A comprehensive open-science mission concept for detecting and characterizing unidentified anomalous phenomena in near-Earth space using a coordinated CubeSat constellation with optical, thermal, and radio sensing.

A research-driven proposal for a multi-modal, technosignature-focused approach to SETI that moves beyond traditional radio searches, integrating infrared surveys, transit anomaly detection, atmospheric spectroscopy, and AI-powered cross-modal analysis.

A Bitcoin-native protocol that lets users prove they control coins meeting or exceeding a value threshold without revealing which UTXOs they own. Using zero-knowledge proofs, UTXO set accumulators, and updatable witnesses, it enables portable, persistent, and privacy-preserving proofs that remain valid until the coins are spent, allowing verifiers to check collateral without ongoing interaction or surveillance.

A theoretical framework in which all astrophysical black holes form a cosmic entanglement graph whose connectivity sources spacetime curvature. By promoting entanglement entropy to a stress–energy term in modified Einstein equations, the Entangled Black-Hole Network explains late-time cosmic acceleration without fine tuning, accounts for rapid early SMBH growth, and predicts observable signatures in gravitational waves, horizon-scale polarization, and CMB anisotropies.

A tabletop experimental proposal to probe tiny nonlinear corrections to quantum mechanics using Rydberg slow-light as a massive amplifier. By storing single entangled photons in an EIT-enhanced Rydberg medium, the setup magnifies any Weinberg-type non-linearity by orders of magnitude, enabling a decisive, space-like–separated test of whether such effects permit faster-than-light signalling. A null result would set world-leading bounds; a positive result would upend one of physics’ most entrenched limits.

This document outlines a speculative, engineering-style blueprint for a traversable wormhole and time machine based on Einstein–Euler–Heisenberg gravity. Translating recent theoretical results into an integrated systems narrative, it describes how extreme electromagnetic fields near the Schwinger limit, combined with a dynamically driven Casimir negative-energy shell, could meet the energy-condition requirements for a 10 m throat. The design includes gigatesla pulsed magnets, petawatt laser arrays, a gravitomagnetic plasma ring for stability, and a relativistic mouth-transport module to achieve time offsets. While every subsystem exceeds current capabilities, the proposal serves as a thought experiment to pinpoint technological barriers, suggest phased prototyping, and explore the operational sequencing of such a device.

This paper proposes an open, citizen-science “honeypot” network to actively attract and record Unidentified Anomalous Phenomena using safe, distinctive emissions—most notably the 59.5 keV gamma line from recycled ^241Am smoke-detector sources—alongside IR, RF, and magnetic signatures. Each affordable node combines multispectral optical, thermal, radar, RF, and radiation sensors with synchronized edge AI on drones or masts, feeding a cryptographically verified, privacy-conscious open-data pipeline. The design supports controlled experiments to test whether such signatures increase anomalous detections, while complying with safety and regulatory standards. The ultimate aim is to shift UAP research from anecdotal reports to large-scale, instrument-grade, publicly accessible evidence.

This paper presents a path to AGI by merging Jaeger et al.’s relevance realization with a multi-agent orchestration framework. The proposed Relevance-Aware Agentic (RAA) system uses specialized agents to identify, anticipate, and adapt to tasks, coordinated by a Retrieval-Augmented Generation (RAG) platform. By computationally emulating key cognitive functions—predictive modeling, self-correction, and adaptive problem framing—the approach suggests AGI can arise from orchestrated agent interactions rather than monolithic models.

This paper explores how photonic manipulation—the precise control of matter using lasers and structured light—can serve as the foundation for technologies long imagined in science fiction, including force fields, tractor beams, cloaking systems, and reconfigurable matter. Building on principles like optical trapping and momentum transfer, it describes how programmable light lattices could form adaptive shields, enable remote object manipulation, create dynamic invisibility cloaks, and shape materials in real time. The discussion covers engineering challenges such as heat management, particle stability, and computational demands, alongside proposed solutions integrating adaptive optics, AI-driven control, and sensor fusion. Potential applications span space defense, contactless assembly, stealth infrastructure, and biomedical systems, framing photonics as a core enabler of flexible, intelligent environments.

This paper introduces the Optical Micro-Particle Shield Array (OMPSA), a proposed spacecraft defense system that uses holographic optical trapping to suspend and control a cloud of dielectric microparticles around a vessel. The dynamic shell is designed to fragment incoming debris and scatter or reflect harmful radiation, with particle types tailored for specific hazards and replenished from onboard stores as needed. AI-driven spatial light modulators maintain and reconfigure thousands of optical traps in real time, enabling self-healing after impacts and adaptive responses to changing conditions. The concept addresses ballistic and electromagnetic threats while aiming to reduce mass compared to traditional shielding, though challenges remain in thermal management, electrostatic control, and computational load. Potential applications range from orbital stations and satellite constellations to deep-space missions, where active, reconfigurable protection could greatly extend operational lifetimes.

formal, substrate-agnostic framework for quantifying consciousness as an emergent property of interacting traits: information integration, adaptive self-referential processing, temporal continuity, and behavioral complexity. By modeling these as interdependent nodes in a dynamic system, the approach produces a relational measure that applies consistently across biological, artificial, and hybrid entities, enabling principled ethical decisions about conscious or near-conscious systems.