Microsoft Adapts Dense Retrievers Using Agent Traces with Zero Labels
_reachsumit · x · 2026-08-18
Microsoft proposes Navigation-Informed Embeddings (NIE), a method to adapt dense retrievers using traces from agentic retrieval workflows (query, retrieval, stop signals) without new relevance labels or synthetic data.
Core Methods:
- NIE-Stop: Treats the stopping document as a soft positive.
- NIE-Path: Uses preceding path documents as hard comparisons and imposes ordinal constraints with geometric decay.
Results:
- Improved Recall@20 on a target benchmark from 72.2 to 78.0 using a BGE encoder adapted from source trajectories.
- NIE-Path achieved 55.4 on long-path tasks compared to 46.7 for the unadapted encoder.
- Improved nDCG@10 by 1.9 points on BEIR HotpotQA without public-benchmark training.
NIE provides a lightweight adaptation channel for settings where trajectories are already retained, with zero incremental labeling cost.
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