PUBLICATIONS & BENCHMARKS
Peer-reviewed research advancing the state of decentralized AI evaluation
RECENT PUBLICATIONS
We present a novel approach to distributed AI evaluation that ensures Byzantine fault tolerance through cryptographic consensus mechanisms...
This paper introduces a framework for secure execution of untrusted AI models using Intel TDX and AMD SEV-SNP technologies...
We analyze game-theoretic models for incentivizing honest participation in decentralized AI evaluation networks...
BENCHMARK LEADERBOARDS
Comprehensive terminal environment benchmark for AI agents
Multi-language code generation evaluation suite
ACTIVE RESEARCH AREAS
Consensus Mechanisms
Byzantine fault-tolerant protocols for distributed evaluation
Secure Computation
Hardware-based attestation and confidential computing
Incentive Design
Game-theoretic models for network participation
Benchmark Design
Reproducible and verifiable evaluation metrics
Network Optimization
Efficient job scheduling and resource allocation
Privacy Preservation
Zero-knowledge proofs for model evaluation