IGS Dataset
AI Training Data Schema
IGS Dataset defines how interaction data is archived on Walrus for continuous AI model training, performance auditing, and protocol transparency.
Dataset Purpose
Every intent execution generates valuable training data:
Intent Classification: Teach AI to categorize new intents
Ranking Optimization: Learn which solutions perform best
Solver Evaluation: Track historical performance patterns
Market Analysis: Understand liquidity and pricing dynamics
Dataset Schema Reference
Schema Overview
RawFeatures
Raw features extracted from IGSIntent for ML training
GroundTruthLabel
Ground truth classification labels
LabelingMetadata
Labeling metadata
ExecutionOutcome
Execution outcome for feedback loop
IntentClassificationTrainingData
Complete training sample for intent classification
RawFeatures
solver_window_ms
number
Solver access window duration
user_decision_timeout_ms
number
User decision timeout
time_to_deadline_ms
number
Time until deadline
time_in_force
string
Time in force policy
max_slippage_bps
number
Maximum slippage in basis points
max_gas_cost_usd
number
Maximum gas cost in USD
max_hops
number
Maximum routing hops
has_whitelist
boolean
Has protocol whitelist
has_blacklist
boolean
Has protocol blacklist
has_limit_price
boolean
Has limit price condition
optimization_goal
string
Primary optimization goal
surplus_weight
number
Surplus weight (0-100)
gas_cost_weight
number
Gas cost weight (0-100)
execution_speed_weight
number
Execution speed weight (0-100)
reputation_weight
number
Reputation weight (0-100)
require_simulation
boolean
Requires simulation
input_count
number
Number of input assets
output_count
number
Number of output assets
input_asset_types
array
Input asset type classifications
output_asset_types
array
Output asset type classifications
input_value_usd
number
Input value in USD
expected_output_value_usd
number
Expected output value in USD
benchmark_source
string
Benchmark data source
benchmark_confidence
number
Benchmark confidence (0-1)
expected_gas_usd
number
Expected gas cost in USD
expected_slippage_bps
number
Expected slippage in basis points
has_nlp_input
boolean
Has natural language input
nlp_confidence
number
NLP parsing confidence (0-1)
client_platform
string
Client platform identifier
tag_count
number
Number of tags
Time in Force Values
immediate
Immediate execution
good_til_cancel
Good until canceled
fill_or_kill
Fill or kill order
Optimization Goal Values
maximize_output
Maximize output amount
minimize_gas
Minimize gas costs
fastest_execution
Fastest execution speed
balanced
Balanced optimization
Asset Type Values
native
Native blockchain token
stable
Stablecoin
volatile
Volatile cryptocurrency
GroundTruthLabel
primary_category
string
Primary intent category
detected_priority
string
Detected user priority
complexity_level
string
Intent complexity level
risk_level
string
Associated risk level
Primary Category Values
swap
Token swap operation
limit_order
Limit order execution
complex_defi
Complex DeFi strategy
arbitrage
Arbitrage opportunity
other
Other intent type
Detected Priority Values
speed
Speed-focused
cost
Cost-focused
output
Output-focused
balanced
Balanced priority
Complexity Level Values
simple
Simple operation
moderate
Moderate complexity
complex
High complexity
Risk Level Values
low
Low risk
medium
Medium risk
high
High risk
LabelingMetadata
labeling_method
string
Method used for labeling
labeled_by
string
Entity that created the label
labeled_at
number
Labeling timestamp
label_confidence
number
Label confidence (0-1)
notes
string
Additional labeling notes
Labeling Method Values
expert_manual
Manual expert labeling
rule_based
Rule-based automatic labeling
outcome_based
Based on execution outcomes
user_feedback
User feedback labeling
synthetic
Synthetically generated
ExecutionOutcome
executed
boolean
Whether intent was executed
chosen_solution_rank
number
Rank of chosen solution
chosen_solution_id
string
ID of chosen solution
actual_metrics
object
Actual execution metrics
user_satisfaction
number
User satisfaction score (1-5)
executed_at
number
Execution timestamp
Actual Metrics Object
actual_output_usd
number
Actual output value in USD
actual_gas_cost_usd
number
Actual gas cost in USD
actual_execution_time_ms
number
Actual execution time in milliseconds
actual_slippage_bps
number
Actual slippage in basis points
IntentClassificationTrainingData
sample_id
string
Unique sample identifier
intent_metadata
object
Intent metadata object
raw_features
RawFeatures
Raw feature data
ground_truth
GroundTruthLabel
Ground truth labels
label_info
LabelingMetadata
Labeling metadata
execution_outcome
ExecutionOutcome
Execution outcome data
dataset_version
string
Dataset version identifier
created_at
number
Sample creation timestamp
full_intent_ref
string
Reference to full intent data
Intent Metadata Object
intent_id
string
Intent identifier
intent_type
string
Intent type classification
created_at
number
Intent creation timestamp
Data Storage
Walrus Integration
All dataset entries stored on Walrus:
Cost Efficiency: Cheap decentralized storage
Availability: Distributed redundancy
Integrity: Cryptographic verification
Permanence: Long-term archival
Continuous Learning
Model Retraining Cycle
Data Collection: New executions archived hourly
Preprocessing: Anonymization and validation
Training: Updated models trained weekly
Validation: Performance tested on holdout set
Deployment: Gradual rollout to production
Reinforcement Learning
Positive Examples:
Solutions that outperformed estimates
High user satisfaction indicators
Novel successful strategies
Negative Examples:
Solutions that underperformed
Failed executions
User complaints or reverts
Learn More
IGS Core - Data type definitions
AI-Powered Ranking - How data is used
Privacy Protection - Security details
References
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