FirstPass: Grounding AI Scientific Judgment in Multi-Round Editorial Outcomes
Official model weights for the ICML 2026 AI4Science Workshop papers:
- Research Track: "FirstPass: Grounding AI Scientific Judgment in Multi-Round Editorial Outcomes"
- Dataset Competition Track: "FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes"
Authors: Prabhjot Singh, Somnath Luitel, Manmeet Singh, Josh Durkee
🗂️ What's in this repo
| Folder | Task | Description |
|---|---|---|
cls_revision_prediction/ |
Classification | Predicts Standard (2-round) vs Extended (3+ round) editorial outcome |
sft_review_generation/ |
Generation | Generates full scientific peer reviews |
checkpoints_v4/cls_adapter/final/ |
Classification (alt path) | Same cls adapter via checkpoints folder |
checkpoints_v4/sft_adapter/final/ |
Generation (alt path) | Same sft adapter via checkpoints folder |
Use cls_revision_prediction/ and sft_review_generation/ directly — those are the clean final adapters.
📊 Key Results
| Task | Metric | Score |
|---|---|---|
| Revision-Cycle Prediction | Accuracy | 80.5% |
| Revision-Cycle Prediction | F1-macro | 78.2% |
| Review Generation | ROUGE-L | 0.154 |
| Review Generation | Avg. length | 1,187 words |
The masking finding: without response-only loss masking → 62.0% accuracy (below majority baseline). With masking → 80.5%. For long-input/short-output classification, masking is an architectural prerequisite, not an optimization trick.
🚀 Quick Start
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model = "Qwen/Qwen2.5-7B-Instruct" # or whichever base was used
adapter_path = "prabhjotschugh/FirstPass-Models/cls_revision_prediction"
tokenizer = AutoTokenizer.from_pretrained(adapter_path)
model = AutoModelForCausalLM.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, adapter_path)
📥 Dataset
🤗 firstpass-peer-review — 3,668 multi-round peer-review dialogues from Nature Communications across 5 domains (biology, chemistry, neuroscience, physics, earth science).