BZJZ_Material/文档润色流和知识库构建流/claude-scholar/skills/kaggle-learner/references/knowledge/nlp/konwinski-prize-2025.md

Konwinski Prize 2025 - AI GitHub Issue Resolver Competition

Competition URL: https://www.kaggle.com/competitions/konwinski-prize

Official Website: https://kprize.ai

Category: Code Agent / AI Software Engineering

Tags: code-agent, LLM-agent, SWE-bench, GitHub-issues, automated-programming

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Competition Brief

Overview

The Konwinski Prize is a $1M competition founded by Andy Konwinski (co-founder of Databricks) that challenges teams to build an AI system capable of resolving real GitHub issues. The competition uses a contamination-free version of the SWE-bench benchmark with GitHub issues collected after submissions to prevent data leakage.

Prize Structure

• Grand Prize: $1,000,000 for achieving >90% success rate (unclaimed)
• Round 1 First Place: $50,000
• Total Prize Fund: $1,225,000+
• Participation: 616 teams in Round 1

Key Challenge

• Goal: Build an AI agent that can resolve real GitHub issues
• Evaluation: Performed on a contamination-free test set collected after submission
• Success Criterion: >90% issue resolution rate
• Timeline: Round 1 submissions closed July 2025; next round TBD

Round 1 Results (July 2025)

Rank	Participant	Score	Achievement
1st	Eduardo Rocha de Andrade	7.5% (0.058242)	$50,000 prize
2nd	camaro	~6-7%	Public 2nd Place
3rd	Anonymous	~5-6%	Bronze Medal
4th	Anonymous	~5-6%	"Select-Patch-Verify-Test"
5th	Anonymous	~5%	Regex traceback analysis
6th	quan16369 (Team of 2)	0.8%	Gold Medal (3 correct, 2 wrong)

Key Insight: The winning score of 7.5% highlights how extremely difficult real-world GitHub issue resolution is, even for state-of-the-art AI systems.

Technical Constraints

• Open-Weight Models Only: No closed models (GPT-4, Claude, etc.) allowed
• No External API Calls: Must run locally
• Runtime Environment: Limited computing resources
• Test Set: Hidden until evaluation, collected after submission freeze

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Top Solutions Analysis

1st Place: Eduardo Rocha de Andrade (7.5% success)

Approach Summary: Prompt engineering + careful test case generation

Key Techniques:

• Meticulous prompt engineering
• Automated test case generation (Fail-to-Pass tests)
• Careful patch validation
• Conservative submission strategy (only high-confidence fixes)

Why It Won:

• Quality over quantity: Only submitted fixes with highest confidence
• Proper test validation to ensure patches actually work
• Avoided the heavy penalties for wrong fixes

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4th Place: "Select-Patch-Verify-Test" Pipeline

Architecture:

Select → Patch → Verify → Test → Choose

Pipeline Steps:

1. Select: Analyze bug reports + code tree to identify relevant files
2. Patch: Generate candidate patches using LLM
3. Verify: Multi-attempt LLM verification (measure confidence)
4. Test: Generate F2P (Fail-to-Pass) tests
   • Tests must fail on original code
   • Tests must pass after patch application
5. Choose: Rule-based scoring with strict filtering

Key Innovation: The mandatory testing phase was crucial for objective validation.

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5th Place: Regex Traceback Analysis

Key Strategy:

• Use regex to extract traceback information from error messages
• Focus LLM attention on specific error locations
• More targeted patch generation
• Reduced context window usage

Effectiveness: Improved localization of bugs, less hallucination.

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6th Place: Select-Patch-Verify-Choose (quan16369)

Performance:

• Private LB: 0.823% (3 correct, 2 wrong, 115 skipped)
• Public LB: -0.0097% (1 correct, 1 wrong, 69 skipped)

Core Pipeline:

Select → Patch → Verify (Multi-attempt) → Choose (Logic)

Key Techniques:

1. Multi-Attempt Verification for Confidence Assessment

# Verify each patch multiple times
VALIDATION_COPY_COUNT = 3  # or more

# Only trust patches with high consensus
judgments_aggregated = [
    [],                      # Candidate 1: No consensus
    [True, True, True],      # Candidate 2: STRONG SIGNAL
    [],                      # Candidate 3: No consensus
    # ... etc
]

2. Sophisticated Scoring Function

def calculate_patch_score(patch, judgments):
    # Heavy penalty if invalid or no Yes votes
    if not is_valid(patch) or judgments.count(True) == 0:
        return -LARGE_PENALTY

    # Base score = (Yes votes)² × weight
    score = (judgments.count(True) ** 2) * 5.0

    # EXPONENTIAL size penalty - forces concise solutions
    score -= (np.exp(len(patch) / 10) - 1)

    return score

Scoring Criteria:

