This paper from Google Quantum AI and the Ethereum Foundation details the catastrophic risks that cryptographically relevant quantum computers (CRQCs) pose to the global cryptocurrency ecosystem. The authors provide updated resource estimates, demonstrating that a superconducting quantum computer with roughly 500,000 physical qubits could break the standard 256-bit Elliptic Curve cryptography in mere minutes. This capability introduces a "fast-clock" threat where attackers can intercept and forge transactions in real-time, known as on-spend attacks, alongside the more traditional threat to dormant assets.

Beyond Bitcoin, the analysis identifies systemic vulnerabilities in Ethereum’s smart contracts, Proof-of-Stake consensus, and tokenized real-world assets, which could lead to total network destabilization. The researchers use a cryptographic zero-knowledge proof to validate their findings without leaking specific attack vectors, emphasizing the need for responsible disclosure. Ultimately, the text serves as an urgent call for the blockchain community to migrate to Post-Quantum Cryptography (PQC) and for policymakers to develop "digital salvage" frameworks for recovering at-risk assets. Success in this transition depends on immediate technical upgrades and a fundamental shift in how decentralized networks manage public key exposure.

NVIDIA researchers introduce Nemotron 3 Super, a highly efficient large language model featuring 120 billion total parameters and 12 billion active parameters. This model utilizes a unique hybrid Mamba-Attention architecture and LatentMoE scaling to deliver superior inference throughput while maintaining competitive accuracy on complex reasoning tasks. Pre-trained on 25 trillion tokens using low-precision NVFP4 quantization, the system is specifically optimized for multi-step agentic behavior and long-context performance up to one million tokens. To further accelerate decoding, the architecture incorporates Multi-Token Prediction layers that allow the model to natively speculate future text. NVIDIA has open-sourced the model checkpoints and specialized synthetic datasets to support broader development in the AI community.

This paper introduces a formal framework to evaluate post-quantum cryptographic (PQC) readiness by analyzing how security protocols interact across different network layers. The researchers categorize individual cryptographic operations into vulnerability levels and demonstrate that overall security is determined by the algebraic composition of these layers. Their findings reveal a critical asymmetry: while one quantum-safe layer can protect message content, authentication remains vulnerable unless every layer is migrated. Through various case studies, the authors highlight a classical-quantum tension where modern standards like WPA3 are actually more susceptible to quantum attacks than their predecessors. Ultimately, the study provides a structured methodology for organizations to prioritize migration strategies and manage the risk of "harvest now, decrypt later" threats.

Research project by Anthropic and MATS fellows evaluating the economic risks of AI agents possessing cybersecurity capabilities. Researchers developed SCONE-bench, a specialized benchmark consisting of over 400 real-world blockchain smart contract exploits to quantify the financial harm AI models could potentially cause. The findings demonstrate that frontier models like Claude 4.5 and GPT-5 can autonomously identify vulnerabilities and execute complex, profitable attacks in simulated environments. One specific case study illustrates a Sonnet 4.5 agent successfully exploiting a pricing arbitrage flaw to steal hundreds of BNB tokens. Ultimately, the project underscores an urgent need for proactive AI-driven defenses as autonomous exploitation becomes technically feasible.