Learning When to Remember: Risk-Sensitive Contextual Bandits for Abstention-Aware Memory Retrieval in LLM-Based Coding Agents
In the rapidly evolving landscape of artificial intelligence, large language model (LLM)-based coding agents are increasingly being utilized to streamline debugging processes and enhance software reliability. A significant aspect of their functionality hinges on the effective use of external memory, which allows these agents to draw upon past experiences, repair traces, and repository-specific operational knowledge. However, the challenge lies in ensuring that the memory retrieved is genuinely relevant to the current issue at hand. Superficial similarities in error messages or stack traces can lead to unsafe memory injections, potentially compounding existing problems rather than resolving them.
To address this critical issue, researchers have reframed the memory retrieval process as a selective, risk-sensitive control problem, diverging from traditional top-k retrieval approaches. This innovative perspective is encapsulated in the introduction of RSCB-MC, a risk-sensitive contextual bandit memory controller. RSCB-MC is designed to make nuanced decisions regarding memory usage, enabling agents to determine whether to utilize memory at all, inject the most relevant resolution, summarize multiple candidates, or abstain from using memory altogether. Additionally, it can solicit feedback when necessary.
Key Features of RSCB-MC
- Memory Storage and Retrieval: RSCB-MC employs a pattern-variant-episode schema to store reusable issue knowledge, allowing for efficient retrieval of relevant memories.
- Contextual State Representation: The system converts retrieval evidence into a structured 16-feature contextual state, which captures essential factors such as relevance, uncertainty, structural compatibility, feedback history, false-positive risk, latency, and token cost.
- Reward Design: The reward system within RSCB-MC is meticulously crafted to penalize false-positive memory injections more severely than missed reuse opportunities, thereby treating non-injection and abstention as primary safety actions.
Performance Metrics
In rigorous testing scenarios, RSCB-MC has demonstrated impressive performance. In deterministic smoke-scale artifacts, the system achieved a remarkable offline replay success rate of 62.5%, all while maintaining a 0.0% false-positive rate. Moreover, in a bounded validation consisting of 200 hot-path cases, RSCB-MC attained a proxy success rate of 60.5% with a corresponding false-positive rate of 0.0%. The system’s efficiency is further underscored by its decision latency, clocking in at an impressive 331.466 microseconds at the 95th percentile.
Conclusion
The research underscores a pivotal advancement in the realm of coding agents; the crucial question transcends the mere selection of the most similar memory. Instead, it emphasizes the necessity of ensuring that any retrieved memory is sufficiently safe to influence the debugging trajectory. As LLM-based coding agents continue to evolve, the implementation of risk-sensitive contextual bandit approaches like RSCB-MC could redefine how AI interacts with memory, ultimately enhancing the reliability and safety of software development practices.
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