Arsh Gupta | Searcharxiv

arXiv2025

QLink: Quantum-Safe Bridge Architecture for Blockchain Interoperability

Secure interoperability across heterogeneous blockchains remains one of the most pressing challenges in Web3 with existing bridge protocols vulnerable to both classical exploits and emerging quantum threats. This paper introduces QLink a quantum-safe Layer 3 interoperability protocol that integrates postquantum cryptography (PQC) quantum key distribution (QKD) and hardware security modules (HSMs) into a unified validator architecture. To our knowledge, QLink is the first interoperability framework to combine these mechanisms to secure validator communication proof aggregation and key management. Validators exchange encryption keys through QKD channels, achieving information-theoretic security against interception, while cross-chain proofs are generated and aggregated with NIST-standardized PQC algorithms. Private keys remain sealed inside HSM enclaves mitigating the risk of theft or leakage. Deployed as a dedicated Layer 3 protocol QLink operates independently of Layer 1 and Layer 2 chains providing a scalable decentralized foundation for secure cross-chain messaging and asset transfer. Experimental evaluation using network simulations demonstrates that validator communication overhead remains sub-second while security guarantees extend beyond current bridge architectures to resist both classical and quantum adversaries. By addressing today vulnerabilities and anticipating future quantum threats QLink establishes a practical and future-proof pathway for blockchain interoperability.

cs.CR↗

arXiv2025

Evaluating Large Language Models on Rare Disease Diagnosis: A Case Study using House M.D

Large language models (LLMs) have demonstrated capabilities across diverse domains, yet their performance on rare disease diagnosis from narrative medical cases remains underexplored. We introduce a novel dataset of 176 symptom-diagnosis pairs extracted from House M.D., a medical television series validated for teaching rare disease recognition in medical education. We evaluate four state-of-the-art LLMs such as GPT 4o mini, GPT 5 mini, Gemini 2.5 Flash, and Gemini 2.5 Pro on narrative-based diagnostic reasoning tasks. Results show significant variation in performance, ranging from 16.48% to 38.64% accuracy, with newer model generations demonstrating a 2.3 times improvement. While all models face substantial challenges with rare disease diagnosis, the observed improvement across architectures suggests promising directions for future development. Our educationally validated benchmark establishes baseline performance metrics for narrative medical reasoning and provides a publicly accessible evaluation framework for advancing AI-assisted diagnosis research.

cs.CL↗