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Guiding Worker Self-Selection in Crowdsourcing Contests: An LLM-Augmented Algorithmic Approach
Researchers introduce GRAF, a greedy framework for crowdsourcing contest self-selection, and LLMScore, an LLM-driven method that auto-designs its scoring algorithm.
The paper studies self-selection in Tullock contests (SSTC), where workers choose contests and then compete within them. GRAF is a greedy polynomial-time framework that orders workers by a score vector with zero worker regret and platform optimality guarantees in special cases. LLMScore is an LLM-driven evolutionary framework that produces human-readable, inspectable scoring code, jointly optimizing platform utility and worker satisfaction. Across 1,000 synthetic instances in four settings, GRAF with LLMScore achieves high-quality, often near-optimal outcomes with low worker regret, transferring from small training instances to larger, structurally different settings.
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Advanced WildFire Archives
Palo Alto Networks describes Advanced WildFire as its cloud malware analysis engine using machine learning and crowdsourced intelligence.
The Unit 42 blog page is a product category archive for Advanced WildFire. The description calls it the industry's largest cloud-based malware analysis and prevention engine, using machine learning and crowdsourced intelligence to detect hard-to-catch threats. No research findings, incidents, or vulnerabilities are discussed.
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