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

Conference: ACL 2026
arXiv: 2603.19297
Code: https://github.com/manitbaser/CLaRE
Area: Model Editing / Knowledge Editing
Keywords: Model Editing, Ripple Effects, Representational Entanglement, Forward Activations, Entanglement Graph

TL;DR

CLARE proposes a lightweight representation-level method that quantifies the entanglement between facts through forward activations of a single intermediate layer to predict ripple effects of model editing, achieving a 62.2% average Spearman correlation improvement over gradient methods while being 2.74× faster and requiring 2.85× less memory.

Background & Motivation

Background: Model editing modifies model weights to update specific factual associations, but often produces ripple effects — unexpected behavioral changes propagating to other outputs and even into the hidden space.

Limitations of Prior Work: (1) Ripple effects can extend to semantically unrelated facts, causing cross-domain interference; (2) Existing methods (e.g., GradSim) use gradient similarity, which is computationally expensive and poorly correlated with cross-domain ripple effects; (3) A systematic study of large-scale cross-domain ripple effects is lacking.

Key Challenge: Model editing requires precise prediction of which facts will be affected, but existing methods are both slow and inaccurate.

Goal: Propose a lightweight, high-accuracy ripple effect prediction method and construct large-scale entanglement graphs.

Key Insight: Replace gradient computation with forward activations — only a single layer's activations are needed to quantify entanglement.

Core Idea: Entanglement between facts can be quantified by the similarity of forward activation representations at a critical layer, without computing gradients.

Method

Overall Architecture

(1) Prepare an 11,427-fact cross-domain corpus (from 3 existing datasets); (2) Extract forward activations at the critical intermediate layer for each fact; (3) Compute entanglement scores between fact pairs; (4) Construct large-scale entanglement graphs for protection set construction, audit trails, and red-team testing.

Key Designs

  1. CLARE Entanglement Quantification (Critical Layer Representation Entanglement):

    • Function: Lightweight quantification of the entanglement degree between two facts in the model
    • Mechanism: For each fact prompt, extract forward activation vectors at the critical intermediate layer (typically identified by causal tracing), and compute the similarity between activation vectors of two facts as the entanglement score. No backpropagation or gradient computation required
    • Design Motivation: Gradient methods require computing full gradients for each fact, with enormous computational and memory costs; forward activations require only a single forward pass

  2. Large-Scale Entanglement Graph Construction:

    • Function: Visualize the global entanglement structure of model knowledge
    • Mechanism: Compute pairwise CLARE entanglement scores for 11,427 facts, constructing weighted entanglement graphs. Graphs for multiple models are released
    • Design Motivation: Entanglement graphs support stronger protection set construction, audit trails, cost-effective red-team testing, and other downstream applications

  3. Cross-Domain Fact Corpus:

    • Function: Systematically study how edits propagate globally
    • Mechanism: Integrate 11,427 facts from 3 existing datasets, covering 212 prompt formats and 6,140 unique subjects
    • Design Motivation: Existing research focuses only on 1-2 hop semantic neighbors, without addressing cross-domain propagation

Loss & Training

No model training is involved. CLARE uses only forward passes to extract activations.

Key Experimental Results

Main Results

  • CLARE improves Spearman correlation by 62.2% on average compared to GradSim (maximum improvement of 0.31)
  • 2.74× faster, 2.85× reduction in peak GPU memory
  • Storage requirements are a fraction of baselines

Ablation Study

  • Consistent results across multiple editing techniques (ROME, MEMIT) and multiple models
  • Protection sets built from entanglement graphs significantly reduce editing side effects

Key Findings

  • Forward activations predict cross-domain ripple effects better than gradients
  • Ripple effects can propagate to semantically completely unrelated facts
  • Single-layer activations are sufficient to capture critical entanglement information

Highlights & Insights

  • Replacing gradient computation with forward activations is a clean and effective insight
  • Releasing large-scale entanglement graphs provides a valuable resource to the community
  • Audit trail and red-team testing application scenarios demonstrate practical value

Limitations & Future Work

  • Critical layer selection may depend on model architecture
  • Entanglement graphs are static and may not reflect changes after multiple edits
  • Future work may explore dynamic entanglement graphs and larger-scale fact repositories
  • An important improvement over GradSim and RippleEdits
  • Provides new tools for model editing safety and interpretability
  • The entanglement graph concept can be extended to model safety and interpretability research

Rating

  • Novelty: ⭐⭐⭐⭐⭐ Forward activation entanglement quantification is a significant methodological innovation
  • Experimental Thoroughness: ⭐⭐⭐⭐⭐ 11,427 facts, multiple models, multiple editing techniques — comprehensive validation
  • Writing Quality: ⭐⭐⭐⭐ Problem motivation is clear, method description is concise