MOOSE-Star-R1D-7B Model Card

Overview

MOOSE-Star-R1D-7B (referred to as MS-7B in the paper) is a 7B parameter multi-task language model fine-tuned for both inspiration retrieval and hypothesis composition in scientific discovery workflows. It matches the IR performance of the single-task model (MOOSE-Star-IR-R1D-7B) while significantly outperforming the single-task HC model (MOOSE-Star-HC-R1D-7B), all in a single unified model.

• Paper: MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier (arXiv:2603.03756)
• Base Model: DeepSeek-R1-Distill-Qwen-7B
• License: Apache 2.0
• Code: ZonglinY/MOOSE-Star

Model Description

Parameter	Value
Base Model	DeepSeek-R1-Distill-Qwen-7B
Training Method	Full-parameter SFT (ZeRO-3)
Training Data	TOMATO-Star-SFT-Data-R1D-32B: IR split (150,218 samples) + HC split with 1x bounded (114,548 samples)
Chat Template	deepseekr1
Cutoff Length	16384
Learning Rate	1e-5
Epochs	1
Batch Size	128

Task 1: Inspiration Retrieval (IR)

The model selects the most relevant cross-paper inspiration from 15 candidates (A-O) that includes 1 correct inspiration and 14 hard negatives.

IR Prompt Format (Simplified Overview)

The full prompt template is constructed via instruction_prompts() in the code examples below. The general structure is:

[Task instruction preamble]

## Context

**Research Question:**
{research_question}

**Background Survey (existing methods for THIS task):**
{background_survey}

**Previous Hypothesis (if any):**
{previous_hypothesis_or_none}

## Candidate Inspiration Papers

### Candidate [A]
**Title:** {title_A}
**Abstract:** {abstract_A}

... (15 candidates total, A through O)

## Output Format

<think>
[reasoning process]
</think>

**Selected ID starts:** [X] **Selected ID ends**

**Selection Reason starts:** [reason] **Selection Reason ends**

IR Usage

Prerequisites: Clone the MOOSE-Star repo for prompt templates and inference utilities:

git clone https://github.com/ZonglinY/MOOSE-Star.git && cd MOOSE-Star
# See requirements.txt for full dependencies; at minimum: pip install transformers torch

Option A: SGLang Deployment (Recommended)

# SGLang requires a separate environment; see https://github.com/sgl-project/sglang for installation
# Start the server
python -m sglang.launch_server --model-path ZonglinY/MOOSE-Star-R1D-7B --port 1235

import sys
sys.path.insert(0, "./Inference")
from ir_probability_extractor import IRProbabilityExtractor

extractor = IRProbabilityExtractor(base_urls=["http://localhost:1235/v1"])
result = extractor.get_selection_probabilities(
    research_question="Your research question",
    background_survey="Your background survey",
    candidates=[
        {"title": "Candidate A title", "abstract": "Candidate A abstract"},
        {"title": "Candidate B title", "abstract": "Candidate B abstract"},
        # ... up to 15 candidates (labeled A-O)
    ],
)
print(f"Selected: [{result.selected_label}]")
print(f"Probabilities: {result.probabilities}")

Option B: Direct HuggingFace Inference

import sys
sys.path.insert(0, "./utils")
from prompt_store import instruction_prompts
from transformers import AutoModelForCausalLM, AutoTokenizer
import re

model_name = "ZonglinY/MOOSE-Star-R1D-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto", device_map="auto")

p = instruction_prompts("inspiration_retrieval_with_reasoning_with_alphabetical_candidates")

candidates = [{"title": "...", "abstract": "..."}, ...]
candidates_text = "".join(
    f"### Candidate [{chr(ord('A') + i)}]\n**Title:** {c['title']}\n**Abstract:** {c['abstract']}\n\n"
    for i, c in enumerate(candidates)
)

research_question = "Your research question"
background_survey = "Your background survey"
prompt = (p[0] + research_question
        + p[1] + background_survey
        + p[2] + "No previous hypothesis."
        + p[3] + candidates_text
        + p[4])

messages = [{"role": "user", "content": prompt}]
formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
formatted += "<\uff5cAssistant\uff5c>"

inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.6, top_p=0.9, do_sample=True)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)

match = re.search(r"\*\*Selected ID starts:\*\*\s*\[(\w)\]\s*\*\*Selected ID ends\*\*", response)
if match:
    print(f"Selected: [{match.group(1)}]")

Task 2: Hypothesis Composition (HC)

The model generates delta hypotheses from inspiration papers. Given a research question, background survey, and new inspiration paper, it outputs structured hypothesis components.

