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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Constructing Fundamentals for the Theory of Proportions a...
Diogen Babuc · 2025-02-02 · via cs.IT updates on arXiv.org

The Theory of Proportions and Symbolic Allusions applied Interdisciplinary (TPASAI) is a framework that integrates mathematics, linguistics, psychology, and game theory to uncover hidden patterns and proportions in reality. Its central idea is that numerical encoding of symbols, dates, and language can reveal recurring structures and connections that reflect universal principles. By applying fractal analysis, the theory identifies patterns across different scales, offering a unifying perspective on the structure of the world. One key aspect of TPASAI is symbolic analysis, which allows for the reinterpretation of traumatic experiences in psychotherapy. For example, assigning numerical values to elements like fingers, dates, or words can help individuals uncover meaningful associations between personal experiences and collective symbols. This approach encourages cognitive flexibility and provides a therapeutic avenue for recontextualizing emotions. The theory also incorporates principles of game theory, which frame reality as a system of symbolic "codes" governed by rules that can be understood and strategically used. This perspective is especially useful for psychological conditions like obsessive-compulsive disorder (OCD), enabling patients to approach their obsessions as decipherable patterns rather than rigid constraints. TPASAI has practical applications in psychology, education, and technology. In education, it aids in teaching mathematical and linguistic concepts by exploring connections between symbolic representations and real-world events. In technology, the methodology can be employed in ciphering and natural language processing. The innovation of TPASAI lies in its ability to merge the structured rigor of mathematics with the interpretative flexibility of symbolic analysis, offering a deeper understanding of events and relationships.