The paper introduces HMOS‑Migrator, an agentic pipeline that converts Android or iOS applications into HarmonyOS NEXT ArkTS projects and validates them by replaying recorded user interactions on a real device. It evaluates migration quality using seven weighted metrics that rely solely on the source application's behavior and generated artifacts, avoiding reference implementations. Across twelve trials on six open‑source apps, the pipeline achieves high structural completeness (83.3–100%) but shows lower on‑device behavior (4.3–63.1%) and UI fidelity (21.1–63.2%) scores, with gradual performance improvements over time.
arXiv:2609.22000v1 Announce Type: new Abstract: Computer-use agents (CUAs) have advanced along two separate lines: graphical interaction and software development through code and the command line. Re...
Litmus is a zero-label system that automatically designs evaluation and monitoring metrics for AI pipelines by extracting evaluation intent from source code and targeted interrogation. It identifies what must be measured and why, then generates a justified, per-stage metric portfolio. In experiments on three real code-defined pipelines, Litmus outperforms AutoMetrics and DynamicRubric baselines in concern coverage, stage span, redundancy, and validity against per-row quality labels, especially on scientific QA.
arXiv:2608.29128v1 Announce Type: new Abstract: Tool-using agents are commonly evaluated by a single bit: whether an end-to-end workflow completed. This metric fails to distinguish failures that matt...
Large language models are deployed as long-horizon agents that compose diverse tools across MCP services into executable workflows. Existing MCP environment-scaling methods are data-centric: they firs...
arXiv:2607. 13465v1 Announce Type: cross Abstract: LLM-based agents have rapidly improved at operating individual digital environments such as mobile applications, desktop systems, and smart homes.
ML code should run where researchers choose, not where it was first written. Today, machine learning code mainly lives in two ecosystems, PyTorch on NVIDIA GPUs and JAX on Google TPUs. Migrating repos...
arXiv:2607. 11245v1 Announce Type: cross Abstract: To reduce the substantial engineering effort required to test the corresponding applications from Android to OpenHarmony, migrating existing GUI test cases has become a critical problem.
arXiv:2607. 11245v2 Announce Type: replace-cross Abstract: To reduce the substantial engineering effort required to test the corresponding applications from Android to OpenHarmony, migrating existing GUI test cases has become a critical problem.
arXiv:2607. 20527v1 Announce Type: new Abstract: Agentic LLM systems such as OpenScholar and PaperQA2 read the scientific literature and return cited answers, and both they and their benchmarks already check whether those citations hold, with a fixed attribution model or human graders.
arXiv:2608. 09273v1 Announce Type: new Abstract: LLMs have demonstrated strong capabilities in code generation and automated program repair, but migrating an entire repository rarely produces a runnable application because long-horizon translation challenges LLM-based agents' ability to maintain repository-level migration objectives.
arXiv:2611.08472v1 Announce Type: cross Abstract: ML code should run where researchers choose, not where it was first written. Today, machine learning code mainly lives in two ecosystems, PyTorch on...
VeriHarness is a method that enhances verification for large language model agents tackling long‑horizon tasks without needing reference answers at test time. It transforms the base LLM into an agentic verifier by providing a workspace, evidence tools, and reusable verification skills, using disagreement resolution and consensus challenge to evaluate competing claims. Across five benchmarks and two frontier models, VeriHarness outperforms baselines, achieving significant performance gains and demonstrating self‑improvement of verification skills from failure feedback.