HC Prompt Format (Simplified Overview)

The full prompt template is constructed via instruction_prompts() in the code examples below. The general structure is:

[Task instruction preamble]

## Information Provided

**Research Question**:
{research_question}

**Background Survey**:
{background_survey}

**Previous Hypothesis**:
{previous_hypothesis_or_none}

**New Inspiration Paper Title**:
{inspiration_title}

**New Inspiration Paper Abstract**:
{inspiration_abstract}

## Your Response

<think>
[reasoning process]
</think>

Inspiration: [Key concept]
- Motivation (WHY): [Why this addresses a gap]
- Mechanism (HOW IT WORKS): [How the concept works]
- Methodology (HOW IT'S INTEGRATED): [Implementation steps]

HC Usage

import sys
sys.path.insert(0, "./utils")
from prompt_store import instruction_prompts
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZonglinY/MOOSE-Star-R1D-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, dtype="auto", device_map="auto")

p = instruction_prompts("prepare_HC_sft_data_to_go_comprehensive_v2_delta")

research_question = "Your research question here"
background_survey = "Your background survey here"
inspiration_title = "Inspiration paper title"
inspiration_abstract = "Inspiration paper abstract"

prompt = (p[0] + research_question
        + p[1] + background_survey
        + p[2] + "No previous hypothesis."
        + p[3] + inspiration_title
        + p[4] + inspiration_abstract
        + p[5])

messages = [{"role": "user", "content": prompt}]
formatted = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
formatted += "<\uff5cAssistant\uff5c>"

inputs = tokenizer(formatted, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=8192, temperature=0.6, top_p=0.9, do_sample=True)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

Evaluation Results

Inspiration Retrieval (Table 1)

Model	Accuracy
Random Selection	6.70%
R1-Distilled-Qwen-7B (base)	28.42%
MS-IR-7B (single-task)	54.37%
MS-7B (this model)	54.34%

Hypothesis Composition - Normal (Table 2)

Rubric-based evaluation with ground-truth inspirations (Judge: GPT-4o):

Model	Total	Mot	Mec	Met	Length
R1-Distilled-Qwen-7B (base)	4.05	1.96	1.30	0.80	231.02
MS-HC-7B (single-task)	4.68	2.13	1.46	1.09	204.12
MS-HC-7B w/ 1x bounded	4.74	2.16	1.48	1.10	203.84
MS-7B (this model)	5.02	2.22	1.59	1.20	208.98

Hypothesis Composition - Bounded (Table 3)

Performance under varying levels of inspiration noise (Judge: GPT-4o):

Model	Easy Total	Medium Total	Hard Total
R1-Distilled-Qwen-7B (base)	2.72	2.27	2.00
MS-HC-7B w/ 2x bounded	3.18	2.74	2.56
MS-7B (this model)	3.37	2.86	2.78

Key Findings

• IR performance preserved: Multi-task training maintains full IR accuracy (54.34% vs 54.37% single-task)
• HC significantly improved: Multi-task HC outperforms all single-task variants, including those with bounded composition augmentation
• Robust under noise: Largest improvements on Hard bounded composition, suggesting IR reasoning skills transfer to HC

Citation

@article{yang2025moosestar,
  title={MOOSE-Star: Unlocking Tractable Training for Scientific Discovery by Breaking the Complexity Barrier},
  author={Yang, Zonglin and Bing, Lidong},
  journal={arXiv preprint arXiv:2603.03756},
  year={2026}
}