Realizing endogenous narrative evolution in LLM-based multi-agent systems is hindered by the inherent stochasticity of generative emergence.In particular, long-horizon simulations suffer from social memory stacking, where conflicting relational states accumulate without resolution, and narrative-spatial dissonance, where spatial logic detaches from the evolving plot.To bridge this gap, we propose EVOSPARK, a framework specifically designed to sustain logically coherent long-horizon narratives within endogenous interactive agent societies.To ensure consistency, the stratified narrative memory employs a role socio-evolutionary base as living cognition, dynamically metabolizing experiences to resolve historical conflicts.Complementarily, a Generative Mise-en-Scène mechanism enforces role-location-plot alignment, synchronizing character presence with the narrative flow.Underpinning these is the unified narrative operation engine, which integrates an Emergent Character Grounding Protocol to transform stochastic sparking into persistent characters.This engine establishes a substrate that expands a minimal premise into an open-ended, evolving story world.Experiments demonstrate that EVOSPARK significantly outperforms baselines across diverse paradigms, enabling the sustained generation of expressive and coherent narrative experiences.

This paper investigates the feasibility of achieving zero-knowledge verifiability for graph databases, enabling database owners to cryptographically prove the query execution correctness without disclosing the underlying data. Although similar capabilities have been explored for relational databases, their implementation for graph databases presents unique challenges. This is mainly attributed to the relatively large complexity of queries in graph databases. When translating graph queries into arithmetic circuits, the circuit scale can be too large to be practically evaluated. To address this issue, we propose to break down graph queries into more fine-grained, primitive operators, enabling a step-by-step evaluation through smaller-scale circuits. Accordingly, the verification with ZKP circuits of complex graph queries can be decomposed into a series of composable cryptographic primitives, each designed to verify a fundamental structural property such as path ordering or edge directionality. Especially, having noticed that the graph expansion (i.e., traversing from nodes to their neighbors along edges) operation serves as the backbone of graph query evaluation, we design the expansion centric operator decomposition. In addition to constructing circuits for the expansion primitives, we also design specialized ZKP circuits for the various attributes that augment this traversal. The circuits are meticulously designed to take advantage of PLONKish arithmetization. By integrating these optimized circuits, we implement ZKGraph, a system that provides verifiable query processing while preserving data privacy. Performance evaluation indicates that ZKGraph significantly outperforms naive in circuit implementations of graph operators, achieving substantial improvements in both runtime and memory consumption.

ABSTRACT Context Open‐source communities have become a crucial driver of technological innovation and developer growth. While numerous deep learning‐based recommender systems exist, they often fail to provide accurate recommendations for identifying suitable developers who meet the task requirements of project development due to project popularity imbalance and developer ability bias. Recent advances in retrieval‐augmented generation (RAG) have shown promise in enhancing data augmentation and personalization for recommender systems in scenarios with data sparsity. Objective This study aims to develop RGPRec, a RAG‐enhanced graph neural network (GNN) for personalized, debiased task recommendations in open‐source communities. The model targets task recommendation quality, fairness, and coverage capabilities for long‐tail developers. Methods RGPRec leverages RAG‐enhanced large language models (LLMs) to retrieve project‐related documents and enriched developer attributes to improve node representations through ego graph structural learning. It also recognizes the importance of long‐tail developers—less involved in projects but still contributing significantly—often overlooked by traditional recommender systems. Moreover, RGPRec reasonably evaluates each developer's ability through a multilabel assessment based on an ego GNN to calculate a debiased rating, generating more personalized and debiased task recommendations. Results Evaluation using real‐world data from two famous open‐source communities of SourceForge and GitHub, indicates that RGPRec significantly outperforms state‐of‐the‐art (SOTA) approaches in rating and ranking performances. In addition, ablation studies demonstrate the necessity of each component in the RGPRec model. Conclusion RGPRec effectively enhances task recommendation quality in open‐source communities. By integrating RAG and LLMs with GNNs, RGPRec achieves superior data augmentation and personalization compared to traditional approaches. Therefore, RGPRec can be a valuable tool for promoting inclusiveness and efficiency in collaborations within open‐source communities.

Mobile edge computing (MEC) is an emerging computing paradigm receiving growing attention. MEC significantly reduces latency by processing user requests on edge servers rather than cloud centers, making it ideal for real-time applications. However, due to resource limitations and user mobility, microservice requests may fail, especially when users move at high speeds. This paper introduces a new tool, MobileEdgeSim, to simulate microservice-oriented MEC environments. MobileEdgeSim integrates mobility prediction and service composition to enhance the pre-deployment of microservices. To evaluate MobileEdgeSim, we conducted a series of experiments comparing it to several state-of-the-art baseline approaches. We also conducted a user study to evaluate the tool’s effectiveness in real-world scenarios. Our results indicate that MobileEdgeSim significantly improves the success rate of both user requests and responses while reducing resource costs. MobileEdgeSim is available at https://github.com/ssea-lab/MobileEdgesim.