Abstract: Journalism plays a structurally indispensable yet under-theorized role in democratic governance. This article situates journalism within a recalibrated constitutional architecture in which the Quarta Politica—conceptualized as an Ombudsman Council—constitutes a fourth power alongside the legislative, executive, and judicial branches, endowed with distinct coercive authority oriented toward systemic correction and participatory accountability. Within this framework, journalism is not a separate power but a constitutionally protected function that secures the informational conditions upon which all four powers depend. By producing, verifying, and disseminating public information, journalism sustains accountability, enables informed participation, and facilitates collective judgment. The article reconceptualizes democracy as dependent on three interrelated dimensions: the distribution of coercive authority, the institutionalization of participatory oversight, and the integrity of the informational environment. It demonstrates that journalism performs a non-substitutable role as an early-warning and accountability-generating mechanism. Under conditions of digital transformation, platform dominance, and media fragility, these informational foundations are increasingly at risk. The article therefore advances a calibrated framework for the constitutional protection of journalism—grounded in independence, sustainability, and accountability—embedded within the Quarta Politica. A comparative perspective, including Global South contexts, underscores the generalizability of this approach across diverse democratic settings.

Abstract: Smooth window functions that restrict field actions to finite spacetime domains appear throughout quantum field theory, quantum optics, and open quantum systems, wherever interactions are switched on and off, detectors couple for finite durations, or systems decohere within bounded regions. When such a window function ⋄(x) is introduced into the matter action of a covariant field theory, two structural consequences are unavoidable: the windowed Ward identities acquire boundary-layer corrections confined to the decoherence transition region, and the contracted Bianchi identity requires a compensating stress-energy contribution at the window boundary. Both consequences follow from the product rule of covariant differentiation and are independent of any specific physical motivation for the window. The present paper develops these consequences systematically for each sector of the Standard Model in curved spacetime. The windowed action prescription is applied to Dirac fermions, complex scalar fields, Maxwell theory, and the complete SU(3)c×SU(2)L×U(1)Y gauge Lagrangian. Each sector is shown to recover standard curved-spacetime quantum field theory exactly within the localization window, with all deviations confined to a boundary layer of thickness set by the decoherence timescale. A Noether analysis yields windowed Ward identities of the form ∇μ(⋄Jμ)=0: gauge invariance and Lorentz symmetry are preserved exactly within the window, and apparent non-conservation is a kinematic boundary effect mathematically identical to open-system flux terms from decoherence theory , . The non-local boundary term Tμνnl required by the Bianchi identity decomposes as Tμνnl=Tμνcomp+TμνRem, where Tμνcomp is the boundary-layer compensator and TμνRem is its macroscopic coarse-grained remnant in the high-localization-density regime. A formal Lemma establishes that for any regular quantum field, Tμνcomp vanishes upon coarse-graining, so standard field evolution leaves no macroscopic stress-energy remnant. The sharp-window limit recovers the Israel junction conditions exactly, and the smooth-window generalization is structurally identical to the Ashtekar–Krishnan dynamical horizon flux balance laws.

Abstract: This study investigates a method that integrates retrieval-augmented mechanisms into large language model agents for scientific literature review generation. The approach addresses the limitations of traditional review models that rely on parametric knowledge with insufficient timeliness and limited coverage. Incorporating external document retrieval and dynamic information fusion into the generation process enhances the accuracy and completeness of the output. The overall framework consists of query encoding, semantic retrieval, document filtering, knowledge fusion, language modeling, task planning, memory storage, and reinforcement optimization, forming a closed loop of retrieval, understanding, and generation. Relevant document fragments are first retrieved through semantic vector search to ensure comprehensive and reliable information sources. These external representations are then integrated with the internal embeddings of the language model through weighted fusion, which preserves fluency while maintaining factual grounding. The task planning module constrains logical flow and text structure, and reinforcement learning optimization further improves relevance and consistency. Comparative experiments on large-scale scientific literature datasets demonstrate that the method outperforms existing approaches on ROUGE, BLEU, METEOR, and diversity metrics, validating its effectiveness and practicality. The findings show that combining retrieval augmentation with agent architectures can significantly improve coverage, accuracy, and language quality in review generation, providing a feasible solution for knowledge organization in complex literature environments.