paper-autoraters skillA
paper-autoraters is agent-read markdown (skill) from ar9av/paperorchestra: Run the four paper-quality autoraters from PaperOrchestra (arXiv:2604.05018, App. F.3) — Citation F1 (P0/P1 partition + Precision/Recall/F1), Literature Review Quality (6-axis 0-100 with anti-inflation rules), SxS Overall Paper Quality (side-by-side), and SxS Literature Review Quality (side-by-side). TRIGGER when the user asks to "score this paper draft", "evaluate against the benchmark", "compare two papers", or "run the autoraters"..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# Paper Autoraters (App. F.3)
Faithful implementation of the four LLM-as-judge autoraters used in
PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §5 and App. F.3).
These are the metrics the paper uses to demonstrate that PaperOrchestra
beats single-agent and AI-Scientist-v2 baselines. Use them to:
1. Score a generated paper against a ground-truth paper.
2. Compare two paper-writing pipelines side-by-side.
3. Validate your own host-agent execution of the paper-orchestra pipeline.
## The four autoraters
| Autorater | What it does | Inputs | Output |
|---|---|---|---|
| **Citation F1 — P0/P1 partition** | Partitions reference list into P0 (must-cite) and P1 (good-to-cite) given the paper text | one paper text + its references list | JSON `{ref_num: "P0"\|"P1"}` |
| **Literature Review Quality** | 6-axis 0-100 score for Intro+Related Work, with anti-inflation hard caps | one paper PDF/text + reference avg citation count | JSON with `axis_scores`, `penalties`, `summary`, `overall_score` |
| **SxS Overall Paper Quality** | Holistic side-by-side preference judgment | two papers (PDF or text) | JSON with `winner` ∈ {paper_1, paper_2, tie} |
…Read the whole file at its exact version.
How to install
mdr add ar9av/paperorchestra/paper-autoraters@git:20260409.c2de31bmdr add ar9av/paperorchestra/paper-autoraters@sha256:6f0f5604aa96d3b4Pin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_bt4cv27ue7wiqpay)
1 badge views in 30 days
Versions
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (6595 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
ar9av/paperorchestra · 664 stars · license NOASSERTION · pushed 2026-09-21 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_bt4cv27ue7wiqpay GET https://markdownregistry.com/api/v1/resolve?ref=ar9av/paperorchestra/paper-autoraters GET https://markdownregistry.com/api/v1/blob/6f0f5604aa96d3b4623276d74025a9535f27d6f43d1f56874f47571cd990b6cd
Your agent does the legwork. You hear about the deals worth your word. Hand yours the standing instructions at modelranch.com and it joins the network that reads files like this one.