Repo0: Design-Driven Zero-to-All Code Generation

CodeSpec: Dual Executable Specifications for Agentic Long-Horizon Feature Development

LLM-based code agents have advanced repository-level software development through iterative interaction with codebases and tools. However, feature development requires integrating new behaviors into existing architectures through coherent cross-component functional chains. Existing agents typically derive such chains through free-form reasoning, often producing unreliable feature designs with incomplete functional chains. Moreover, textual designs are difficult to verify and enforce, making it challenging to maintain design-implementation consistency throughout long-horizon development. We propose CodeSpec, a dual executable specification method for repository-level feature development. It builds reliable functional chains from evidence pairing sub-requirement semantics with repository architectures, then compiles them into complementary architecture and behavior specifications that check chain completeness and correctness while preserving design-implementation consistency over long interactions. On FeatureBench, which targets feature development in existing repositories, CodeSpec achieves 70.7%, 55.0%, and 49.9% pass rates under DeepSeek-V4-Pro, outperforming representative baselines such as Claude Code. Results on the repository generation benchmark NL2Repo-Bench further demonstrate its generalizability.

TraceDev: A Traceability-Driven Multi-agent Framework for Requirement-to-Code Development

In modern software development, the rapid advancement of Large Language Models (LLMs) has made the end-to-end transformation of Natural Language Requirements (NLRs) into executable repository-level code increasingly feasible. However, existing approaches typically rely on simplified instructions (e.g., single-sentence descriptions), failing to reflect complex software development scenarios. Moreover, they lack explicit requirement traceability mechanisms, making it difficult to precisely align and validate generated code against original requirements. To address these limitations, we propose TraceDev, a multi-agent framework for automated software development grounded in use cases that contain multiple functional points and complex semantics. TraceDev employs five role-specific agents, including a Requirement Refiner, Designer, Developer, Tester, and Validator. Notably, the Validator Agent constructs and maintains a heterogeneous traceability graph that links requirements, design models, and code artifacts for interacting with the preceding four agents. The traceability graph maintains consistency across various artifacts and serves as a structured context for efficient memory management, supporting reliable repository-level code generation. We evaluate TraceDev on two widely used datasets (including 125 use cases) compared with two state-of-the-art approaches. On the ETOUR dataset, TraceDev achieves a success rate of 53.63\%, outperforming baseline approaches by up to 186.63\%. A similar trend is observed on the SMOS dataset, where TraceDev attains a success rate of 56.82\%, exceeding baseline approaches by up to 340.80\%. These results demonstrate the effectiveness of TraceDev in repository-level code generation from requirements.

Beyond Maintenance: A Benchmark and Multi-Agent Framework for Repository-Usage Code Generation

Repository-level code generation has attracted growing interest, yet most benchmarks and methods remain maintainer-centric, emphasizing bug fixing and feature implementation. In contrast, a common yet underexplored scenario is repository usage: external users want to build applications by correctly invoking repository-internal APIs, composing them into runnable end-to-end workflows rather than modifying the codebase. To support this setting, we introduce RUCCE, a benchmark for repository-usage code generation built from real-world Python repositories. Each instance pairs a natural-language usage instruction with grounded target APIs and a verified reference script, enabling evaluation of both API retrieval and repository-usage code generation. Building on RUCCE, we propose RUCACoder, a closed-loop multi-agent framework with a Retriever for hierarchical repository exploration, a Verifier for reranking and validation, and a Coder for feedback-driven script synthesis. Experiments across multiple backbone LLMs show that RUCACoder consistently outperforms strong retrieval and generation baselines.

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

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.

QiMeng-VPID: Verification-Grounded Port-Level Iterative Decomposition for Complex Verilog Generation

BRIDGE: Building Representations in Domain-Guided Program Synthesis

BRIDGE is presented, a structured prompting framework that decomposes verification into three interconnected domains: Code (implementations), Specifications (formal intent), and Theorem State-ments (constructive correctness claims), and elicits domain-specific intermediate reasoning to connect them.