CLaRE Predicts AI Model Side Effects 2.74x Faster Before Weights Change
CLaRE predicts ripple effects in LLM edits using cosine similarity of single-layer hidden states, no gradients needed. Achieves 62.2% higher Spearman correlation with actual ripple effects than gradient baselines. Runs 2.74x faster and uses 2.85x less peak GPU memory than GradSim.
Takeaways
- CLaRE predicts ripple effects in LLM edits using cosine similarity of single-layer hidden states, no gradients needed.
- Achieves 62.2% higher Spearman correlation with actual ripple effects than gradient baselines.
- Runs 2.74x faster and uses 2.85x less peak GPU memory than GradSim.
- Built on a corpus of 11,427 facts covering GPT2-XL, GPT-J, and Llama3-Instruct.
- Enables preservation sets, audit trails, red-teaming, and focused post-edit evaluation.
- Code, corpus, and entanglement graphs released on GitHub ; paper at arXiv .
Editing a fact in a large language model can alter predictions far beyond the intended update. A paper accepted to Findings of ACL 2026, CLaRE-ty Amid Chaos , introduces a method for estimating which facts are vulnerable before an edit runs. CLaRE uses hidden states from forward passes, giving developers a faster way to target regression tests and preservation safeguards.
One patch, many side effects
Methods such as ROME, MEMIT, and AlphaEdit modify model weights to update factual associations without full retraining. Those associations overlap in the model’s hidden representations, so changing a country’s capital or a politician’s party can perturb predictions about unrelated subjects. Researchers call these collateral changes ripple effects.
Detecting ripples usually requires broad post-edit evaluation. Gradient-based predictors can narrow that search by identifying facts with similar optimization directions, but each comparison requires costly backward-pass information. The paper’s comparison uses GradSim as the baseline for this approach.
Hidden states replace gradients
CLaRE stands for Critical Layer Representation Entanglement. For each fact, the method runs one forward pass and extracts a hidden-state vector from an intermediate layer selected empirically for that model. It then calculates cosine similarity between fact vectors.
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