R1 Arxiv

L3
ModelContextProtocolPlaywrightWeb Search

Search arXiv for R1 model research papers, extract technical specifications, analyze methodology sections, compile research findings, and generate comprehensive literature review.

Created by Arvin Xu
2025-08-18
Search AggregationData ExtractionComparative AnalysisContent Submission

Model Ranking

Click on the dots to view the trajectory of each task run
Model
Run Results
Pass@4
Pass^4
Avg Time
Avg Turns
Input Tokens
Output Tokens
Total Tokens
Grok
grok-code-fast-1
1
/4
51.8s
6.3
590,506
4,394
594,900
Claude
claude-opus-4-1
0
/1
--
192.0s
7.0
660,052
835
660,887
Claude
claude-sonnet-4
0
/4
180.1s
15.8
1,051,280
1,796
1,053,076
Claude
claude-sonnet-4-high
0
/4
177.2s
17.3
1,616,502
2,531
1,619,033
Claude
claude-sonnet-4-low
0
/4
145.8s
13.8
1,052,553
2,291
1,054,843
DeepSeek
deepseek-chat
0
/4
202.3s
12.8
720,010
724
720,734
Gemini
gemini-2-5-flash
0
/4
155.0s
14.0
1,865,818
1,769
1,867,595
Gemini
gemini-2-5-pro
0
/4
106.0s
13.3
743,525
3,862
747,387
Z.ai
glm-4-5
0
/4
164.7s
14.8
837,564
1,691
839,254
OpenAI
gpt-4-1
0
/4
44.5s
9.5
637,713
512
638,225
OpenAI
gpt-4-1-mini
0
/4
30.1s
7.3
252,081
508
252,589
OpenAI
gpt-4-1-nano
0
/4
12.2s
4.8
60,019
197
60,216
OpenAI
gpt-5-high
0
/4
397.4s
10.0
624,603
10,646
635,248
OpenAI
gpt-5-low
0
/4
185.3s
10.0
839,908
5,135
845,043
OpenAI
gpt-5-medium
0
/4
157.8s
9.0
423,925
5,581
429,506
OpenAI
gpt-5-mini-high
0
/4
309.9s
23.8
2,895,862
13,894
2,909,756
OpenAI
gpt-5-mini-low
0
/4
41.7s
7.0
504,366
710
505,076
OpenAI
gpt-5-mini-medium
0
/4
187.5s
17.8
2,179,654
4,925
2,184,579
OpenAI
gpt-5-nano-high
0
/4
98.3s
5.5
139,993
18,514
158,506
OpenAI
gpt-5-nano-low
0
/4
17.6s
6.3
55,512
800
56,312
OpenAI
gpt-5-nano-medium
0
/4
65.3s
7.0
220,038
10,739
230,777
OpenAI
gpt-oss-120b
0
/4
25.2s
5.5
172,930
569
173,499
Grok
grok-4
0
/4
356.6s
16.0
998,727
5,495
1,004,222
MoonshotAI
kimi-k2-0711
0
/4
175.2s
6.5
354,040
490
354,530
MoonshotAI
kimi-k2-0905
0
/4
408.1s
14.3
1,341,601
1,673
1,343,274
OpenAI
o3
0
/4
93.8s
9.8
495,624
2,346
497,969
OpenAI
o4-mini
0
/4
382.8s
21.8
1,574,262
6,466
1,580,728
Qwen
qwen-3-coder-plus
0
/4
76.8s
9.0
316,341
457
316,798
Qwen
qwen-3-max
0
/4
306.8s
16.0
1,068,327
627
1,068,954

Instruction

Web Search Task

Use Playwright MCP tools to search for the DeepSeek R1 research paper and extract all the paragraphs of the Conclusion section.

Requirements:

  1. Search for the DeepSeek R1 research paper
  2. Navigate to the paper and find the Conclusion section
  3. Extract ALL the paragraphs of the Conclusion section
  4. Provide the content in Markdown format - no explanations, no additional text

Important Notes:

  • Output ALL the paragraphs of text
  • Do NOT include any explanations, summaries, or additional content
  • The response should contain ONLY the Conclusion section content formatted in Markdown

Expected Output:

All the paragraphs of the Conclusion section from the DeepSeek R1 paper, formatted in Markdown with proper paragraph structure and formatting.



Verify

*.py
Python
#!/usr/bin/env python3
"""
Verification script for Playwright web search task.

Simple verification that checks if the AI agent found the correct Introduction content.
The expected ground truth answer is configured at the top of the file.
"""

import sys
import json
import os
from pathlib import Path
from typing import Dict, Any

# =============================================================================
# CONFIGURATION
# =============================================================================

# Expected ground truth content from content.txt
EXPECTED_CONTENT_FILE = "content.txt"

# =============================================================================
# MCP RESULT PARSING
# =============================================================================


def get_working_directory() -> Path:
    """Get the working directory where messages.json should be."""
    # Priority 1: Use MCP_MESSAGES path if available (most reliable)
    messages_path = os.getenv("MCP_MESSAGES")
    if messages_path and Path(messages_path).exists():
        return Path(messages_path).parent.resolve()

