Table of Links Abstract and 1. Introduction Related Work Preliminaries and Notations Differentiable Structural Information 4.1. A New Formulation 4.2. Properties 4.3. Differentiability & Deep Graph Clustering LSEnet 5.1. Embedding Leaf Nodes 5.2. Learning Parent Nodes 5.3. Hyperbolic Partitioning Tree Experiments 6.1. Graph Clustering 6.2. Discussion on Structural Entropy Conclusion, Broader Impact, and References Appendix A. Proofs B. Hyperbolic Space C. Technical Details D. Additional Results 5.1. Embedding Leaf Nodes where ⊙ is the Hadamard product, masking the […]

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arXiv:2603.03383v1 Announce Type: new Abstract: To mitigate the Memory Wall bottleneck encountered by Large Language Models (LLMs) during inference on textbf{NPU} hardware, and addressing the scarcity of native support for mainstream speculative decoding algorithms on domestic infrastructure, this study presents an end-to-end speculative inference acceleration scheme for OpenPangu-7B.

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arXiv:2602.06194v1 Announce Type: new Abstract: Asynchronous, text-based discourse-such as students’ posts in discussion forums-is widely used to support collaborative learning. However, the distributed and evolving nature of such discourse often makes it difficult to see how ideas connect, develop, and build on one another over time. As a result, learners may struggle to recognize relationships among ideas-a process that is critical for idea advancement in productive collaborative discourse. To address this challenge, we explore how large language models […]

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Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but […]

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Shiba Inu has generated about 150,000X returns since its launch in August 2020 and PEPE has paid out more than 46,000X to first entrants since April 2023. Both the tokens presently trade at multi-billion dollars valuations that deny them any similar percentage gains to the present value. SHIB has a market cap of over $5B, and PEPE has approximately $2.7B which puts them in mathematical limits regarding the possibility of massive future growth. Cryptocurrency whale wallets have distinctive […]

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arXiv:2601.07988v1 Announce Type: new Abstract: While NLP typically treats documents as independent and unordered samples, in longitudinal studies, this assumption rarely holds: documents are nested within authors and ordered in time, forming person-indexed, time-ordered $textit{behavioral sequences}$. Here, we demonstrate the need for and propose a longitudinal modeling and evaluation paradigm that consequently updates four parts of the NLP pipeline: (1) evaluation splits aligned to generalization over people ($textit{cross-sectional}$) and/or time ($textit{prospective}$); (2) accuracy metrics separating between-person differences from […]

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arXiv:2604.05080v1 Announce Type: new Abstract: We present Nidus, a governance runtime that mechanizes the V-model for AI-assisted software delivery. In the self-hosting deployment, three LLM families (Claude, Gemini, Codex) delivered a 100,000-line system under proof obligations verified against the current obligation set on every commit. The system governed its own construction. Engineering invariants – traced requirements, justified architecture, evidenced deliveries – cannot be reliably maintained as learned behavior; assurance requires enforcement by a mechanism external to the proposer. […]

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arXiv:2603.24639v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled the development of autonomous agents capable of complex reasoning and multi-step problem solving. However, these agents struggle to adapt to specialized environments and do not leverage past interactions, approaching each new task from scratch regardless of their accumulated experience. We introduce Experiential Reflective Learning (ERL), a simple self-improvement framework that enables rapid environment adaptation through experiential learning. ERL reflects on task trajectories and outcomes […]

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