WEAVE is a framework that converts verified pull‑request histories into executable, dependency‑structured tasks for code agents. It organizes related PRs into bounded directed acyclic graphs (DAGs) using repository‑calibrated typed change loci, creating unified tasks that reflect real‑world code evolution. The framework also introduces Graph Credit, a deterministic, test‑grounded metric that measures partial progress consistent with dependencies, providing dense, verifiable rewards for reinforcement learning.
arXiv:2607. 28591v1 Announce Type: cross Abstract: Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation.
Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation. Each task must couple a realistic software state with a specification, development tools, and reliable verification.
E2E-SWE is a benchmark that tests large language models’ ability to create complete, functional software repositories from scratch. It includes 186 tasks across 11 programming languages, each requiring an agent to build an installable project based solely on a natural‑language specification and an empty workspace, while passing a hidden test suite. The benchmark was crafted by software engineers and LLMs, then refined through iterative verification by autonomous agents to ensure clarity and solvability.
SWE-Critix is a post‑training pipeline that creates generalist verifiers for coding‑agent trajectories. It uses supervised fine‑tuning followed by reinforcement learning to train 30B models on CodeForge‑Preview data, enabling the verifier to predict completion labels and generate natural‑language explanations. The approach raises classification accuracy from 57% to 78% on a held‑out test set.
arXiv:2603. 05026v2 Announce Type: replace-cross Abstract: Language model (LM) agents have driven substantial progress in automated software engineering (SWE), yet building and testing software repositories at scale remains a largely manual and labor-intensive bottleneck.
arXiv:2610.09079v1 Announce Type: cross Abstract: Large language models equipped with development environments have moved code generation toward repository-scale construction, yet building complete r...
arXiv:2603. 13428v3 Announce Type: replace-cross Abstract: Real-world software must continuously evolve to meet ever-changing and open-ended requirements.
arXiv:2610.07832v1 Announce Type: cross Abstract: Large language models (LLMs) equipped with terminal access have demonstrated strong capabilities in automating software engineering tasks. However, e...
arXiv:2512. 18552v3 Announce Type: replace-cross Abstract: While current software agents powered by large language models (LLMs) and agentic reinforcement learning (RL) can boost programmer productivity, their training data (e.
arXiv:2603. 13428v2 Announce Type: replace-cross Abstract: With AI agents increasingly deployed as long-running systems, it becomes essential to autonomously construct and continuously evolve customized software to enable interaction within dynamic environments.
arXiv:2602. 22480v4 Announce Type: replace Abstract: An important emerging application of coding agents is agent harness optimization: the iterative improvement of a target agent by editing and evaluating its code.
SWE‑Nexus is a fully automated pipeline that builds repository‑level implementation tasks by analyzing test execution. It uses four construction agents to select tests, trace production function bodies, and generate behavioral specifications, retaining scopes only when all expected scoring points pass and masking the traced bodies triggers failures. The benchmark covers 600 tasks from 105 repositories in six languages, and the best configuration resolves 37.8% of tasks in a single attempt, demonstrating the system’s ability to construct tasks from validated behavior beyond individual code changes.