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[2602.18640] Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System
Summary
The paper presents GEARS, a novel framework for optimizing large-scale ranking systems by transforming the optimization process into an autonomous discovery mechanism, enhancing decision-making through agentic reasoning.
Why It Matters
As large-scale ranking systems face increasing complexity, GEARS addresses the challenge of translating ambiguous product intents into actionable strategies. This framework not only improves operational efficiency but also enhances the reliability of ranking systems, making it crucial for industries relying on data-driven decision-making.
Key Takeaways
- GEARS reframes ranking optimization as an autonomous discovery process.
- The framework utilizes Specialized Agent Skills to encapsulate expert knowledge.
- Validation hooks are integrated to ensure production reliability and filter out ineffective policies.
- Experimental results show GEARS achieves superior, near-Pareto-efficient policies.
- The approach balances algorithmic signals with deep ranking context for stable deployments.
Computer Science > Artificial Intelligence arXiv:2602.18640 (cs) [Submitted on 20 Feb 2026] Title:Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System Authors:Longfei Yun, Yihan Wu, Haoran Liu, Xiaoxuan Liu, Ziyun Xu, Yi Wang, Yang Xia, Pengfei Wang, Mingze Gao, Yunxiang Wang, Changfan Chen, Junfeng Pan View a PDF of the paper titled Decoding ML Decision: An Agentic Reasoning Framework for Large-Scale Ranking System, by Longfei Yun and 11 other authors View PDF HTML (experimental) 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 sta...
