AI IN GAMING & DIALOGUE SYSTEMS
Aligning Large Language Models with Procedural Rules: An Autoregressive State-Tracking Prompting for In-Game Trading
Large Language Models (LLMs) enable dynamic game interactions but struggle with procedural adherence in rule-governed systems like in-game trading. This work introduces Autoregressive State-Tracking Prompting (ASTP), a novel methodology that makes LLM state-tracking explicit and verifiable, ensuring transactional integrity and enhancing player trust. ASTP, combined with placeholder post-processing, significantly improves compliance and accuracy, allowing smaller models to achieve performance comparable to larger ones with substantial speed gains.
Executive Impact: Enhanced Reliability & Performance
ASTP delivers tangible benefits for rule-governed AI applications, particularly in commercial gaming, by ensuring both conversational flexibility and critical procedural compliance.
0%
Procedural Compliance (up from 78.1%)
0%
Price Calculation Accuracy (up from 84.3%)
0x Faster
Responses for Smaller Models (vs. larger models)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Findings
Explore how ASTP transforms LLM-driven interactions, enhancing both procedural compliance and computational accuracy in critical enterprise applications like in-game trading. The modules below provide a detailed breakdown of the methodology, its impact, and real-world applicability.
ASTP Workflow for Procedural Adherence
Prime Model (Identify Previous State)
→
Guide Reasoning (Conditional Rules)
→
Enforce Compliance (Report Previous State)
0%
Procedural Compliance (up from 78.1%)
0%
Price Calculation Accuracy (up from 84.3%)
0x
Faster Responses for Smaller Models (vs. larger models)
| Method | Key Features | Procedural Compliance (STCR) |
|---|---|---|
| ASTP (Proposed) |
|
99.6% |
| ZS-CoT |
|
89.42% |
| AutoTOD |
|
96.35% |
| DFI-Inspired |
|
88.32% |
Real-world Application: In-Game Trading with ASTP
ASTP successfully addresses the core tension between creative flexibility and procedural demands in rule-governed trading systems. It ensures transactional integrity by making state-tracking explicit and verifiable, complemented by state-specific placeholder post-processing (PPP) for accurate price calculations. This enables smaller models (like Gemini-2.5-Flash) to match the accuracy of larger ones (Gemini-2.5-Pro) with significantly faster response times (from 21.2s to 2.4s, a 9x speedup). This establishes a practical foundation for commercial games, enhancing player trust and system integrity by preventing procedural violations such as unwanted purchases or skipped review steps. PPP improves price calculation accuracy in trading tasks from 84.3% to 99.3%, while the Prime-Guide-Enforce workflow increases procedural adherence from 78.1% to 99.6%.
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