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Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System

Longfei Yun, Yihan Wu, Haoran Liu, Xiaoxuan Liu, Ziyun Xu, Yi Wang, Yang Xia, Pengfei Wang, Mingze Gao, Yunxiang Wang, Changfan Chen, Junfeng Pan

TL;DR

GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment, and incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals.

Abstract

Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this domain is increasingly bottlenecked by the engineering context constraint: the arduous process of translating ambiguous product intent into reasonable, executable, verifiable hypotheses, rather than by modeling techniques alone. We present GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment. Rather than treating optimization as static model selection, GEARS leverages Specialized Agent Skills to encapsulate ranking expert knowledge into reusable reasoning capabilities, enabling operators to steer systems via high-level intent vibe personalization. Furthermore, to ensure production reliability, the framework incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals. Experimental validation across diverse product surfaces demonstrates that GEARS consistently identifies superior, near-Pareto-efficient policies by synergizing algorithmic signals with deep ranking context while maintaining rigorous deployment stability.

Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System

TL;DR

GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment, and incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals.

Abstract

Modern large-scale ranking systems operate within a sophisticated landscape of competing objectives, operational constraints, and evolving product requirements. Progress in this domain is increasingly bottlenecked by the engineering context constraint: the arduous process of translating ambiguous product intent into reasonable, executable, verifiable hypotheses, rather than by modeling techniques alone. We present GEARS (Generative Engine for Agentic Ranking Systems), a framework that reframes ranking optimization as an autonomous discovery process within a programmable experimentation environment. Rather than treating optimization as static model selection, GEARS leverages Specialized Agent Skills to encapsulate ranking expert knowledge into reusable reasoning capabilities, enabling operators to steer systems via high-level intent vibe personalization. Furthermore, to ensure production reliability, the framework incorporates validation hooks to enforce statistical robustness and filter out brittle policies that overfit short-term signals. Experimental validation across diverse product surfaces demonstrates that GEARS consistently identifies superior, near-Pareto-efficient policies by synergizing algorithmic signals with deep ranking context while maintaining rigorous deployment stability.
Paper Structure (35 sections, 13 equations, 5 figures, 4 tables, 1 algorithm)

This paper contains 35 sections, 13 equations, 5 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Works
  3. The Evolution of Context Engineering
  4. Uplift Modeling and HTE
  5. Adaptive Experimentation (AE)
  6. The GEARS System
  7. Motivation.
  8. Intent-Conditioned Personalization
  9. Candidate Generation via GAS.
  10. Tolerance-Based Frontier Expansion.
  11. Insight-Driven Policy Selection
  12. Deterministic Lifecycle Governance
  13. Experiments
  14. Experimental Settings
  15. Dataset
  16. ...and 20 more sections

Figures (5)

  • Figure 1: End-to-end workflow for experiment-driven optimization with GEARS: starting from an experiment link, the GAS agent searches over candidate policies to generate insights (e.g., trade-offs between topline metrics), followed by feature understanding, validation and recommendations (stability and interpretability checks), and culminating in an auto-generated iterated ranking configuration.
  • Figure 2: Tolerance-based frontier expansion allows GEARS to surface both convex and near-frontier candidates, enabling more stable and preference-aligned policy selection.
  • Figure 3: Pareto efficiency of generated policies. We plot the performance of all candidate policies, with the Pareto frontier (dark blue line) indicating the optimal trade-off curve. Annotated stars mark key policies of interest.
  • Figure 4: The backtest results indicate that the metrics improvement achieved by the selected policy remains consistent over a period of one month.
  • Figure 5: comprehensive evaluation of GEARS against baselines. We visualize the performance across ten distinct metrics, covering Ranking Quality (nDCG@1, 3, 5), Precision (Precision@1, 3, 5), Recall (Recall@1, 3, 5), and Top-1 Accuracy.