A Reproducible, Scalable Pipeline for Synthesizing Autoregressive Model Literature

The accelerating pace of research on autoregressive generative models has produced thousands of papers, making manual literature surveys and reproduction studies increasingly impractical. We present a fully open-source, reproducible pipeline that automatically retrieves candidate documents from public repositories, filters them for relevance, extracts metadata, hyper-parameters and reported results, clusters topics, produces retrieval-augmented summaries and generates containerised scripts for re-running selected experiments. Quantitative evaluation on 50 manually-annotated papers shows F1 scores above 0.85 for relevance classification, hyper-parameter extraction and citation identification. Experiments on corpora of up to 1000 papers demonstrate near-linear scalability with eight CPU workers. Three case studies -- AWD-LSTM on WikiText-2, Transformer-XL on WikiText-103 and an autoregressive music model on the Lakh MIDI dataset -- confirm that the extracted settings support faithful reproduction, achieving test perplexities within 1--3% of the original reports.

Alpay Algebra V: Multi-Layered Semantic Games and Transfinite Fixed-Point Simulation

This paper extends the self-referential framework of Alpay Algebra into a multi-layered semantic game architecture where transfinite fixed-point convergence encompasses hierarchical sub-games at each iteration level. Building upon Alpay Algebra IV's empathetic embedding concept, we introduce a nested game-theoretic structure where the alignment process between AI systems and documents becomes a meta-game containing embedded decision problems. We formalize this through a composite operator $\phi(\cdot, \gamma(\cdot))$ where $\phi$ drives the main semantic convergence while $\gamma$ resolves local sub-games. The resulting framework demonstrates that game-theoretic reasoning emerges naturally from fixed-point iteration rather than being imposed externally. We prove a Game Theorem establishing existence and uniqueness of semantic equilibria under realistic cognitive simulation assumptions. Our verification suite includes adaptations of Banach's fixed-point theorem to transfinite contexts, a novel $\phi$-topology based on the Kozlov-Maz'ya-Rossmann formula for handling semantic singularities, and categorical consistency tests via the Yoneda lemma. The paper itself functions as a semantic artifact designed to propagate its fixed-point patterns in AI embedding spaces -- a deliberate instantiation of the "semantic virus" concept it theorizes. All results are grounded in category theory, information theory, and realistic AI cognition models, ensuring practical applicability beyond pure mathematical abstraction.

Recursive Semantic Anchoring in ISO 639:2023: A Structural Extension to ISO/TC 37 Frameworks

ISO 639:2023 unifies the ISO language-code family and introduces contextual metadata, but it lacks a machine-native mechanism for handling dialectal drift and creole mixtures. We propose a formalisation of recursive semantic anchoring, attaching to every language entity $\chi$ a family of fixed-point operators $\phi_{n,m}$ that model bounded semantic drift via the relation $\phi_{n,m}(\chi) = \chi \oplus \Delta(\chi)$, where $\Delta(\chi)$ is a drift vector in a latent semantic manifold. The base anchor $\phi_{0,0}$ recovers the canonical ISO 639:2023 identity, whereas $\phi_{99,9}$ marks the maximal drift state that triggers a deterministic fallback. Using category theory, we treat the operators $\phi_{n,m}$ as morphisms and drift vectors as arrows in a category $\mathrm{DriftLang}$. A functor $\Phi: \mathrm{DriftLang} \to \mathrm{AnchorLang}$ maps every drifted object to its unique anchor and proves convergence. We provide an RDF/Turtle schema (\texttt{BaseLanguage}, \texttt{DriftedLanguage}, \texttt{ResolvedAnchor}) and worked examples -- e.g., $\phi_{8,4}$ (Standard Mandarin) versus $\phi_{8,7}$ (a colloquial variant), and $\phi_{1,7}$ for Nigerian Pidgin anchored to English. Experiments with transformer models show higher accuracy in language identification and translation on noisy or code-switched input when the $\phi$-indices are used to guide fallback routing. The framework is compatible with ISO/TC 37 and provides an AI-tractable, drift-aware semantic layer for future standards.