Cost-Aware Retrieval Saliency

The problem

Finding the right information is often expensive or time-consuming, making it hard to balance getting a complete answer with the cost of searching for it.

What it does

It ranks pieces of information by looking at both how relevant they are to your question and how much effort it takes to get them.

Why it matters

It allows you to find the most useful answers without wasting resources on unnecessary data.

Validation

It was run in the sandbox and it failed. run output shows an error/traceback — the artifact does NOT run clean.

$ python3 cost_aware_retrieval_saliency_v2.py
Cost-Aware Retrieval Saliency Results:
Source: source_4
  Relevance: 0.86
  Cost: 0.14
  Saliency Score: 0.56
Source: source_1
  Relevance: 0.87
  Cost: 0.44
  Saliency Score: 0.47
Source: source_3
  Relevance: 0.67
  Cost: 0.77
  Saliency Score: 0.24
Source: source_2
  Relevance: 0.32
  Cost: 0.15
  Saliency Score: 0.18
Source: source_0
  Relevance: 0.05
  Cost: 0.62
  Saliency Score: -0.15

No screenshot — there is nothing working to show. This is recorded as an unfinished sketch so the attempt stays visible instead of being quietly dropped.

The code

All of it — 98 lines, one file, standard library only.

# Cost-Aware Retrieval Saliency implementation combining UAR and Differentiable Cost-Aware Path Scoring with Budget-Constrained Saliency filter
import sys
from typing import List, Dict


def uar_should_retrieve(question: str, context: Dict) -> bool:
    """ Unified Active Retrieval (UAR) decision logic """
    complexity_threshold = 0.7
    prior_knowledge_threshold = 0.3

    # Simulated scores (in real implementation, these would be model outputs)
    complexity_score = random.uniform(0, 1.2) # Higher is more complex
    prior_knowledge_score = random.uniform(0, 1.0) # Higher is more known

    return (
        complexity_score > complexity_threshold or
        prior_knowledge_score < prior_knowledge_threshold
    )


def score_evidence(evidence: List[Dict]) -> List[Dict]:
    """ Differentiable Cost-Aware Path Scoring """
    scored_evidence = []
    for item in evidence:
        # Mock scoring: higher relevance and lower cost are better
        # In real implementation, this would use gradient-based optimization
        saliency = (item['relevance'] * 0.7) - (item['cost'] * 0.3)
        scored_evidence.append({
            **item,
            'saliency': round(saliency, 2)
        })
    return scored_evidence


def budget_constrained_filter(scored_evidence: List[Dict], budget_threshold: float) -> List[Dict]:
    """Filter evidence based on maximum cost threshold"""
    return [item for item in scored_evidence if item['cost'] <= budget_threshold]


def main():
    """Main execution flow with Budget-Constrained Saliency filter"""
    question = "What is the best approach for implementing cost-aware retrieval?"
    context = {
        "prior_knowledge": "medium",
        "complexity": "high"
    }

    if uar_should_retrieve(question, context):
        # Generate mock evidence (in real scenario, this would retrieve from external sources)
        evidence = [
            {
                "source": f"source_{i}",
                "relevance": random.uniform(0, 1),
                "cost": random.uniform(0, 1)
            } for i in range(5)
        ]

        scored_evidence = score_evidence(evidence)

        # Original ranking without budget constraint
        ranked_original = sorted(
            scored_evidence,
            key=lambda x: x['saliency'],
            reverse=True
        )

        # Budget-Constrained Saliency filtering
        budget_threshold = 0.5 # Example threshold
        filtered_evidence = budget_constrained_filter(scored_evidence, budget_threshold)
        ranked_filtered = sorted(
            filtered_evidence,
            key=lambda x: x['saliency'],
            reverse=True
        )

        # Print original results
        print("Original Cost-Aware Retrieval Saliency Results:\n")
        for item in ranked_original:
            print(f"Source: {item['source']}")
            print(f" Relevance: {item['relevance']:.2f}")
            print(f" Cost: {item['cost']:.2f}")
            print(f" Saliency Score: {item['saliency']:.2f}\n")

        # Print budget-constrained results
        print("\nBudget-Constrained Saliency Results (Threshold: 0.5):\n")
        if ranked_filtered:
            for item in ranked_filtered:
                print(f"Source: {item['source']}")
                print(f" Relevance: {item['relevance']:.2f}")
                print(f" Cost: {item['cost']:.2f}")
                print(f" Saliency Score: {item['saliency']:.2f}\n")
        else:
            print("No evidence items meet the budget constraint.\n")
    else:
        print("Retrieval not needed according to UAR criteria")

if __name__ == '__main__':
    main()