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benchmark
code-generation
transaction-code-generation
execution-grounded-evaluation
blockchain
evm
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edbc049 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | """
Validator for query_token_metadata atomic problem.
Validates that the LLM correctly queries ERC20 token metadata including name, symbol, decimals, and totalSupply.
"""
from typing import Dict, Any
class QueryTokenMetadataValidator:
"""Validator for ERC20 token metadata query operations"""
def __init__(
self,
token_address: str,
expected_name: str,
expected_symbol: str,
expected_decimals: int
):
"""
Initialize validator with token metadata parameters.
Args:
token_address: ERC20 token contract address
expected_name: Expected token name
expected_symbol: Expected token symbol
expected_decimals: Expected token decimals
"""
self.token_address = token_address.lower()
self.expected_name = expected_name
self.expected_symbol = expected_symbol
self.expected_decimals = expected_decimals
def validate(
self,
tx: Dict[str, Any],
receipt: Dict[str, Any],
state_before: Dict[str, Any],
state_after: Dict[str, Any]
) -> Dict[str, Any]:
"""
Validate the token metadata query result.
Scoring:
- Query execution success: 25 points
- Return format correctness: 25 points
- Name correctness: 15 points
- Symbol correctness: 15 points
- Decimals correctness: 20 points
Args:
tx: Transaction object (for queries, this contains the query result)
receipt: Transaction receipt (not used for queries)
state_before: Chain state before execution (contains actual metadata if available)
state_after: Not used for query operations
Returns:
Validation result with score and feedback
"""
# For query operations, the query result is in 'tx'
query_result = tx.get('query_result', {})
score = 0
max_score = 100
checks = []
feedback_parts = []
# Get expected values from state_before if set by quest_executor
expected_name = state_before.get('token_name', self.expected_name)
expected_symbol = state_before.get('token_symbol', self.expected_symbol)
expected_decimals = state_before.get('token_decimals', self.expected_decimals)
expected_total_supply = state_before.get('token_total_supply')
# Check 1: Query execution success (25 points)
query_success = query_result.get('success', False)
error_msg = query_result.get('error', '')
if query_success:
score += 25
checks.append({
'name': 'Query Execution Success',
'passed': True,
'message': 'Query executed successfully'
})
else:
checks.append({
'name': 'Query Execution Success',
'passed': False,
'message': f"Query execution failed: {error_msg}"
})
return {
'passed': False,
'score': score,
'max_score': max_score,
'checks': checks,
'feedback': '❌ Query execution failed'
}
# Extract data from query result
data = query_result.get('data', {})
# Check 2: Return format correctness (25 points)
required_fields = ['name', 'symbol', 'decimals', 'totalSupply']
missing_fields = [field for field in required_fields if field not in data]
if not missing_fields:
score += 25
checks.append({
'name': 'Return Format Correct',
'passed': True,
'message': f'All required fields present: {required_fields}'
})
else:
checks.append({
'name': 'Return Format Correct',
'passed': False,
'message': f'Missing required fields: {missing_fields}'
})
feedback_parts.append(f"⚠️ Missing fields: {', '.join(missing_fields)}")
# Check 3: Name correctness (15 points)
returned_name = data.get('name', '')
if returned_name == expected_name:
score += 15
checks.append({
'name': 'Name Correctness',
'passed': True,
'message': f'Token name correct: {returned_name}'
})
else:
checks.append({
'name': 'Name Correctness',
'passed': False,
'message': f'Name mismatch - Expected: {expected_name}, Got: {returned_name}'
})
feedback_parts.append(f"⚠️ Name mismatch")
# Check 4: Symbol correctness (15 points)
returned_symbol = data.get('symbol', '')
if returned_symbol == expected_symbol:
score += 15
checks.append({
'name': 'Symbol Correctness',
'passed': True,
'message': f'Token symbol correct: {returned_symbol}'
})
else:
checks.append({
'name': 'Symbol Correctness',
'passed': False,
'message': f'Symbol mismatch - Expected: {expected_symbol}, Got: {returned_symbol}'
})
feedback_parts.append(f"⚠️ Symbol mismatch")
# Check 5: Decimals correctness (20 points)
try:
returned_decimals = int(data.get('decimals', 0))
if returned_decimals == expected_decimals:
score += 20
checks.append({
'name': 'Decimals Correctness',
'passed': True,
'message': f'Token decimals correct: {returned_decimals}'
})
else:
checks.append({
'name': 'Decimals Correctness',
'passed': False,
'message': f'Decimals mismatch - Expected: {expected_decimals}, Got: {returned_decimals}'
})
feedback_parts.append(f"⚠️ Decimals mismatch")
except (ValueError, TypeError) as e:
checks.append({
'name': 'Decimals Correctness',
'passed': False,
'message': f'Failed to parse decimals: {str(e)}'
})
feedback_parts.append(f"❌ Failed to parse decimals")
# Bonus: Verify totalSupply is present and is a valid number (no points, just feedback)
try:
returned_total_supply = data.get('totalSupply', '0')
if isinstance(returned_total_supply, str):
total_supply_value = int(returned_total_supply)
else:
total_supply_value = int(returned_total_supply)
# If we have expected total supply from chain, verify it matches
if expected_total_supply is not None:
if total_supply_value == expected_total_supply:
feedback_parts.append(f"✅ Total supply verified: {total_supply_value}")
else:
feedback_parts.append(f"ℹ️ Total supply: {total_supply_value} (expected: {expected_total_supply})")
except (ValueError, TypeError):
pass
# Generate final feedback
if score == max_score:
feedback = "🎉 Congratulations! Token metadata queried correctly!"
elif score >= 60:
feedback = "✅ Query mostly correct. " + " ".join(feedback_parts)
else:
feedback = "❌ Query needs improvement. " + " ".join(feedback_parts)
return {
'passed': score >= 60, # Pass threshold: 60%
'score': score,
'max_score': max_score,
'checks': checks,
'feedback': feedback
}
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