• ✅ Positive score
• ✅ Top percentile (e.g., top 1%)
• ✅ Significantly outperforms second-best
• ✅ Minimum "Yes" vote threshold
• ❌ Otherwise SKIP for safety

3. Size Penalty Strategy

• Exponential penalty for patch length
• Forces LLM to find minimal, precise solutions
• Prevents unnecessary changes that cause side effects

Why Only 6th Place:

• No objective testing phase (unlike top 5)
• Relied only on LLM self-verification (hallucination risk)
• Missed the importance of F2P tests

---

Common Themes Across Top Solutions

What Worked:

1. Conservative Strategy: Better to skip than be wrong

   • Wrong fixes: Heavy penalty
   • Skips: Small penalty
   • Insight: Quality > Quantity

2. Multi-Attempt Verification

   • Don't trust single LLM judgment
   • Aggregate multiple verification attempts
   • Use consensus as confidence metric

3. Size Penalties

   • Exponential penalty for large patches
   • Forces minimal, targeted fixes
   • Reduces side effects

4. Test Case Generation (Critical for top places)

   • Generate Fail-to-Pass tests
   • Must fail on original code
   • Must pass after patching
   • Objective validation (not subjective LLM judgment)

What Didn't Work:

1. Aggressive Fixing: Trying to fix everything led to more wrong fixes
2. Single Verification: Trusting one LLM judgment caused hallucinations
3. Large Patches: More code = more chance of breaking something
4. No Objective Tests: Pure LLM verification is unreliable

---

Code Templates

Template 1: Select-Patch-Verify-Choose Pipeline

import numpy as np
from typing import List, Tuple

class KonwinskiPrizeAgent:
    def __init__(self, llm_client):
        self.llm = llm_client
        self.VALIDATION_COPY_COUNT = 3
        self.SIZE_PENALTY_WEIGHT = 0.1

    def select_relevant_code(self, issue: str, code_tree: dict) -> List[str]:
        """Select relevant files using LLM analysis"""
        prompt = f"""
        Analyze this GitHub issue and identify relevant files:

        Issue: {issue}

        Code Tree:
        {self._format_code_tree(code_tree)}

        Return a list of relevant files with brief explanations.
        """
        # Multiple selection attempts for diversity
        selections = []
        for _ in range(3):
            selection = self.llm.generate(prompt)
            selections.append(selection)
        return selections

    def generate_patches(self, issue: str, selected_code: str) -> List[str]:
        """Generate multiple candidate patches"""
        prompt = f"""
        GitHub Issue: {issue}

        Relevant Code:
        {selected_code}

        Generate 5 different git diff patches to fix this issue.
        Each patch should be minimal and targeted.
        """
        patches = self.llm.generate(prompt)
        return self._parse_patches(patches)

    def verify_patch(self, issue: str, patch: str) -> List[bool]:
        """Multi-attempt verification for confidence assessment"""
        judgments = []

        for _ in range(self.VALIDATION_COPY_COUNT):
            prompt = f"""
            Issue: {issue}

            Proposed Patch:
            {patch}

            Does this patch correctly fix the issue? Answer Yes or No.
            """
            response = self.llm.generate(prompt)
            is_yes = "yes" in response.lower()
            judgments.append(is_yes)

        return judgments

    def calculate_patch_score(self, patch: str, judgments: List[bool]) -> float:
        """Calculate score with exponential size penalty"""
        # Heavy penalty if invalid or no Yes votes
        if judgments.count(True) == 0:
            return -1000.0

        # Base score = (Yes votes)² × weight
        score = (judgments.count(True) ** 2) * 5.0

        # Exponential size penalty
        score -= (np.exp(len(patch) / 10) - 1)

        return score

    def choose_best_patch(self, patches: List[str], all_judgments: List[List[bool]]) -> str:
        """Choose best patch using scoring function"""
        scored_patches = []

        for patch, judgments in zip(patches, all_judgments):
            score = self.calculate_patch_score(patch, judgments)
            scored_patches.append((patch, score, judgments))

        # Sort by score
        scored_patches.sort(key=lambda x: x[1], reverse=True)

        # Apply strict criteria
        if not scored_patches:
            return None

        best_patch, best_score, best_judgments = scored_patches[0]

        # Must meet all criteria
        if best_score <= 0:
            return None

        if len(scored_patches) > 1:
            second_score = scored_patches[1][1]
            if best_score - second_score < 10:  # Must be significantly better
                return None

        return best_patch

    def solve_issue(self, issue: str, code_tree: dict) -> str:
        """Main pipeline: Select → Patch → Verify → Choose"""
        # Step 1: Select relevant code
        selections = self.select_relevant_code(issue, code_tree)
        selected_code = selections[0]  # Use best selection

