Self-Improved Intent Alignment via Post Hoc Grounding for Repository Generation

Benefiting from increasingly capable large language models (LLMs), coding agents are able to tackle increasingly complex programming tasks. However, existing agents still face significant limitations in end-to-end repository generation. In particular, fine-grained implementation intents described in large-scale task specifications often lack sufficient salience, especially over long-horizon generation trajectories, leading to discrepancies between the generated repositories and the intended functionality. In this paper, we propose SIRA, a runtime augmentation framework that mitigates specification drift by re-grounding coding agents in task intents at file-creation checkpoints. SIRA combines self-improved key intent extraction with retrieval-driven post hoc grounding. It derives transferable extraction guidance from discrepancies observed between task specifications and their generated repositories in prior repository-generation tasks and uses this guidance to construct a compact index of behaviorally critical intents for the current task. As each new file is created, SIRA retrieves the file-relevant intents and injects them into the agent's context, enabling local inconsistencies to be identified and corrected before they propagate. We evaluate SIRA on two representative repository-generation benchmarks, NL2Repo and RepoGenesis. Experimental results show that SIRA consistently achieves the best performance on the primary metrics, surpassing the strongest baseline by an average of 5.28 points in Score on NL2Repo and 9.63 points in Pass@1 on RepoGenesis.