# CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing

Source: [CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing](<https://www.sota2.com/research/paper/69c0c9020636e5bf4afc3b5a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-09T05:00:04.534Z

Authors: Manit Baser, Alperen Yildiz, Dinil Mon Divakaran, Mohan Gurusamy

Published: 2026-03-11T04:11:49.000Z

## Abstract

The static knowledge representations of large language models (LLMs) inevitably become outdated or incorrect over time. While model-editing techniques offer a promising solution by modifying a model's factual associations, they often produce unpredictable ripple effects, which are unintended behavioral changes that propagate even to the hidden space. In this work, we introduce CLaRE, a lightweight representation-level technique to identify where these ripple effects may occur. Unlike prior gradient-based methods, CLaRE quantifies entanglement between facts using forward activations from a single intermediate layer, avoiding costly backward passes. To enable systematic study, we prepare and analyse a corpus of 11,427 facts drawn from three existing datasets. Using CLaRE, we compute large-scale entanglement graphs of this corpus for multiple models, capturing how local edits propagate through representational space. These graphs enable stronger preservation sets for model editing, audit trails, efficient red-teaming, and scalable post-edit evaluation. In comparison to baselines, CLaRE achieves an average of 62.2% improvement in Spearman correlation with ripple effects while being $2.74\\times$ faster, and using $2.85\\times$ less peak GPU memory. Besides, CLaRE requires only a fraction of the storage needed by the baselines to compute and preserve fact representations. Our entanglement graphs and corpus are available at https://github.com/manitbaser/CLaRE.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Ripple effect magnitude prediction (\|Δ log P(y)\|) on Curated ripple-effect corpus 1.0 (test)](<https://www.sota2.com/research/sota/ripple-effect-magnitude-prediction-log-p-y-on-curated-ripple-effect-corpus-1-0-test.md>): Ripple effect magnitude prediction (\|Δ log P(y)\|); Curated ripple-effect corpus 1.0 (test); Spearman Correlation (ρs): 0.854.

- [Ripple effect magnitude prediction (ℓ2 logit shift) on Curated ripple-effect corpus 1.0 (test)](<https://www.sota2.com/research/sota/ripple-effect-magnitude-prediction-2-logit-shift-on-curated-ripple-effect-corpus-1-0-test.md>): Ripple effect magnitude prediction (ℓ2 logit shift); Curated ripple-effect corpus 1.0 (test); Spearman Correlation (ρs): 0.918.

- [Efficiency Evaluation of Factual Representation Extraction on Corpus of 11,427 facts](<https://www.sota2.com/research/sota/efficiency-evaluation-of-factual-representation-extraction-on-corpus-of-11-427-facts.md>): Efficiency evaluation of factual representation extraction; Corpus of 11,427 facts; Total Processing Time (s): 0.305.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2603.19297>)

[PDF](<https://arxiv.org/pdf/2603.19297.pdf>)