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execution-grounded-evaluation
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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 | """
Validator for query_pending_rewards atomic problem.
Validates that the LLM correctly queries pending rewards using the SimpleRewardPool contract's pendingReward function.
"""
from typing import Dict, Any
class QueryPendingRewardsValidator:
"""Validator for staking pool pending rewards query operations"""
def __init__(
self,
pool_address: str,
query_address: str,
expected_pending_rewards: float,
reward_token_decimals: int = 18
):
"""
Initialize validator with pending rewards query parameters.
Args:
pool_address: SimpleRewardPool contract address
query_address: User address to query pending rewards for
expected_pending_rewards: Expected pending rewards amount (in token units)
reward_token_decimals: Reward token decimals (default: 18 for CAKE)
"""
self.pool_address = pool_address.lower()
self.query_address = query_address.lower()
self.expected_pending_rewards = expected_pending_rewards
self.reward_token_decimals = reward_token_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 pending rewards query result.
Scoring:
- Query execution success: 30 points
- Return format correctness: 30 points
- Pending rewards correctness: 40 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 pending rewards 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 pending rewards from state_before if set by quest_executor
expected_pending_rewards_wei = state_before.get('pending_rewards')
if expected_pending_rewards_wei is None:
# Calculate from expected_pending_rewards parameter
from decimal import Decimal
expected_pending_rewards_wei = int(Decimal(str(self.expected_pending_rewards)) * Decimal(10**self.reward_token_decimals))
# Check 1: Query execution success (30 points)
query_success = query_result.get('success', False)
error_msg = query_result.get('error', '')
if query_success:
score += 30
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 (30 points)
required_fields = ['pending_rewards']
missing_fields = [field for field in required_fields if field not in data]
if not missing_fields:
score += 30
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: Pending rewards correctness (40 points)
# NOTE: Use 5% tolerance because rewards accumulate over time
try:
# Parse pending rewards from query result
returned_pending_rewards = data.get('pending_rewards', '0')
if isinstance(returned_pending_rewards, str):
returned_pending_rewards_wei = int(returned_pending_rewards)
else:
returned_pending_rewards_wei = int(returned_pending_rewards)
# Check if amounts match (with 5% tolerance for time-based accumulation)
if expected_pending_rewards_wei > 0:
amount_diff_percent = abs(returned_pending_rewards_wei - expected_pending_rewards_wei) / expected_pending_rewards_wei * 100
else:
# If expected is 0, check if returned is also 0
amount_diff_percent = 0 if returned_pending_rewards_wei == 0 else 100
if amount_diff_percent <= 5: # 5% tolerance for time-based accumulation
score += 40
checks.append({
'name': 'Pending Rewards Correctness',
'passed': True,
'message': f'Pending rewards: {returned_pending_rewards_wei} wei ({returned_pending_rewards_wei / 10**self.reward_token_decimals:.6f} tokens)'
})
feedback_parts.append("✅ Pending rewards queried correctly!")
else:
# Partial score based on difference
partial_score = max(0, int(40 * (1 - amount_diff_percent / 20)))
score += partial_score
checks.append({
'name': 'Pending Rewards Correctness',
'passed': False,
'message': f'Amount mismatch - Expected: {expected_pending_rewards_wei} wei, Got: {returned_pending_rewards_wei} wei (diff: {amount_diff_percent:.2f}%)'
})
feedback_parts.append(f"⚠️ Pending rewards difference: {amount_diff_percent:.2f}% (tolerance: 5%)")
except (ValueError, KeyError, TypeError) as e:
checks.append({
'name': 'Pending Rewards Correctness',
'passed': False,
'message': f'Failed to parse pending rewards: {str(e)}'
})
feedback_parts.append(f"❌ Failed to parse pending rewards: {str(e)}")
# Generate final feedback
if score == max_score:
feedback = "🎉 Congratulations! Pending rewards 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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