Papers by Mohan Gurusamy
CLaRE-ty Amid Chaos: Quantifying Representational Entanglement to Predict Ripple Effects in LLM Editing (2026.findings-acl)
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| Challenge: | Large language models (LLMs) are outdated or incorrect over time due to unintended ripple effects that propagate even to the hidden space. |
| Approach: | They propose a lightweight representation-level technique to identify where ripple effects may occur by detecting entanglement between facts using forward activations from a single intermediate layer. |
| Outcome: | The proposed method achieves 62.2% improvement in Spearman correlation with ripple effects while being 2.74 faster and using 2.85 less peak GPU memory. |