Agentic Aggregation for Parallel Scaling of Long-Horizon Agentic Tasks

This project is a reference implementation for a research paper focused on inference-time scaling (test-time scaling) for agents. While the problem it solves—how to combine multiple parallel 'deep research' attempts into a single high-quality answer—is at the absolute frontier of AI development, the project itself has no moat. It is currently a 0-star repository that serves as a proof-of-concept for an academic insight. Frontier labs like OpenAI (with o1 and Deep Research), Google (Gemini 2.0 Thinking), and Perplexity are already implementing proprietary versions of these scaling laws. The defensibility is low because the technique, once published, can be trivially integrated into existing agentic frameworks or platform-level reasoning engines. The 4 forks in 4 days suggest some initial interest from the research community, but this is a 'feature' or an 'insight' rather than a sustainable software product. It is highly likely to be superseded by platform-native scaling solutions within months.