Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents

Researchers introduce RSCB-MC, a risk-sensitive contextual bandit system that improves how LLM-based coding agents decide whether to use external memory for debugging tasks. Rather than treating memory retrieval as a simple similarity-matching problem, the system treats it as a safety-critical control problem, achieving 62.5% success rate with zero false positives in testing.

This research addresses a critical vulnerability in AI-assisted development: the dangerous tendency of language models to misapply previous solutions to superficially similar but fundamentally different problems. Memory injection errors in coding agents can introduce bugs, security vulnerabilities, or unsafe patches, making the decision of when to use memory as important as which memory to retrieve.

The RSCB-MC approach represents a paradigm shift in how AI systems should handle external knowledge bases. Rather than maximizing retrieval accuracy, it explicitly penalizes false positives more heavily than missed opportunities—a conservative stance appropriate for safety-critical systems. The 16-feature contextual state captures nuanced factors like structural compatibility and feedback history, allowing fine-grained risk assessment beyond simple semantic similarity scores.

For the broader AI development ecosystem, this work validates that production AI systems require explicit safety mechanisms at every decision point. The zero false-positive rate in testing demonstrates that risk-aware design can be competitive with naive approaches. This has implications for enterprise deployments of coding assistants, where incorrect memory injection could damage productivity or introduce vulnerabilities.

The research signals growing maturity in AI safety engineering for practical applications. As companies deploy autonomous coding agents at scale, systems that abstain safely when uncertain—rather than confidently making mistakes—will become table-stakes requirements. Future work likely extends these principles to other knowledge-injection scenarios in multimodal AI systems.

• →RSCB-MC achieves 62.5% success rate with 0% false positives by treating memory use as a safety-critical control problem, not pure retrieval optimization.
• →The system uses 16 contextual features including relevance, uncertainty, structural compatibility, and false-positive risk to make abstention decisions.
• →Risk-sensitive design penalizes incorrect memory injection more heavily than missed reuse, making safety the primary objective.
• →Zero false-positive rate in 200-case validation demonstrates that conservative AI decision-making can match or exceed aggressive retrieval strategies.
• →Pattern-variant-episode schema for knowledge storage enables compatibility checking beyond surface-level stack trace similarity.