arXiv:2601.19915v1 Announce Type: new Abstract: We introduce the emph{Arrow Language Model}, a neural architecture derived from an intuitionistic-logic interpretation of next-token prediction. Instead of representing tokens as additive embeddings mixed by attention, we encode a prefix as a emph{left-nested implication chain} whose structure preserves order through non-commutative composition. Next-token prediction corresponds to emph{modus ponens}, and sequence processing becomes constructive proof extension under the Curry–Howard correspondence. Our Prolog-based specialized theorem provers validate fundamental properties of the neural models, among […]

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This paper establishes the Quantum Voronovskaya–Damasclin (QVD) Theorem, providing a complete asymptotic characterization of Quantum Neural Network Operators in the approximation of arbitrary quantum channels. The result extends the classical Voronovskaya theorem from scalar approximation to the non-commutative operator framework of quantum information theory. We introduce rigorous quantum analogues of Sobolev and Hölder spaces defined through Fréchet differentiability in the Liouville representation and measured using the completely bounded (diamond) norm. Within this framework, we derive an explicit asymptotic […]

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Deep dive into a product that had the world in its clutches just a week ago, and now that the noise has died out a bit, lets actually explore if it is worth your while – regardless of who you are. A bit of history and setting the stage So far, it seems that the tech world – specifically the field of AI – is making insane progress in whatever it touches. But if you zoom out, most […]

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arXiv:2602.17674v1 Announce Type: new Abstract: When AI systems summarize and relay information, they inevitably transform it. But how? We introduce an experimental paradigm based on the telephone game to study what happens when AI talks to AI. Across five studies tracking content through AI transmission chains, we find three consistent patterns. The first is convergence, where texts differing in certainty, emotional intensity, and perspectival balance collapse toward a shared default of moderate confidence, muted affect, and analytical structure. […]

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Humanity, Teilhard argues, is awakening in an evolutionary storm—one pulling us toward a larger, more unified “Super-Humanity.” In this widening scale of mind and world, Christ emerges not as a distant memory but as the universe’s glowing center, the Omega drawing all things together. From this cosmic magnetism arises a “Super-Charity”: love powerful enough to drive evolution itself.

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arXiv:2602.15074v1 Announce Type: new Abstract: We introduce a structure-aware approach for symbolic piano accompaniment that decouples high-level planning from note-level realization. A lightweight transformer predicts an interpretable, per-measure style plan conditioned on section/phrase structure and functional harmony, and a retriever then selects and reharmonizes human-performed piano patterns from a corpus. We formulate retrieval as pattern matching under an explicit energy with terms for harmonic feasibility, structural-role compatibility, voice-leading continuity, style preferences, and repetition control. Given a structured lead […]

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arXiv:2602.15278v1 Announce Type: new Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs). These agents make visual decisions at scale, deciding what to click, recommend, or buy. Yet, we know little about the structure of their visual preferences. We introduce a framework for studying this by placing VLMs in controlled image-based choice tasks and systematically perturbing their inputs. Our key idea is to treat the […]

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arXiv:2602.13307v1 Announce Type: new Abstract: Cooperative edge caching in overlapping zones creates intricate coupling among Base Station (BS) decisions, making content replacement highly sensitive to topology and temporal reuse. While heuristics are often myopic and Deep Reinforcement Learning lacks robustness under dynamics, this paper proposes a Large Language Model (LLM)-based multi-BS orchestrator. The LLM acts as the sole autonomous engine, interacting with the environment via a validated text-to-action interface. Each time slot, the system renders environmental states — […]

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arXiv:2505.19013v2 Announce Type: replace-cross Abstract: Data Shapley is an important tool for data valuation, which quantifies the contribution of individual data points to machine learning models. In practice, group-level data valuation is desirable when data providers contribute data in batch. However, we identify that existing group-level extensions of Data Shapley are vulnerable to shell company attacks, where strategic group splitting can unfairly inflate valuations. We propose Faithful Group Shapley Value (FGSV) that uniquely defends against such attacks. Building […]

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Reinforcement Learning with Verifiable Rewards (RLVR) is commonly based on group sampling to estimate advantages and stabilize policy updates. In practice, large group sizes are not feasible due to computational limits, which biases learning toward trajectories that are already likely. Smaller groups often miss rare-correct trajectories while still containing mixed rewards, concentrating probability on common solutions. We derive the probability that updates miss rare-correct modes as a function of group size, showing non-monotonic behavior, and characterize how updates […]

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