# WOWService Large Model Interaction System — Technical Report Summary
## Industry Context & Challenges
In the **local life services sector**, implementing large models faces a **“triple dilemma”**:
1. **General vs. Domain-Specific Alignment** — Hard to adapt general capabilities to specialized business needs.
2. **Reliability vs. Personalization** — Balancing stable service with customized user experiences in complex scenarios.
3. **Data Costs & Long Cycles** — High-quality data is expensive and training takes too long, raising development difficulty.
Additionally, the industry **lacks reusable frameworks** and **real-world optimization solutions**, resulting in **low deployment efficiency**.
**Key Question:**
How can we break the stalemate and achieve the optimal balance of **experience** and **efficiency**?
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## WOWService: An Overview
Leveraging Meituan’s deployment experience in **intelligent customer service** and **multi-business scenarios**, the **LongCat team** released:
🔗 **Technical Report:** [https://arxiv.org/pdf/2510.13291](https://arxiv.org/pdf/2510.13291)
WOWService integrates:
- **Multi-Agent Collaboration**
- **Reinforcement Learning**
- **Domain Knowledge Enhancement**
Key capabilities:
- Handles complex, multi-task instructions with **human-machine collaborative annotation**, **model self-critique**, and **knowledge rewriting**.
- Achieves **similar results with only 10% of the annotated data** compared to traditional small models.
- Covers dozens of Meituan business scenarios.
- **Outperforms the Base model in 11 metrics**.
- Enables **continuous self-evolution** through data and knowledge integration.
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## Core Technical Frameworks
### 1. Data & Knowledge Dual-Drive
**Goal:** Full-scope evolution for business scenarios.
- Combines **structured business knowledge** (rules, process specs) with **large-scale real interaction data**.
- Balances **knowledge vs. dataset** ratio to enhance compliance.
- Reinforcement learning strengthens adherence to rules in dynamic environments.
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### 2. Self-Optimization Training (SRT)
**Goal:** Self-evolving intelligent interaction.
Steps:
1. **Positive Sample Selection** — Picks excellent cases from service logs, adding them to the training set.
2. **Negative Sample Analysis** — Identifies low-quality outputs, rewrites dialogues into preference comparison data.
3. **Autonomous Data Loop** — Automates collection, filtering, and evaluation of dialogues for continuous improvement.
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### 3. Four-Phase Training Pipeline
**Goal:** Systematic upgrade of interaction capabilities.
Phases:
1. **Continual Pre-Training (CPT)** — Retains general + domain strength via adaptive data mixing.
2. **Supervised Fine-Tuning (SFT)** — Uses lightweight, high-quality datasets to align with domain styles.
3. **Direct Preference Optimization (DPO)** — Adjusts outputs toward human expectations.
4. **Reinforcement Learning (RL)** — Hybrid “data + knowledge” drive with refined reward systems for complex reasoning.
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### 4. Multi-Agent Collaboration Mechanism
**Goal:** Flexible, scenario-oriented cooperation.
Architecture:
- **Main Agent** — Manages conversations and decision-making.
- **Specialized Sub-Agents** — Handle tasks like outbound calls, multimodal understanding, proactive collaboration.
- **Dynamic Invocation** — Main agent calls sub-agents as needed and integrates outputs.
- **Handoff Mode** — Seamless context transfer among agents to maintain reliability.
Example:
A **Proactive Collaboration Agent** detects user needs, switches contexts intelligently, and boosts satisfaction.
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## Experimental Results
**Outcome:** WOWService beats the Base model in all **11 metrics**:
- **RR** — Repetition Rate
- **SER** — Solution Effectiveness Rate
- **QR** — Queue Rate
- **FSR** — Full Score Rate
- **AVG_F1** — Average F1 Score
- **RF** — Recall Frequency
- **SR_Acc** — Solution Accuracy
- **UR** — Usability Rate
- **DS_Acc** — Domain Accuracy
- **USM1, USM2** — User Satisfaction Metrics
**Impact:**
- Stronger automation
- Higher closed-loop efficiency
- Better adaptability
- More accurate reasoning
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## Summary & Outlook
WOWService converts **data + knowledge dual-drive**, **SRT**, and **multi-agent collaboration** into **deployable, high-efficiency AI solutions**.
**Key Achievements:**
- Domain adaptation with minimal extra data
- Reduced annotation cost to **10%** of traditional methods
- Continuous improvement via four-stage training pipeline
- Stable multi-agent cooperation in complex business environments
**Future Directions:**
- Deeper daily-life integration
- Agent-based reinforcement learning for tool usage
- Enhanced multimodal understanding
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## Related Tools & Ecosystem
Open-source platforms such as **[AiToEarn官网](https://aitoearn.ai/)** help creators and enterprises:
- Generate AI-driven content
- Publish across **Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X (Twitter)**
- Analyze and rank models
- Monetize AI creativity
**Potential Synergy:** Integrating AiToEarn with WOWService could enable **large-scale, efficient deployment** of intelligent interactive services in both enterprise and creator economies.
