DeepRepro: State-Aware Subplanning for Paper-to-Code Reproduction in Evolving Repositories

• URL: http://arxiv.org/abs/2608.26557v1
• Date: Thu, 27 Aug 2026 02:54:30 GMT
• Title: DeepRepro: State-Aware Subplanning for Paper-to-Code Reproduction in Evolving Repositories
• Abstract summary: We propose a state-aware framework for paper-to-code reproduction based on execution-state-aware subplanning.<n>DeepRepro transforms evolving repository states and runtime feedback into fine-grained implementation subplans.<n>Experiments on PaperBench Code-Dev show that DeepRepro consistently outperforms strong scientific and commercial code-agent baselines.
• Score: 116.27249583403871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in agentic large language models (LLMs) have enabled increasingly autonomous software engineering workflows, yet automatic machine learning (ML) paper-to-code reproduction remains a challenging long-horizon problem. Unlike conventional code generation, this task requires constructing and maintaining a fully functional repository whose state continuously evolves during execution. Existing systems typically rely on static upfront planning followed by sequential file-level generation, which often leads to inconsistencies as dependencies, interfaces, and execution feedback change over time. We propose DeepRepro, a state-aware framework for paper-to-code reproduction based on execution-state-aware subplanning. DeepRepro dynamically transforms evolving repository states and runtime feedback into fine-grained implementation subplans, keeping planning aligned with execution throughout repository construction. The framework further incorporates repository-aware orchestration and a lightweight process-aware interface for transparent monitoring of long-horizon reproduction. Experiments on PaperBench Code-Dev show that DeepRepro consistently outperforms strong scientific and commercial code-agent baselines.

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