SHIFT turns LLMs into reasoning-efficient retrievers with reconstruction
_reachsumit · x · 2026-07-24
SHIFT, short for Self-reconstruction Harnesses Implicit Fine-grained Thinking for Retrieval, turns LLMs into more reasoning-efficient retrievers.
The method combines:
- residual projection
- bidirectional attention pooling
- fine-grained next-token-prediction reconstruction
The core idea is to make retrieval less like plain generation and more like structured internal reconstruction, so the model can reason about candidates more efficiently.
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