Standard entropy-based uncertainty quantification (UQ) fails in RAG because the 'induction heads' that copy correct answers also trigger 'entropy neurons', causing false uncertainty signals.
March 24, 2026
Original Paper
INTRYGUE: Induction-Aware Entropy Gating for Reliable RAG Uncertainty Estimation
arXiv · 2603.21607
The Takeaway
This reveals a mechanistic 'tug-of-war' in LLM internals that makes traditional entropy metrics unreliable for RAG. Practitioners can use the proposed INTRYGUE method to gate uncertainty based on induction head activation, significantly improving hallucination detection.
From the abstract
While retrieval-augmented generation (RAG) significantly improves the factual reliability of LLMs, it does not eliminate hallucinations, so robust uncertainty quantification (UQ) remains essential. In this paper, we reveal that standard entropy-based UQ methods often fail in RAG settings due to a mechanistic paradox. An internal "tug-of-war" inherent to context utilization appears: while induction heads promote grounded responses by copying the correct answer, they collaterally trigger the previ