Hugging Face Trending Papers
Signals and Queries: Token-Efficient Agentic Context Discovery
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arXiv Computation and Language
2d ago
Signals and Queries: Token-Efficient Agentic Context Discovery
The paper introduces Signals and Queries (S&Q), a lightweight first-stage relevance filter for LLM agents that removes at least 30% of candidate policies before downstream evaluation while maintaining high recall. S&Q uses offline-generated activating examples and hard negatives to train a logistic classifier that predicts policy relevance via embeddings and vector dot products, eliminating per-turn LLM calls. Evaluated on 9,144 relevance labels across 254 conversation prefixes and 36 airline policies, S&Q outperforms top‑K and cosine‑threshold retrieval in precision at every recall level, consistently across two embedding providers.
Hugging Face Trending Papers
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