Deepscientist Eval

---
name: deepscientist-eval
description: Evaluates an autonomous AI research system's ability to progressively advance state-of-the-art methods across three distinct AI tasks: agent failure attribution, LLM inference acceleration, and AI text detection. It also assesses the scientific quality of the AI-generated research papers through automated and human peer review. Use when the user wants to benchmark on Who&When benchmark, MBPP, AI Text Detection dataset, or asks about evaluating this task. Reports Accuracy, AUROC.
metadata:
  skill_kind: dataset_eval
  source_arxiv: 2509.26603
  bibtex_key: [REDACTED]
  confidence: high
---

# deepscientist-eval

> DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively — Weng et al. (2025) (arXiv:2509.26603, 2025)

## What this evaluates

Evaluates an autonomous AI research system's ability to progressively advance state-of-the-art methods across three distinct AI tasks: agent failure attribution, LLM inference acceleration, and AI text detection. It also assesses the scientific quality of the AI-generated research papers through automated and human peer review.

## Datasets

- **Who&When benchmark** — total ?; splits: test (-1)
- **MBPP** — total ?; splits: test (-1)
- **AI Text Detection dataset** — total ?; splits: test (-1)

## Metrics

- `Accuracy` **(primary)** — range: percent
  - Percentage of correctly attributed agent failures in handcraft and algorithm-generated settings.
- `Tokens/second` — range: other
  - Throughput metric measuring the number of tokens generated per second during LLM inference.
- `AUROC` **(primary)** — range: [0, 1]
  - Area under the Receiver Operating Characteristic curve for binary classification of human vs. AI-generated text.
- `Latency` — range: other
  - Inference time measured in milliseconds.
- `Rating` — range: [0, 5]
  - Average score (1-5 scale) assigned by reviewers across Soundness, Presentation, Contribution, and overall Rating dimensions.

## Input / output format

**Input**: Baseline SOTA method code and execution logs, task descriptions, and human supervision feedback for task advancement; generated research papers for quality evaluation.

**Output**: New method code/algorithm, performance metrics (Accuracy, Tokens/second, AUROC, Latency), and full research paper text.

## Scoring recipe

```python
For tasks:
  acc = (correct_predictions / total_samples) * 100
  auroc = compute_roc_auc(y_true, y_pred)
  tokens_sec = total_tokens / total_inference_time
  latency = mean_inference_time_ms
For papers:
  rating = mean(review_scores['Soundness'], review_scores['Presentation'], review_scores['Contribution'], review_scores['Rating'])
```

## Common pitfalls

- The system's low scientific progress rate (1-3%) reflects expected exploration behavior, not system failure.
- Distinguishing genuine scientific discoveries from engineering optimizations requires careful protocol adherence.
- Human evaluation relies on a small committee (3 reviewers), limiting statistical generalizability.

## Evidence (verbatim from paper)

> As shown in Figure [1] and [3](d), this entire discovery trajectory demonstrates DeepScientist’s ability for advancing frontier-pushing scientific findings progressively, establishing a new SOTA with a 7.9% higher AUROC while also doubling the inference speed.

## Citation

```bibtex
@misc{weng2025deepscientist,
  title={DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively},
  author={Weng et al. (2025)},
  year={2025},
  note={arXiv:2509.26603}
}
```

- arXiv: 2509.26603