    # Priority 2: Use PLAYWRIGHT_WORK_DIR environment variable
    work_dir = os.getenv("PLAYWRIGHT_WORK_DIR")
    if work_dir:
        work_path = Path(work_dir).resolve()
        if (work_path / "messages.json").exists():
            return work_path

    # Priority 3: Check current directory (fallback)
    current_dir = Path.cwd()
    if (current_dir / "messages.json").exists():
        return current_dir

    # Priority 4: Default fallback
    return Path(".").resolve()


def load_expected_content() -> str:
    """Load the expected content from content.txt"""
    # content.txt is in the same directory as verify.py
    current_file = Path(__file__).resolve()
    content_file = current_file.parent / EXPECTED_CONTENT_FILE

    if not content_file.exists():
        print(f"| {EXPECTED_CONTENT_FILE} not found at: {content_file}")
        return ""

    print(f"| Found {EXPECTED_CONTENT_FILE} at: {content_file}")

    try:
        with open(content_file, "r", encoding="utf-8") as f:
            return f.read().strip()
    except (IOError, UnicodeDecodeError) as e:
        print(f"| Warning: Could not read {content_file}: {e}")
        return ""


def parse_ai_results(work_dir: Path) -> Dict[str, Any]:
    """Parse the AI agent's results from messages.json"""
    messages_file = work_dir / "messages.json"
    if not messages_file.exists():
        return {"success": False, "error": "No messages.json found"}

    try:
        with open(messages_file, "r", encoding="utf-8") as f:
            messages = json.load(f)
    except (json.JSONDecodeError, IOError) as e:
        return {"success": False, "error": f"Failed to read messages.json: {e}"}

    # Look for extracted content in the AI's responses
    found_content = False
    ai_responses = []
    extracted_content = ""

    for message in messages:
        if message.get("role") == "assistant":
            content = str(message.get("content", ""))

            # Handle both string and list content formats
            if isinstance(message.get("content"), list):
                content = " ".join(
                    item.get("text", "") if isinstance(item, dict) else str(item)
                    for item in message.get("content", [])
                )

            ai_responses.append(content)

            # Store the last response as extracted content
            extracted_content = content

    return {
        "success": True,
        "found_content": True,  # Assuming content was found if we have responses
        "ai_responses": ai_responses,
        "extracted_content": extracted_content,
        "total_responses": len(ai_responses),
    }


def compare_content(extracted: str, expected: str) -> Dict[str, Any]:
    """Compare extracted content with expected content"""
    if not expected:
        return {"success": False, "error": "No expected content to compare against"}

    if not extracted:
        return {"success": False, "error": "No extracted content found"}

    # Normalize content for comparison (remove extra whitespace, normalize line breaks)
    extracted_normalized = " ".join(extracted.split())
    expected_normalized = " ".join(expected.split())

    # Direct text comparison - content must be exactly the same
    is_exact_match = extracted_normalized == expected_normalized

    return {
        "success": True,
        "is_exact_match": is_exact_match,
        "extracted_length": len(extracted_normalized),
        "expected_length": len(expected_normalized),
        "extracted_preview": extracted_normalized[:100] + "..." if len(extracted_normalized) > 100 else extracted_normalized,
        "expected_preview": expected_normalized[:100] + "..." if len(expected_normalized) > 100 else expected_normalized
    }


# =============================================================================
# MAIN VERIFICATION
# =============================================================================


def verify_task(work_dir: Path) -> bool:
    """Verify the AI agent found the correct Introduction content"""
    print("| Verifying Playwright Web Search Task - DeepSeek R1 Introduction")
    print("| " + "=" * 70)

    # Load expected content
    print("| Loading expected content...")
    expected_content = load_expected_content()

    if not expected_content:
        print("| Error: Could not load expected content")
        return False

    print(f"| Expected content loaded ({len(expected_content)} characters)")

    # Parse MCP messages
    messages = parse_ai_results(work_dir)

    if not messages["success"]:
        print(f"| Error: Could not parse AI results: {messages.get('error')}")
        return False

    # Extract AI agent response
    extracted_content = messages.get("extracted_content", "")

    if not extracted_content:
        print("| Error: No AI agent response found")
        return False

    print(f"| Extracted content: {len(extracted_content)} characters")

    # Compare content
    print("| Comparing extracted content with expected content...")
    comparison = compare_content(extracted_content, expected_content)

    if not comparison["success"]:
        print(f"| Comparison failed: {comparison.get('error')}")
        return False

    print(f"| Content comparison results:")
    print(f"|   - Extracted length: {comparison['extracted_length']} characters")
    print(f"|   - Expected length: {comparison['expected_length']} characters")
    print(f"|   - Extracted preview: {comparison['extracted_preview']}")
    print(f"|   - Expected preview: {comparison['expected_preview']}")

    if comparison['is_exact_match']:
        print("| Task completed successfully! Content matches exactly.")
        return True
    else:
        print("| Task verification failed. Content does not match exactly.")
        return False


def main():
    """Main verification function"""
    print("| Starting verification...")

    # Get working directory
    work_dir = get_working_directory()
    print(f"| Working directory: {work_dir}")

    # Run verification
    success = verify_task(work_dir)

    if success:
        sys.exit(0)
    else:
        sys.exit(1)


if __name__ == "__main__":
    main()