Getting Better at Working With You: Compiling User Corrections into Runtime Enforcement for Coding Agents
arXiv:2606.13174v1 Announce Type: cross Abstract: Interactive LLM agents are becoming part of daily work, but they do not reliably become easier to work with over time: a correction remembered in one session may still be violated in the next. We study this gap between preference access and preference compliance. In tasks derived from anonymized real-user friction cases, Mem0 memory still leaves 57.5% of applicable preference checks violated. We introduce Test-time Rule Acquisition and Compiled Enforcement (TRACE), a drop-in skill-layer pipeline for coding-agent runtimes that mines user correct
The paper directly addresses a key limitation in current interactive LLM agents, which is their inability to reliably incorporate user corrections over time, a problem becoming more apparent as these agents are deployed in real-world scenarios.
Improving the 'coachability' of AI agents directly translates to greater user satisfaction, reduced friction in daily work, and faster adoption of these technologies, making them more effective and reliable tools.
AI agents will become more adaptive and personalize their behavior based on user feedback, potentially reducing the need for constant re-instruction and making them more integral to workflows.
- · AI software developers
- · Businesses adopting AI agents
- · End-users of coding agents
- · Legacy software with poor customization
Coding agents will become more efficient and less frustrating for developers to use.
Increased trust and reliance on AI agents for more complex and personalized tasks.
Accelerated development of domain-specific, highly customized AI agents across various industries.
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Read at arXiv cs.CL