25 · Read on arXiv

cs.LG

Submitted: 2026-09-25

Updated: 2026-09-25

Code: https://github.com/ayanban011/ACE

Terminology

• Accuracy — 127× in this paper · explained in 1 episode
• Generalization — 32× in this paper · explained in 13 episodes
• Robustness — 22× in this paper · explained in 5 episodes
• Alignment — 19× in this paper · explained in 1 episode
• Benchmark — 17× in this paper · explained in 2 episodes
• Consistency — 16× in this paper · explained in 3 episodes
• Domain Shift — 16× in this paper · explained in 4 episodes
• Transformer — 15× in this paper · explained in 2 episodes
• Entanglement — 14× in this paper · explained in 4 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Treelet Kernel — 10× in this paper
• Optimal Transport — 9× in this paper · explained in 5 episodes
• Distribution Shift — 8× in this paper · explained in 6 episodes
• Interpretability — 8× in this paper · explained in 15 episodes
• Aggregation — 7× in this paper · explained in 1 episode
• Circuit Alignment Score — 7× in this paper
• Adam — 6× in this paper · explained in 2 episodes
• Failure Modes — 5× in this paper · explained in 3 episodes
• Mechanistic interpretability — 5× in this paper · explained in 2 episodes
• Regularization — 5× in this paper · explained in 3 episodes
• Activation Patching — 4× in this paper · explained in 2 episodes
• Adaptation — 4× in this paper · explained in 1 episode
• Domain Adaptation — 4× in this paper · explained in 5 episodes
• Abstraction — 3× in this paper · explained in 1 episode
• Core — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Clustering — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Optimal Transport (OT) — 2× in this paper · explained in 4 episodes
• Wasserstein Distance — 2× in this paper · explained in 4 episodes
• ADAPTER CIRCUIT EXTRACTION — 1× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Additivity — 1× in this paper · explained in 1 episode
• Adversarial Training — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Graph Construction — 1× in this paper · explained in 3 episodes
• Influence Functions — 1× in this paper · explained in 2 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• LoRA Adapters — 1× in this paper · explained in 3 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Sample Complexity — 1× in this paper · explained in 15 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Verification — 1× in this paper · explained in 2 episodes

Sources

• Invariant Risk Minimization
• Adam: A Method for Stochastic Optimization
• How good is PAC-Bayes at explaining generalisation?
• Open Problems in Mechanistic Interpretability
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Out-of-Distribution Generalization Analysis via Influence Function

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: Local-Consistency Optimisation for Bayesian Neural Networks
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks