Trace2Policy: From Expert Behavior Traces to Self-Evolving Decision Agents
Trace2Policy transforms expert behavior into self-evolving decision agents using EISR, achieving 79.6% accuracy.
Key Findings
Methodology
Trace2Policy employs EISR (Error-driven Iterative Skill Refinement) to convert expert behavior into executable decision rules. EISR optimizes a rule document through a diagnose-and-patch loop, classifying errors into MISSING, WRONG, or CONFLICT, and applies patches confirmed by a regression gate. This process compiles rules into Python without LLM calls.
Key Results
- After 8 EISR rounds, rule accuracy improved from 70% to 79.6%, with compiled Python outperforming LLM prompts by 9.8 percentage points.
- Deployed for 22 days at a logistics company, handling 3,349 audit cases, the compiled pipeline outperformed the pure LLM baseline (72.7%).
- Auto-EISR matched Human-EISR accuracy at $5-10 per cycle, transferring to other benchmarks without re-engineering.
Significance
Trace2Policy significantly enhances decision accuracy in compliance-sensitive tasks by improving rule quality rather than model capability. It achieves efficient rule execution without relying on LLMs, reducing computational costs and providing auditable decision processes for enterprises.
Technical Contribution
Trace2Policy introduces a novel rule optimization method through EISR, distinct from existing model performance optimization methods. It offers rule version control and regression-gated mechanisms, ensuring rule interpretability and consistency.
Novelty
Trace2Policy is the first to transform expert behavior into self-evolving decision agents through EISR, providing deeper knowledge extraction compared to existing one-shot methods.
Limitations
- EISR requires multiple iterations to reach optimal performance; initial rule extraction may lack precision.
- Cross-domain applicability of rules needs further validation.
- System relies heavily on accurate error classification.
Future Work
Future work includes exploring EISR applications in other domains, optimizing error classification mechanisms, and developing more efficient automated rule extraction methods.
AI Executive Summary
Trace2Policy transforms expert behavior into self-evolving decision agents using the EISR mechanism, addressing the challenge of extracting and optimizing decision rules in compliance-sensitive tasks. Unlike traditional methods that rely on enhancing model capabilities, Trace2Policy improves decision accuracy by enhancing rule quality.
In production, Trace2Policy compiles optimized rules into Python code, avoiding LLM calls and significantly boosting execution efficiency. During a 22-day deployment, it handled 3,349 audit cases, outperforming the pure LLM baseline. The Auto-EISR variant achieved comparable accuracy to Human-EISR at a low cost and successfully transferred to other benchmarks.
Despite significant advancements, Trace2Policy has room for improvement in cross-domain applications and error classification mechanisms. Future research will focus on optimizing these areas and exploring its potential in more domains.
Deep Analysis
Background
In enterprises, many tasks involve complex decision-making that relies on expert experience and implicit rules. Traditional automation methods often fail to capture these implicit rules, whereas Trace2Policy achieves systematic extraction and optimization of rules through the EISR mechanism.
Core Problem
The core problem is how to extract executable decision rules from expert behavior and optimize them to improve decision accuracy without relying on LLMs.
Innovation
Trace2Policy's core innovation lies in introducing the EISR mechanism, which enhances rule quality through iterative optimization. Unlike existing methods, EISR provides rule interpretability and consistency and can be executed efficiently in production.
Methodology
- �� EISR mechanism optimizes rule documents through a diagnose-and-patch loop.
- �� Errors are classified into MISSING, WRONG, or CONFLICT.
- �� Regression gate confirms patch effectiveness.
- �� Rules are compiled into Python code to enhance execution efficiency.
Experiments
Experiments were conducted at a logistics company, processing 3,349 audit cases. Five LLMs were compared to evaluate rule accuracy before and after EISR optimization. Results showed significant accuracy improvement after 8 EISR rounds.
Results
After multiple EISR rounds, rule accuracy improved from 70% to 79.6%. In production, compiled Python rules outperformed LLM prompts by 9.8 percentage points, significantly enhancing execution efficiency.
Applications
Trace2Policy is applicable to compliance-sensitive tasks requiring high decision accuracy, such as audits and compliance checks. Its rule optimization mechanism can reduce decision costs for enterprises.
Limitations & Outlook
Despite progress in rule optimization, Trace2Policy has room for improvement in cross-domain applications and error classification mechanisms. Future research will focus on optimizing these areas.
Plain Language Accessible to non-experts
Imagine a factory where workers operate machines based on experience and implicit rules. Trace2Policy acts like a smart assistant that observes the workers, records these implicit rules, and continuously optimizes them to enhance factory efficiency. This assistant doesn't rely on external experts but improves factory productivity through its learning and optimization.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to make decisions based on different scenarios. Trace2Policy is like a super helper that watches how you play, records your decision rules, and keeps optimizing them so you perform better in the game. This helper doesn't need outside help but improves your gaming skills through its learning.
Glossary
EISR (Error-driven Iterative Skill Refinement)
A mechanism that optimizes rule documents through a diagnose-and-patch loop.
Used to optimize decision rules in Trace2Policy.
LLM (Large Language Model)
A large-scale model used for natural language processing.
Used to compare and evaluate Trace2Policy's rule optimization effects.
Python Compilation
Compiling rules into Python code to enhance execution efficiency.
Used to execute optimized rules in production environments.
Regression Gate
A mechanism to confirm the effectiveness of patches.
Ensures rule optimization doesn't break previously correct cases.
Auto-EISR
An automated EISR variant driven by LLM.
Used to achieve rule optimization at low cost.
Open Questions Unanswered questions from this research
- 1 How to enhance EISR's transferability across domains?
- 2 How to optimize error classification mechanisms to improve rule optimization efficiency?
Applications
Immediate Applications
Logistics Audit
Trace2Policy can be used to enhance audit efficiency in logistics companies, reducing decision costs.
Long-term Vision
Cross-domain Applications
In the future, Trace2Policy can be applied to other high-decision-accuracy fields, such as legal compliance checks.
Abstract
Decision rules that enterprise experts apply tacitly -- in auditing, compliance, and contract review -- can be systematically recovered and improved through iterative error analysis. We present \textbf{Trace2Policy}, whose core mechanism -- \textbf{EISR} (\textbf{E}rror-driven \textbf{I}terative \textbf{S}kill \textbf{R}efinement) -- maintains a human-readable rule document as its optimization target: each round executes the rules on a validation set, clusters errors by root cause into MISSING, WRONG, or CONFLICT types, applies targeted patches, and commits only those that pass a regression gate. \textbf{For this class of compliance-sensitive, skewed-base-rate decision tasks, we identify rule quality -- not model capability -- as the dominant performance lever}: across five LLMs, one-shot distillation plateaus near $\sim$70\% on the deployed pool, while eight EISR rounds lift the same rules to 79.6\% when compiled into deterministic Python -- zero LLM calls at inference. \textbf{Execution form compounds the gain: in production, the same EISR-refined content runs 9.8~pp higher as compiled Python than as an LLM prompt, a form-and-engineering bundle the 22-day deployment matured together.} Deployed for 22 days at a major logistics carrier (3,349 audit cases), the compiled pipeline outperforms the pure-LLM baseline it replaced (72.7\%); on these calibrated, skewed-base-rate workloads, re-enabling LLM fallback monotonically degrades accuracy. An LLM-driven variant, \textbf{Auto-EISR}, reproduces this refinement at \$5--\$10 per cycle versus $\sim$70 expert-hours, and transfers to four public benchmarks spanning legal reasoning (LegalBench) and process-mining decisions (BPIC 2012) without re-engineering.