CORVUS: Context Optimization and Reduction Via Underlying Synchronization for LLM Coding Agents
CORVUS introduces a novel trajectory architecture for LLM coding agents that decouples file-read actions from observations by maintaining a synchronized registry of relevant files. This approach eliminates redundant file copies and stale snapshots, resulting in lighter-weight trajectories that remain synchronized with the actual codebase state. Evaluation on SWE-POLYBENCH_VERIFIED and SWE-BENCH PRO across four LLMs shows significant reductions in input tokens, prompt length, and reasoning cycles
Analysis
TL;DR
- CORVUS introduces a novel trajectory architecture for LLM coding agents that decouples file-read actions from observations by maintaining a synchronized registry of relevant files.
- This approach eliminates redundant file copies and stale snapshots, resulting in lighter-weight trajectories that remain synchronized with the actual codebase state.
- Evaluation on SWE-POLYBENCH_VERIFIED and SWE-BENCH PRO across four LLMs shows significant reductions in input tokens, prompt length, and reasoning cycles while maintaining comparable pass rates.
Why It Matters
This research addresses a critical inefficiency in LLM coding agents where conventional append-only trajectory architectures lead to bloated and outdated context, causing reasoning errors and redundant computations. By proposing a more dynamic and synchronized approach, CORVUS enhances the efficiency and accuracy of LLM-based coding agents, which is crucial for practical applications in software development and automated coding tasks.
Technical Details
- Decoupled File-Read Actions: CORVUS separates file-read actions from their observations, using a synchronized registry to inject only the current contents of relevant files at each reasoning cycle.
- Synchronized Registry: This registry ensures that the agent always has access to the most up-to-date file contents, reducing the need for repeated reads and avoiding stale data.
- Lighter-Weight Trajectories: The new architecture significantly reduces the size of the trajectory by eliminating redundant file copies and snapshots, leading to more efficient processing.
- Performance Metrics: The study reports a 9-50% reduction in average input tokens per task, 15-32% shorter final prompts, and up to 37% fewer reasoning cycles, all while maintaining similar performance levels as measured by pass rates.
Industry Insight
- Efficiency Improvements: Implementing CORVUS can lead to substantial improvements in the efficiency of LLM coding agents, making them more scalable and cost-effective for large-scale software projects.
- Enhanced Accuracy: By keeping the context synchronized with the actual codebase, CORVUS reduces the likelihood of reasoning errors caused by outdated information, thereby improving the reliability of automated coding solutions.
- Adoption Potential: The demonstrated benefits of CORVUS suggest that it could be widely adopted in both academic research and industrial applications, potentially setting a new standard for trajectory management in LLM coding agents.
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