AutoR

🌟 Showcase

AutoR already has a full example run used throughout the repository: runs/20260330_101222.

That run produced:

• a compiled paper PDF: example_paper.pdf
• executable research code
• machine-readable datasets and result files
• real figures used in the paper
• review and dissemination materials

Highlighted outcomes from that run:

• AGSNv2 reached 36.21 ± 1.08 on Actor
• the system produced a full NeurIPS-style paper package
• the final run preserved the full human-in-the-loop approval trail

Terminal Experience

AutoR is designed for terminal-first execution, but the interaction layer is not limited to raw logs and plain prompts. The current UI supports banner-style startup, colored stage panels, parsed Claude event streams, wrapped markdown summaries, and a menu-driven approval loop suitable for demos and recordings.

Example Figures

Paper Preview

Human-in-the-Loop in Practice

The example run is interesting not because the AI was left alone, but because the human intervened at critical moments:

• Stage 02 narrowed the project to a single core claim.
• Stage 04 pushed the system to download real datasets and run actual pre-checks.
• Stage 05 forced experimentation to continue until real benchmark results were obtained.
• Stage 06 redirected the story away from leaderboard-only framing toward mechanism-driven analysis.

That is the intended shape of AutoR: AI handles execution load; humans steer the research when direction actually matters.

🚀 Quick Start

Prerequisites

• Python 3.10+
• Claude CLI available on PATH for real runs
• Local TeX tools are helpful for Stage 07, but not required for smoke tests

Start a new run

python main.py

Start with an explicit goal

python main.py --goal "Your research goal here"

Run a local smoke test without Claude

python main.py --fake-operator --goal "Smoke test"

Choose a Claude model

python main.py --model sonnet
python main.py --model opus

Choose a writing venue profile

python main.py --venue neurips_2025
python main.py --venue nature
python main.py --venue jmlr

If --venue is omitted, AutoR defaults to neurips_2025.

Resume or redo work inside the same run

python main.py --resume-run latest
python main.py --resume-run 20260329_210252 --redo-stage 03

Valid stage identifiers include 03, 3, and 03_study_design.

⚙️ How It Works

AutoR uses a fixed 8-stage pipeline:

1. 01_literature_survey
2. 02_hypothesis_generation
3. 03_study_design
4. 04_implementation
5. 05_experimentation
6. 06_analysis
7. 07_writing
8. 08_dissemination

flowchart TD
    A[Start or resume run] --> S1[01 Literature Survey]
    S1 --> H1{Human approval}
    H1 -- Refine --> S1
    H1 -- Approve --> S2[02 Hypothesis Generation]
    H1 -- Abort --> X[Abort]

    S2 --> H2{Human approval}
    H2 -- Refine --> S2
    H2 -- Approve --> S3[03 Study Design]
    H2 -- Abort --> X

    S3 --> H3{Human approval}
    H3 -- Refine --> S3
    H3 -- Approve --> S4[04 Implementation]
    H3 -- Abort --> X

    S4 --> H4{Human approval}
    H4 -- Refine --> S4
    H4 -- Approve --> S5[05 Experimentation]
    H4 -- Abort --> X

    S5 --> H5{Human approval}
    H5 -- Refine --> S5
    H5 -- Approve --> S6[06 Analysis]
    H5 -- Abort --> X

    S6 --> H6{Human approval}
    H6 -- Refine --> S6
    H6 -- Approve --> S7[07 Writing]
    H6 -- Abort --> X

    S7 --> H7{Human approval}
    H7 -- Refine --> S7
    H7 -- Approve --> S8[08 Dissemination]
    H7 -- Abort --> X

    S8 --> H8{Human approval}
    H8 -- Refine --> S8
    H8 -- Approve --> Z[Run complete]
    H8 -- Abort --> X

Stage Attempt Loop

flowchart TD
    A[Build prompt from template + goal + memory + optional feedback] --> B[Start or resume stage session]
    B --> C[Claude writes draft stage summary]
    C --> D[Validate markdown and required artifacts]
    D --> E{Valid?}
    E -- No --> F[Repair, normalize, or rerun current stage]
    F --> A
    E -- Yes --> G[Promote draft to final stage summary]
    G --> H{Human choice}
    H -- 1 or 2 or 3 --> I[Continue current stage conversation with AI refinement]
    I --> A
    H -- 4 --> J[Continue current stage conversation with custom feedback]
    J --> A
    H -- 5 --> K[Append approved summary to memory.md]
    K --> L[Continue to next stage]
    H -- 6 --> X[Abort]

Approval semantics

• 1 / 2 / 3: continue the same stage conversation using one of the AI's refinement suggestions
• 4: continue the same stage conversation with custom user feedback
• 5: approve and contin