AI-Researcher Driven Scientific Breakthroughs

# Import necessary libraries
import json
import torch
from ai_researcher import CycleReviewer

# Initialize CycleReviewer with the 8B parameter model (for faster inference)
# You can choose "8B", "70B", or "123B" depending on available computational resources
reviewer = CycleReviewer(model_size="8B")

# Load a paper from a JSON file
with open('generated_paper.json', 'r', encoding='utf-8') as f:
    papers = json.load(f)

# Generate the review with multiple simulated reviewers
review_result = reviewer.evaluate([paper['latex'] for paper in papers])

# Display the Meta Review which synthesizes multiple reviewer opinions
print("META REVIEW:")
print("=" * 50)
print(review_result[0]['meta_review'])

# Display individual reviewer comments
print("\nREVIEWER COMMENTS:")
print("=" * 50)
for i, review in enumerate(review_result[0]['reviews']):
    print(f"Reviewer {i+1} (Score: {review['rating']}/10):")
    print(f"Strengths: {review['strengths'][:100]}...")
    print(f"Weaknesses: {review['weaknesses'][:100]}...")
    print()

# Import necessary libraries
import json
import torch
from ai_researcher import DeepReviewer

# Initialize DeepReviewer with the 14B parameter model
# You can choose "7B" or "14B" depending on available computational resources
reviewer = DeepReviewer(model_size="14B")

# Load a paper from a JSON file
with open('generated_paper.json', 'r', encoding='utf-8') as f:
    papers = json.load(f)

# Generate a deep review in Standard Mode with 4 reviewers
# DeepReviewer includes literature queries, multi-reviewer discussion, weakness verification
standard_review = reviewer.evaluate(
    paper_context=[paper['latex'] for paper in papers],
    mode="Standard Mode",
    reviewer_num=4
)

# Access different stages of the DeepReviewer process
print("NOVELTY VERIFICATION:")
print("=" * 50)
print(standard_review[0]['novelty_verification'][:200] + "...")

print("\nMULTI-DIMENSIONAL REVIEW:")
print("=" * 50)
print(standard_review[0]['multi_dimensional_review'][:200] + "...")

print("\nRELIABILITY VERIFICATION:")
print("=" * 50)
print(standard_review[0]['reliability_verification'][:200] + "...")

print("\nFINAL META REVIEW:")
print("=" * 50)
print(standard_review[0]['meta_review'])

# Import necessary libraries
from ai_researcher import CycleResearcher, ExperimentRunner
from ai_researcher.utils import analyze_results, plot_results
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# Initialize the researcher and experiment runner
researcher = CycleResearcher(model_size="72B")
runner = ExperimentRunner()

# Generate research paper with experiment design
paper_with_experiments = researcher.generate_research_paper(
    topic="Neural Network Optimization Techniques",
    include_experiments=True,
    domain="machine_learning"
)

# Extract experiment specifications from paper
experiment_specs = researcher.extract_experiment_specs(paper_with_experiments)

# Execute real-world experiments
print("Running experiments...")
experiment_results = []

for i, experiment in enumerate(experiment_specs):
    print(f"Experiment {i+1}/{len(experiment_specs)}: {experiment['name']}")
    # Execute the actual experiment
    result = runner.run_experiment(
        experiment_type=experiment['type'],
        parameters=experiment['parameters'],
        dataset=experiment['dataset'],
        metrics=experiment['metrics']
    )
    experiment_results.append(result)

# Statistical analysis of experimental results
analysis = analyze_results(experiment_results)
print("\nStatistical Analysis:")
print(analysis['summary'])

# Create visualization of results
figures = plot_results(experiment_results, plot_type='comparison')

# Update paper with real experimental results
final_paper = researcher.update_paper_with_results(
    paper_with_experiments,
    experiment_results,
    analysis
)

print("\nResearch Conclusions:")
print(final_paper['conclusion'])