February 2026

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 2. Related Work Deep Graph Clustering. Deep models have achieved state-of-the-art results […]

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This study develops a Constraint-Driven Model of Intelligence to explain the emergence of structured meaning in complex systems, reconciling probability and cybernetics. Building on Émile Borel’s Infinite Monkey Theorem, which illustrates the theoretical inevitability of order through unbounded stochastic processes, and Gregory Bateson’s principle of negative explanation, which frames structure as the consequence of systematically eliminated alternatives, the analysis formalizes how constraints break ergodicity and generate asymmetry. Shannon’s entropy quantifies the informational effects of constraints, while Simon’s bounded […]

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The aim of the article is to introduce a few variants of generalized quasi-continuity of multifunctions defined on a bitopological space and to study their mutual relationship. The results known for functions are extended to multifunctions which provide a wider range of relationships, mainly in terms of upper and lower semi continuities and corresponding continuities with respect to a dual bitopology. The proof procedures are based on a notion of pseudo refinement of two topologies and the Baire […]

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This study formulates collaborative large language model (LLM) agents as a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) and optimizes group behavior using centralized training with decentralized execution (CTDE). A group-relative policy optimization (GRPO) objective is introduced to jointly optimize solution quality, coordination consistency, and response latency. Experiments are conducted on collaborative writing and collaborative coding benchmarks comprising 6,000 multi-agent episodes with 2–4 agents per task. Compared with single-agent and prompt-only collaboration baselines, the proposed approach achieves a […]

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Forest fire poses a significant global threat, endangering ecosystems, biodiversity and the harmony of natural habitat. As the forest fires becomes more frequent and intense, the loss of natural habitat has increased rapidly. Therefore, it becomes important to detect forest fire at an early stage so that prompt and effective measures can be implemented immediately. To address this, we propose a low-cost drone-based forest fire detection and monitoring system that utilizes computer vision and deep learning for real-time […]

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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 Abstract Graph clustering is a fundamental problem in machine learning. Deep learning […]

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Reinforcement learning has emerged as a promising paradigm for aligning diffusion and flow-matching models with human preferences, yet practitioners face fragmented codebases, model-specific implementations, and engineering complexity. We introduce Flow-Factory, a unified framework that decouples algorithms, models, and rewards through through a modular, registry-based architecture. This design enables seamless integration of new algorithms and architectures, as demonstrated by our support for GRPO, DiffusionNFT, and AWM across Flux, Qwen-Image, and WAN video models. By minimizing implementation overhead, Flow-Factory empowers […]

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Learning to coordinate many agents in partially observable and highly dynamic environments requires both informative representations and data-efficient training. To address this challenge, we present a novel model-based multi-agent reinforcement learning framework that unifies joint state-action representation learning with imaginative roll-outs. We design a world model trained with variational auto-encoders and augment the model using the state-action learned embedding (SALE). SALE is injected into both the imagination module that forecasts plausible future roll-outs and the joint agent network […]

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The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) has fostered a growing family of algorithms designed to solve large-scale multi-agent systems. However, the field currently lacks a standardized evaluation protocol, forcing researchers to rely on bespoke, isolated, and often simplistic environments. This fragmentation makes it difficult to assess the robustness, generalization, and failure modes of emerging methods. To address this gap, we propose a comprehensive benchmark suite for MFGs (Bench-MFG), focusing on the discrete-time, discrete-space, […]

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