# DeepRule: An Integrated Framework for Automated Business Rule Generation via Deep Predictive Modeling and Hybrid Search Optimization

arXiv: https://arxiv.org/abs/2512.03607 · HTML: https://mlsanity.com/paper/2512.03607/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 30 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 23 of 23)

- [RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback](https://mlsanity.com/paper/2309.00267/) (2023)
- [Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients](https://mlsanity.com/paper/1912.04871/) (2019)
- [OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment](https://mlsanity.com/paper/2502.18965/) (2025)
- [Interpretable Scientific Discovery with Symbolic Regression: A Review](https://mlsanity.com/paper/2211.10873/) (2022)
- [Transformer-based Planning for Symbolic Regression](https://mlsanity.com/paper/2303.06833/) (2023)
- [ToolGen: Unified Tool Retrieval and Calling via Generation](https://mlsanity.com/paper/2410.03439/) (2024)
- [SymFormer: End-to-end symbolic regression using transformer-based architecture](https://mlsanity.com/paper/2205.15764/) (2022)
- [Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws](https://mlsanity.com/paper/2303.03192/) (2023)
- [In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery](https://mlsanity.com/paper/2404.19094/) (2024)
- [Large Language Models Enhanced Collaborative Filtering](https://mlsanity.com/paper/2403.17688/) (2024)
- [Model Distillation for Revenue Optimization: Interpretable Personalized Pricing](https://mlsanity.com/paper/2007.01903/) (2020)
- [Perishability of Data: Dynamic Pricing under Varying-Coefficient Models](https://mlsanity.com/paper/1701.03537/) (2017)
- [Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing](https://mlsanity.com/paper/1801.10242/) (2018)
- [Context-Based Dynamic Pricing with Online Clustering](https://mlsanity.com/paper/1902.06199/) (2019)
- [Multi-Product Dynamic Pricing in High-Dimensions with Heterogeneous Price Sensitivity](https://mlsanity.com/paper/1901.01030/) (2019)
- [Pricing and Competition for Generative AI](https://mlsanity.com/paper/2411.02661/) (2024)
- [Model-Free Assortment Pricing with Transaction Data](https://mlsanity.com/paper/2101.02251/) (2021)
- [Price Optimization in Fashion E-commerce](https://mlsanity.com/paper/2007.05216/) (2020)
- [Towards a Benchmark for Large Language Models for Business Process Management Tasks](https://mlsanity.com/paper/2410.03255/) (2024)
- [Tricks from the Trade for Large-Scale Markdown Pricing: Heuristic Cut Generation for Lagrangian Decomposition](https://mlsanity.com/paper/2404.02996/) (2024)
- [Dynamic Assortment Selection and Pricing with Censored Preference Feedback](https://mlsanity.com/paper/2504.02324/) (2025)
- [Dynamic pricing with Bayesian updates from online reviews](https://mlsanity.com/paper/2404.14953/) (2024)
- [Constrained Pricing in Choice-based Revenue Management](https://mlsanity.com/paper/2501.01764/) (2025)

## Built on by (most cited first, 1 of 1)

- [MALLES: A Multi-agent LLMs-based Economic Sandbox with Consumer Preference Alignment](https://mlsanity.com/paper/2603.17694/) (2026)

## Abstract (arXiv)

This paper proposes DeepRule, an integrated framework for automated business rule generation in retail assortment and pricing optimization. Addressing the systematic misalignment between existing theoretical models and real-world economic complexities, we identify three critical gaps: (1) data modality mismatch where unstructured textual sources (e.g. negotiation records, approval documents) impede accurate customer profiling; (2) dynamic feature entanglement challenges in modeling nonlinear price elasticity and time-varying attributes; (3) operational infeasibility caused by multi-tier business constraints. Our framework introduces a tri-level architecture for above challenges. We design a hybrid knowledge fusion engine employing large language models (LLMs) for deep semantic parsing of unstructured text, transforming distributor agreements and sales assessments into structured features while integrating managerial expertise. Then a game-theoretic constrained optimization mechanism is employed to dynamically reconcile supply chain interests through bilateral utility functions, encoding manufacturer-distributor profit redistribution as endogenous objectives under hierarchical constraints. Finally an interpretable decision distillation interface leveraging LLM-guided symbolic regression to find and optimize pricing strategies and auditable business rules embeds economic priors (e.g. non-negative elasticity) as hard constraints during mathematical expression search. We validate the framework in real retail environments achieving higher profits versus systematic B2C baselines while ensuring operational feasibility. This establishes a close-loop pipeline unifying unstructured knowledge injection, multi-agent optimization, and interpretable strategy synthesis for real economic intelligence.