ScientistTwo

I want to evaluate and, if it fits, integrate a technique from recent AI research into my project.

## Technique: ScientistTwo
Google's autonomous research agent outperforms human papers on 86 tasks by 25%

ScientistTwo: a 5-stage autonomous research pipeline (Hypothesis Generation → Experimental Design → Code & Execution → Results Analysis → Paper Writing) that generates papers beating human NeurIPS submissions by 25% across 86 ML tasks. Key advantage: exhaustive ablations and 3-round reviewer-rubric self-critique — steps humans routinely skip. Cost: ~$800/paper. Runtime: ~32 hours. 53 parallel agents across stages. Directly implementable for autonomous ML R&D, A/B harnesses, and reproducibility pipelines.

What the reference demo shows: 4-sub-tab Gradio demo — (1) Architecture: 5-stage pipeline with agent counts, inputs/outputs, KPI tiles; (2) Benchmarks: 86-task results by category + quality dimension breakdown vs human baseline; (3) Research Demo: interactive trace of 4 simulated research tasks through the full pipeline; (4) Implementation Patterns: 3 drop-in patterns (hypothesis loop, parallel ablation agent, self-critiquing paper writer).

## Reference implementation (Vector Ridge Labs, POC-044)
- Live demo: https://osoman2-gradio-pocs.hf.space/?poc=POC-044
- Source code: https://huggingface.co/spaces/osoman2/Gradio_pocs/tree/main/scientist_two
Original source: AlphaSignal Signal · 2026-09-29 · 1,820 likes

## My project
<Describe your stack, the problem you want to solve and where this technique would sit. Example: "Python FastAPI RAG service over 50k PDFs, Postgres + pgvector, OpenAI embeddings, p95 latency budget 800 ms.">

## What I need from you
1. Read the reference source code and explain in at most 5 bullets how the technique works and what it would change compared to my current approach.
2. Decide whether it fits my case. If it does not, say why and stop there.
3. Propose the smallest viable integration: files or modules to touch, new dependencies, configuration.
4. Implement it behind a flag or as a separate function so it can be compared side by side with what I have today.
5. Add an evaluation script that measures task success rate, number of steps and token cost per task, before and after, on my own data.
6. List the risks (latency, cost, failure modes) and what to monitor once it is in production.

Constraints: keep my existing stack and conventions, avoid heavy dependencies unless the gain is clear, and reference the file in the demo source whenever you reuse its logic.