Explainable AI for Reinforcement Learning via Causal Reasoning

Inside the black box: A comprehensive survey of explainable AI benchmarks, protocols, technical analysis, and open gaps

AI and Bullshit

It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.

End-to-End Federated Intelligence for Secure and Standardized Digital Twin Orchestration in 6G Smart Cities

Digital twins (DTs) are rapidly emerging as foundational enablers of 6G smart cities, offering real time monitoring, predictive analytics, and autonomous control across transportation, energy, healthcare, and industrial domains. Large scale DT adoption faces critical barriers including cybersecurity vulnerabilities, privacy risks, and the absence of standardized orchestration frameworks. This article presents Fed-DTOrch, a comprehensive end to end architecture that integrates federated intelligence, blockchain based audit trails, and AI governance to achieve secure and privacy preserving DT management. The proposed three tier architecture spans IoT and edge devices, domain specific twins, and a city level orchestrator, employing secure federated learning for model updates, lightweight cryptographic authentication, and tamper proof logging. We quantify the DT threat landscape, perform a standards gap analysis across ISO/IEC 27001, 3GPP TS 33.501, ITU-T IoT risk frameworks, and NIST AI RMF, and introduce a 6G ready security framework incorporating federated AI trust metrics, secure synchronization, and explainable AI audits. Cross domain evaluation across five smart city sectors demonstrates 35-60% privacy gain, 40-55% attack mitigation, 28-40% reliability uplift, 26-30% latency reduction, and >85% compliance readiness with <10% overhead. These results provide the first integrated blueprint that combines federated intelligence, blockchain-based auditability, and standards gap analysis to enable secure, standardized, and interoperable DT orchestration for trustworthy 6G ecosystems.

Physics-Informed Hybrid Machine Learning Model for Carbonation Depth Prediction in Concrete through Residual Correction and Variogram Analysis of Response Surfaces

A hybrid residual correction framework that integrates a physics-based carbonation model with a stacked ensemble of machine learning algorithms: gradient boosted regression trees (GBRT), support vector regression (SVR), and Gaussian process regression (GPR), combined through an XGBoost metamodel, demonstrating that the residual-based metamodel reproduced observed carbonation depths with higher accuracy.

An SAC-Based Auto Optimization Model for Dynamic Current Balancing of Multichip Paralleled SiC Power Module

The dynamic current imbalance between the paralleled SiC mosfets in multichip power modules, which is commonly attributed to the asymmetric module layout, severely limits their current capacity and thermal reliability. Adjusting the connection points of bonding wires is an effective method to mitigate imbalanced dynamic current. However, manual trial-and-error is currently the most common method for optimizing connection points, which is both deficient and inefficient. Existing automated solutions usually rely on a prefitting process based on large datasets, which is time-consuming and impractical for high-dimensional parameter applications. Thus, this article proposes an optimization model to mitigate dynamic current imbalance, which can automatically adjust the connection points of bonding wires without any manual intervention. The reinforcement learning (RL) soft actor-critic algorithm was applied to the power module optimization, eliminating the need for prefitting and enabling high-dimensional parameter optimization. After optimization, nearly complete dynamic current balancing in both high-side and low-side switches in a multichip-paralleled half-bridge power module is achieved, as verified by simulations and experiments. This model achieves true dynamic current balancing automation for the first time, providing an important reference for the application of RL to the automated optimization of multichip power modules.

The Awakener: A Unified Materialist Theory of Consciousness as a Dynamic Process

What is consciousness? Despite millennia of inquiry, humanity still lacks a consensus definition. This paper proposes a unified, strictly materialist theory: consciousness is not an entity but a process-specifically, the dynamic process that occurs when a specific structure, formed within complex, chaotic circuits, formats and processes input data and produces output. The structure is the carrier; the processing itself is consciousness. Building on this definition, the paper introduces a two-layer architecture that distinguishes basal consciousness (the autonomous survival functions shared by nearly all living organisms) from advanced consciousness (spanning the awareness of basic sensations, self-awareness, metacognition, and abstract reasoning). We argue that the two layers are related not as a hierarchy of subordination, but as a partnership of trust shaped by natural selection. The paper further proposes the mechanism of the self-reinforcing circuit: advanced consciousness is not complete at birth; it must be "awakened" through sustained external stimulation. Once awakened, it consolidates its own existence by continuously reinforcing the neural pathways dedicated to it, sculpting the unique "I" of each individual. This mechanism offers a parsimonious explanation of dissociative identity disorder, personality change after brain injury, and the diversity of human personality. The Awakener model traces the complete evolutionary path: from primitive sensory-interpretation tools, through the emergence of intelligence, the first awakening event, and chained cultural transmission, to the birth of language and writing. We apply this framework to explain why intelligent animals never developed civilization, to predict which species might awaken next, and to redefine the current state of artificial intelligence as a frozen brain-structurally capable of consciousness, but temporally static: a single frame. Within a single, self-consistent framework, the theory unifies the core problems of consciousness research: the definition and essence of consciousness, the hard problem, the two-layer distinction, the physical basis of the "I", personality diversity, the evolutionary origin of consciousness, the boundaries of animal consciousness, the origin of language, the conscious status of AI, free will and the Libet experiments, split-brain interpretation, dreams and sleepwalking, and the relation between the subconscious and System 1/System 2. No immaterial components are required: every step rests on existing experimental evidence or established neuroscientific findings.