Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents

2026-06-11 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

TRACE (Test-time Rule Acquisition and Compiled Enforcement) is a novel skill-layer pipeline designed to address the persistent issue of large language model (LLM) coding agents failing to retain user corrections across sessions. Existing memory solutions, such as Mem0, still result in 57.5% of applicable preference checks being violated. TRACE mines user corrections from chat interactions, rewrites them as atomic rules, and compiles these into runtime enforcement checks that agents must satisfy before completing future tasks. Evaluated on ClawArena coding-agent tasks, TRACE significantly reduced held-out preference violation from 100.0% to 37.6% for in-distribution tasks and to 2.0% for out-of-distribution tasks. On MemoryArena-derived tasks, it lowered in-distribution violation from 100.0% to 60.5%, while maintaining or improving task pass rates compared to strong memory baselines. This approach aims to reduce the need for users to repeatedly state the same corrections.

Key takeaway

For AI Engineers developing interactive coding agents, if you are struggling with agents repeatedly violating user preferences despite memory solutions, consider integrating a runtime enforcement pipeline like TRACE. This approach, which compiles user corrections into atomic rules, can drastically reduce the need for users to restate the same feedback, improving agent reliability and user satisfaction. You should explore the provided open-source code to implement similar preference compliance mechanisms.

Key insights

Compiling user corrections into runtime enforcement rules significantly improves coding agents' compliance with preferences across sessions.

Principles

• Memory alone does not reliably solve repeated-friction failure modes.
• User-generated rules enhance agent compliance more effectively.
• Runtime enforcement prevents preference violations proactively.

Method

TRACE mines user chat corrections, rewrites them as atomic rules, and compiles these into runtime checks. Agents must pass these checks before completing future tasks, ensuring preference compliance.

In practice

• Implement TRACE to reduce repeated user corrections for coding agents.
• Use runtime checks to enforce user preferences in LLM workflows.
• Integrate user feedback directly into agent behavior rules.

Topics

• LLM Agents
• User Corrections
• Runtime Enforcement
• Coding Agents
• Preference Compliance
• TRACE Pipeline

Code references

• YujunZhou/TRACE_exp
• YujunZhou/tellonce

Best for: Research Scientist, AI Scientist, AI Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.