Toward Autonomous Long-Horizon Engineering for ML Research
- Published
- Source
- arXiv
- Paper number
- 144
- Field
- Agents / Research
- arXiv ID
- 2604.13018
Key points
- Current autonomous AI systems struggle with long-horizon tasks that take hours to days and require sustained, coherent progress across multiple stages.
- Machine learning research papers are often underspecified, forcing agents to infer missing implementation details and manage complex environment setup.
- Interpreting delayed and noisy experimental feedback and maintaining project state continuity across iteration cycles pose major challenges.
- AiScientist implements thin control over thick state, where the top-level Orchestrator uses concise summaries and detailed project information lives in a permission-scoped shared workspace.
- It uses a File-as-Bus protocol for artifact-mediated coordination and leverages the structured organization of the shared workspace to preserve persistent state and cumulative progress.
- Its hierarchical orchestration model lets the Orchestrator delegate tasks to Tier-1 specialist agents exposed as callable tools, such as paper-understanding, implementation, and experimentation experts.
Paper links
External research summaries. These are not HDATF publications or measured product results.