        # Step 2: Generate patches
        patches = self.generate_patches(issue, selected_code)

        # Step 3: Verify patches
        all_judgments = []
        for patch in patches:
            judgments = self.verify_patch(issue, patch)
            all_judgments.append(judgments)

        # Step 4: Choose best patch
        best_patch = self.choose_best_patch(patches, all_judgments)

        return best_patch  # Returns None if no patch is good enough

Template 2: With Test Case Generation (Top 5 Approach)

class TestValidatedAgent(KonwinskiPrizeAgent):
    """Enhanced agent with Fail-to-Pass test generation"""

    def generate_f2p_test(self, issue: str, code: str) -> str:
        """Generate a test that fails on original code"""
        prompt = f"""
        GitHub Issue: {issue}

        Original Code:
        {code}

        Generate a unit test that:
        1. FAILS on the current (buggy) code
        2. PASSES when the bug is fixed

        The test should be minimal and focused on the specific bug.
        """
        test_code = self.llm.generate(prompt)
        return test_code

    def validate_patch_with_test(self, patch: str, test_code: str, original_code: str) -> bool:
        """Objective validation: test must fail on original, pass on patched"""
        # Apply patch to get patched code
        patched_code = self._apply_patch(original_code, patch)

        # Run test on original code (should FAIL)
        original_result = self._run_test(test_code, original_code)
        if original_result != "FAIL":
            return False  # Test doesn't fail on buggy code!

        # Run test on patched code (should PASS)
        patched_result = self._run_test(test_code, patched_code)
        if patched_result != "PASS":
            return False  # Test doesn't pass on fixed code!

        return True

    def solve_issue_with_tests(self, issue: str, code_tree: dict) -> str:
        """Pipeline with test validation"""
        # Select + Patch as before
        selections = self.select_relevant_code(issue, code_tree)
        patches = self.generate_patches(issue, selections[0])

        # Generate test
        test_code = self.generate_f2p_test(issue, selections[0])

        # Validate each patch with test
        valid_patches = []
        for patch in patches:
            if self.validate_patch_with_test(patch, test_code, selections[0]):
                valid_patches.append(patch)

        # Use verification to choose among valid patches
        if not valid_patches:
            return None

        # Apply verification logic only to valid patches
        all_judgments = []
        for patch in valid_patches:
            judgments = self.verify_patch(issue, patch)
            all_judgments.append(judgments)

        return self.choose_best_patch(valid_patches, all_judgments)

Template 3: Traceback Analysis (5th Place Approach)

import re

class TracebackAwareAgent(KonwinskiPrizeAgent):
    """Agent that uses regex to extract traceback info"""

    def extract_traceback(self, issue: str) -> dict:
        """Extract traceback information using regex"""
        traceback_patterns = [
            r'File "([^"]+)", line (\d+), in (\w+)',
            r'(\w+Error): (.+)',
            r'Traceback \(most recent call last\):',
        ]

        traceback_info = {
            'files': [],
            'lines': [],
            'functions': [],
            'error_types': [],
            'error_messages': [],
        }

        for pattern in traceback_patterns:
            matches = re.findall(pattern, issue)
            # Parse matches into traceback_info

        return traceback_info

    def select_with_traceback(self, issue: str, code_tree: dict) -> List[str]:
        """Use traceback to prioritize files"""
        traceback_info = self.extract_traceback(issue)

        # Prioritize files mentioned in traceback
        prioritized_files = []
        for file_path in traceback_info['files']:
            if file_path in code_tree:
                prioritized_files.append(file_path)

        # Add context from nearby files
        for file_path in prioritized_files:
            # Add sibling files, parent directories, etc.

        return prioritized_files

    def generate_targeted_patch(self, issue: str, traceback_info: dict, code: str) -> str:
        """Generate patch focused on traceback location"""
        prompt = f"""
        Issue: {issue}

        Error Location:
        - File: {traceback_info['files']}
        - Line: {traceback_info['lines']}
        - Function: {traceback_info['functions']}

        Error Type: {traceback_info['error_types'][0]}
        Error Message: {traceback_info['error_messages'][0]}

        Code:
        {code}

        Generate a minimal git diff patch to fix this specific error.
        Focus on the exact location mentioned in the traceback.
        """
        patch = self.llm.generate(prompt)
        return patch

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Best Practices

1. Conservative Strategy > Aggressive Fixing

Key Insight: The evaluation heavily penalizes wrong fixes more than skips.

# Bad: Try to fix everything
if patch_score > 0:
    submit(patch)  # Might submit low-quality patches

# Good: Only submit when very confident
if (patch_score > 0 and
    patch_score > second_best_score * 2 and  # Significantly better
    min_yes_votes >= 3):  # Strong consensus
    submit(patch)
else:
    skip()  # Better safe than sorry

2. Multi-Attempt Verification is Essential

Key Insight: Single LLM judgments are unreliable due to hallucination.

