SolarChain is a platform that ensures verifiable trust in decentralized solar energy markets by anchoring digital energy credits to the hard physical limits of solar yield, thereby preventing data man…

This paper proposes an Explainable Deep Reinforcement Learning (XRL) framework to optimize energy management in complex buildings, demonstrating that on-policy algorithms provide superior cost reducti…

This paper demonstrates that a complex deep reinforcement learning policy for power grid control can be successfully distilled into a lightweight, auditable decision tree and random forest surrogate t…

The paper addresses the reliability of open-weight LLMs for power system code generation by identifying structured API-knowledge boundary errors and proposing a boundary-aware intervention that signif…

The paper proposes S3TS, a novel tree search algorithm that simultaneously handles both non-linear system models and explicit uncertainties (scenarios) for advanced energy planning, achieving near-opt…

EnergyMamba proposes an uncertainty-aware, graph-enhanced selective state space model to significantly improve both the accuracy and reliability of energy consumption prediction by explicitly modeling…

The paper proposes replacing individual agent autonomy with a structured 'social contract' and institutional Separation of Power (SoP) to mitigate systemic failures and deceptive behavior in multi-age…

The paper models the trade-off between deploying increasingly capable AI systems and managing associated cyber risks, finding a 'deployment paradox' where high-loss environments with weak governance l…

This paper investigates the thermal constraints of deploying AI compute infrastructure in space, comparing GPUs and compute-in-memory (CIM) accelerators using a co-design methodology.

The paper analyzes Ethereum builder transactions to show that builder centralization is an emergent property of the Proposer-Builder Separation (PBS) architecture, driven by specific order flow and ME…

This paper analyzes the x402 payment protocol, revealing systemic vulnerabilities in state synchronization and signature design that allow attackers to exploit payment systems for resource leakage in…

This paper analyzes the x402 payment protocol, revealing critical synchronization and security flaws that allow attackers to exploit payment systems and force merchants to subsidize compute costs.

The paper proposes an engineering framework, inspired by metamaterials physics, to quantify institutional coordination and predict civilizational stability in the age of AI.

The paper empirically characterizes 'shadow AI'—the unsanctioned use of frontier AI in critical infrastructure—as a systemic threat that erodes established assurance and security controls.

The paper analyzes transaction selection strategies in DAG-based distributed ledgers using game theory, finding that Collaborative Fee Sharing (CFS) achieves superior performance compared to Random Fe…

The paper proposes a Quantum Augmented Microgrid (QuAM) framework that integrates quantum networking concepts to enhance the cybersecurity, confidentiality, and privacy of decentralized microgrids aga…

The paper proposes a scalable, market-analysis-driven methodology to assess national charging station cybersecurity by extrapolating field test results from a manageable subset of stations to estimate…

This paper reviews the current state of cybersecurity for EV charging infrastructure, analyzing existing machine learning countermeasures and proposing future directions to overcome data limitations i…

The paper introduces 'Matter to Mechanism,' a novel benchmark designed to rigorously evaluate AI co-scientists' ability to generate plausible, mechanism-grounded solution hypotheses for complex materi…

The paper introduces PIRS, a physics-informed reward shaping method that replaces ad-hoc comfort proxies with the ISO 7730 PMV formulation, enabling deep reinforcement learning agents to achieve energ…