Papers with constrained counterfactual optimization
The Mechanics of Interference: Defusing Distractors in RAG via Sparse Autoencoder Interventions (2026.findings-acl)
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| Challenge: | Large language models exhibit a critical vulnerability to distractor interference when processing retrieval-augmented contexts. |
| Approach: | They propose a mechanistic framework that corrects this failure mode through targeted interventions in the model’s latent space. |
| Outcome: | The proposed framework achieves recovery rates of up to 94% on distractor-vulnerable samples on Gemma-2 and Llama-3 model families across three QA benchmarks. |