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This paper proposes 'Soul Computing', a theoretical framework for building intelligent agents with independent consciousness, distinguishing it from affective computing and traditional virtual humans, and outlines a hierarchical technical architecture and core challenges for implementation.
This paper argues that universal LLM reliability is impossible, but within operationally bounded patches (e.g., legal review, medical RAG), failures are sparse and repetitive, making reliability a local catalogue-discovery problem. It formalizes this with propositions and a corollary, relocating rather than dissolving the difficulty of long-context generation.
This paper proposes a structural and dynamical framework for modeling cognitive processes using iterative state transformations and semantic equivalence, integrating dynamical systems, category theory, and feedback mechanisms to model cognition as a process evolving toward stable interpretations.
This paper proposes Human-Centered Learning Mechanics (HCLM), a dynamical and information-theoretic framework for studying open and controlled learning systems. It formalizes entropy regularization through effective information force, derives convergence and generalization results, and provides a conditional interpretation of scaling-law behavior.
The NOVA framework models the 'generate, verify, accumulate, retrain' loop as an adaptive sampling process over a knowledge space, identifying failure modes and proving a scaling law for cumulative generation cost under Zipf-like discovery distributions.
This paper introduces a unified geometric framework showing that weighted InfoNCE objectives can be interpreted as Distance Geometry Problems, providing exact characterizations of optimal embeddings for supervised and weakly supervised contrastive learning methods and revealing when such embeddings are geometrically realizable, degenerate, or inconsistent.
This paper introduces PLACO, a framework for selecting cost-effective subsets of humans to collaborate with AI models in classification tasks, balancing performance and human labeling costs.
This arXiv preprint introduces GRALIS, a unified mathematical framework using Riesz Representation Theory to formalize and compare linear attribution methods like SHAP, LIME, and Integrated Gradients.
Introduces MidSteer, a theoretical framework for concept steering in generative models, bridging the gap between empirical success and theoretical understanding by providing optimal affine transformations for steering, erasing, and switching concepts in LLMs and vision diffusion models.