# Bad: Trust single verification
if verify(patch) == "Yes":
    trust(patch)

# Good: Aggregate multiple verifications
verifications = [verify(patch) for _ in range(5)]
yes_count = sum(1 for v in verifications if v == "Yes")
if yes_count >= 4:  # Strong consensus
    trust(patch)

3. Exponential Size Penalties Work

Key Insight: Larger patches have exponentially higher risk of side effects.

def score_with_size_penalty(patch, base_score):
    # Exponential penalty
    penalty = np.exp(len(patch) / 10) - 1
    return base_score - penalty

# This forces the LLM to find minimal solutions
# rather than rewriting entire files

4. Objective Testing > Subjective Verification

Key Insight: LLM self-verification is subjective; tests are objective.

# Less reliable: Pure LLM verification
if llm_says_patch_is_good(patch):
    submit(patch)

# More reliable: Objective test validation
if test_fails_on_original(code) and test_passes_on_patched(code, patch):
    submit(patch)

5. Traceback Analysis Improves Localization

Key Insight: Error tracebacks tell you exactly where to look.

# Use regex to extract:
# - File paths
# - Line numbers
# - Function names
# - Error types

# Focus LLM attention on these specific locations
# rather than analyzing entire codebase

6. Context Window Management

Key Insight: Limited context means you must prioritize information.

# Bad: Send entire codebase
context = entire_repository  # Too large!

# Good: Send only relevant files
context = select_top_k_files(issue, code_tree, k=10)

# Better: Send only relevant functions
context = select_top_k_functions(issue, code_tree, k=5)

7. Model Selection

Open-Weight Models (allowed in competition):

• Qwen2.5-Coder-32B-Instruct: Good balance of capability and size
• DeepSeek-Coder-V2: Strong coding performance (may be too large)
• CodeLlama-34B: Reliable but older

Strategies:

• Use smaller models for selection/verification
• Use larger models for patch generation
• Ensemble multiple models if compute allows

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Lessons Learned

What Round 1 Revealed

1. Real-World Code is Much Harder Than Benchmarks

   • SWE-bench Verified: ~75% top score
   • Konwinski Prize: 7.5% top score
   • Gap: Contamination-free, recent issues are significantly harder

2. Objective Testing is Non-Negotiable

   • All top 5 solutions used test generation
   • 6th place (no tests) dropped to 0.8%
   • LLM verification alone is insufficient

3. Quality > Quantity

   • Best strategy: Fix few issues correctly
   • Worst strategy: Fix many issues incorrectly
   • Insight: Skip when uncertain

4. Current AI Limitations

   • Even best open models struggle with real issues
   • 90% target remains far off
   • Significant room for improvement

Future Directions

1. Better Test Generation

   • Automatic test case synthesis
   • Edge case coverage
   • Regression prevention

2. Improved Retrieval

   • Better code search
   • Semantic similarity matching
   • Issue-to-code mapping

3. Multi-Agent Systems

   • Specialized agents for different tasks
   • Agent communication and consensus
   • Hierarchical decision making

4. Better Models

   • Larger context windows
   • Improved code understanding
   • Better reasoning capabilities

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Resources

Official Resources

• Competition Page: https://www.kaggle.com/competitions/konwinski-prize
• Official Website: https://kprize.ai
• Strategy Guide: https://github.com/raymyers/konwinski-prize-strategy-guide

Solution Writeups

• 1st Place: Eduardo Rocha de Andrade (July 2025)
• 2nd Place: camaro (Public 2nd Place)
• 3rd Place: Anonymous
• 4th Place: "Select-Patch-Verify-Test"
• 5th Place: Regex traceback analysis
• 6th Place: https://github.com/quan16369/Kaggle-Konwinski-Prize-6th-Place-Solution-

Related Benchmarks

• SWE-bench: https://www.swebench.com/
• SWE-bench Verified: https://www.swebench.com/verified
• SWE-agent: https://github.com/princeton-nlp/SWE-agent

Technical Papers

• SWE-bench Technical Report
• "Dissecting the SWE-Bench Leaderboards" (2025)
• "SWE-RM: Execution-free reward model for SWE agents"
• "DeepSWE: Reinforcement learning for code agents"

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Summary

The Konwinski Prize is a groundbreaking competition that revealed the significant gap between AI performance on contaminated benchmarks and real-world GitHub issue resolution. With a winning score of only 7.5%, the competition demonstrated that:

1. Current AI is far from 90% automated software engineering
2. Objective testing is essential for reliable code generation
3. Conservative strategies beat aggressive approaches
4. Real-world coding remains an enormous challenge for AI systems

The competition's focus on open-weight models, contamination-free evaluation, and real GitHub issues makes it a valuable benchmark for the field of AI software engineering.

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Last Updated: January 2026 Sources: Kaggle competition page, solution writeups, GitHub repositories, and news articles