arXiv AI By Nazib Sorathiya, Daniel Zhang, Bardiya Akhbari

Enrich-Retrieve-Rank: Scaling Capability Discovery Beyond In-Context Routing

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The paper introduces Enrich‑Retrieve‑Rank, a scalable method for discovering capabilities in large agent ecosystems. It replaces in‑context routing with an offline enrichment step that converts sparse metadata into searchable profiles, followed by an online retrieve‑then‑rank pipeline that returns a ranked shortlist without invoking candidates. Experiments show that as the number of capabilities grows from 10 to 7,278, the new approach maintains higher top‑1 accuracy and reduces cost by 70× compared to full‑context baselines.

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arXiv Machine Learning
Jul 30

Scores Are Not Decisions: Cost-Aware Stopping for Tool Acquisition in LLM Agents

arXiv:2607. 27083v1 Announce Type: new Abstract: As LLM agents increasingly depend on diverse external services such as search engines, databases, and connectors, agent harnesses face a fundamental tool-selection challenge: acquiring too few tools leaves the task under-informed, while too many adds cost, context load, and privacy exposure.

By Yicheng Feng, Yan Zhang, Yan Cheng, Wei Qi
Hugging Face Trending Papers
Aug 19

Pairwise Ranking Outperforms Single-Action RL for Offline Explanation Selection: A Practical Lesson

The paper presents a cost‑effective approach for industrial explainable‑recommendation systems by decoupling explanation generation from selection. Explanations are pre‑generated using six prompt styles and two commodity LLMs, then a lightweight CPU‑resident selector (e.g., LambdaRank) chooses the best one at request time, achieving sub‑100 ms latency without GPUs. Experiments on a 2,958‑pair Google Local subset and a 300‑pair MovieLens‑1M split show that pairwise ranking outperforms single‑action RL methods, while KG‑path selectors achieve near‑perfect unique‑output rates, and the overall end‑to‑end build cost is around $15 on commodity hardware.