RSCB-MC: Risk-Sensitive Memory Control for LLM Coding Agents
A recent publication on arXiv (2604.27283) presents RSCB-MC, a memory controller designed for LLM-based coding agents that is sensitive to risk in contextual bandit scenarios. Rather than approaching memory retrieval as a top-k challenge, this system evaluates whether to forgo memory, introduce a top resolution, summarize various options, engage in high-precision or high-recall retrieval, abstain, or seek feedback. It utilizes a pattern-variant-episode schema to store reusable knowledge about issues and transforms retrieval evidence into a consistent 16-feature vector. This research redefines the utilization of issue memory as a selective control problem sensitive to risk, aiming to prevent unsafe memory injections caused by superficial similarities in stack traces, errors, or paths.
Key facts
- Paper is on arXiv with ID 2604.27283
- Introduces RSCB-MC memory controller
- Uses risk-sensitive contextual bandit approach
- Decides among multiple actions including abstention
- Stores knowledge via pattern-variant-episode schema
- Converts retrieval evidence into 16-feature vector
- Aims to prevent unsafe memory injection
- Focuses on LLM-based coding agents
Entities
Institutions
- arXiv