arXiv:2606. 28436v1 Announce Type: cross Abstract: Program verifiers play a central role in training coding agents, including selecting trajectories for supervised fine-tuning (SFT) and providing rewards for reinforcement learning (RL).
arXiv:2601. 22859v3 Announce Type: replace-cross Abstract: The evolution of Large Language Model (LLM) agents for software engineering (SWE) is constrained by the scarcity of verifiable datasets, a bottleneck stemming from the complexity of constructing executable environments across diverse languages.
arXiv:2609.05576v1 Announce Type: new Abstract: The paradigm of LLMs has rapidly shifted from passive language interfaces to autonomous Claw-like agents that execute long-horizon tasks across statefu...
arXiv:2607. 21557v1 Announce Type: new Abstract: Modern AI agents rely on elaborate inference harnesses such as Claude Code, Codex, and OpenClaw to drive multi-turn reasoning, tool use, and access to external systems.
arXiv:2609.38269v1 Announce Type: cross Abstract: Coding agents are increasingly asked to build software rather than patch it, yet benchmarks for from-scratch repository construction are mostly limit...
ProgramGym introduces a scalable framework for training long-horizon program reproduction agents, comprising Program-Env and Program-Agent components. Program-Env builds verifiable training environments from repositories via a feedback loop that repairs sandbox builds and improves black-box tests, yielding 1,648 environments across C, C++, Go, and Rust. Program-Agent employs a role-isolated workflow of analysis, implementation, and review with revision management, boosting performance on ProgramBench and raising Qwen3.8-27B’s accuracy from 34.87% to 42.84% and surpassing flagship models with fine-tuning.
arXiv:2606. 05548v1 Announce Type: cross Abstract: The rapid proliferation of Agent Development Kits (ADKs), SDK-level frameworks for building LLM-powered autonomous agents, has outpaced any empirical understanding of how framework choice affects agent performance.
arXiv:2607. 27146v1 Announce Type: cross Abstract: Coding agents have made substantial progress on software engineering tasks that modify existing codebases, including bug fixing and feature implementation.
arXiv:2610.16867v1 Announce Type: cross Abstract: Reliable agentic tool use requires training data that pair diverse multi-step workflows with executable evidence that the requested outcomes were ach...
arXiv:2605. 25160v2 Announce Type: replace Abstract: GUI agents powered by large language models are advancing rapidly, creating urgent needs for evaluation and training based on realistic environments.
arXiv:2606. 16038v1 Announce Type: cross Abstract: The path toward autonomous software engineering is currently bottlenecked by a severe deficit of diverse, large-scale trajectory data.
OpenAI4S is an open‑source scientific research agent that treats code as action and science as sessions, combining a persistent computing runtime with structured session management. It uses tool calls for orchestration, executes code cells in persistent Python and R kernels, and records an append‑only Action Ledger, per‑cell execution logs, versioned artifacts, environment snapshots, and workspace checkpoints to preserve provenance and enable session recovery, branching, and extension. Evaluated on 36 research scenarios—including retrosynthesis, molecular dynamics, and protein design—OpenAI4S achieved a higher overall score (7.83) than a general‑purpose coding harness, especially on long‑horizon, computation‑intensive workflows, though reproducibility remains an open challenge. whyItMatters":"The system demonstrates that persistent execution coupled with session‑level provenance can enhance the reliability of AI‑assisted scientific workflows, as evidenced by its superior performance across diverse research scenarios."