arXiv subjects

Shengli Zhang

Publications and source records attributed to Shengli Zhang.

At least 37 records · Page 2Linked to original sources

arXiv2025

DAO-Agent: Zero Knowledge-Verified Incentives for Decentralized Multi-Agent Coordination

Autonomous Large Language Model (LLM)-based multi-agent systems have emerged as a promising paradigm for facilitating cross-application and cross-organization collaborations. These autonomous agents often operate in trustless environments, where centralized coordination faces significant challenges, such as the inability to ensure transparent contribution measurement and equitable incentive distribution. While blockchain is frequently proposed as a decentralized coordination platform, it inherently introduces high on-chain computation costs and risks exposing sensitive execution information of the agents. Consequently, the core challenge lies in enabling auditable task execution and fair incentive distribution for autonomous LLM agents in trustless environments, while simultaneously preserving their strategic privacy and minimizing on-chain costs. To address this challenge, we propose DAO-Agent, a novel framework that integrates three key technical innovations: (1) an on-chain decentralized autonomous organization (DAO) governance mechanism for transparent coordination and immutable logging; (2) a ZKP mechanism approach that enables Shapley-based contribution measurement off-chain, and (3) a hybrid on-chain/off-chain architecture that verifies ZKP-validated contribution measurements on-chain with minimal computational overhead. We implement DAO-Agent and conduct end-to-end experiments using a crypto trading task as a case study. Experimental results demonstrate that DAO-Agent achieves up to 99.9% reduction in verification gas costs compared to naive on-chain alternatives, with constant-time verification complexity that remains stable as coalition size increases, thereby establishing a scalable foundation for agent coordination in decentralized environments.

cs.MA↗

arXiv2025

Binding Agent ID: Unleashing the Power of AI Agents with accountability and credibility

Autonomous AI agents lack traceable accountability mechanisms, creating a fundamental dilemma where systems must either operate as ``downgraded tools'' or risk real-world abuse. This vulnerability stems from the limitations of traditional key-based authentication, which guarantees neither the operator's physical identity nor the agent's code integrity. To bridge this gap, we propose BAID (Binding Agent ID), a comprehensive identity infrastructure establishing verifiable user-code binding. BAID integrates three orthogonal mechanisms: local binding via biometric authentication, decentralized on-chain identity management, and a novel zkVM-based Code-Level Authentication protocol. By leveraging recursive proofs to treat the program binary as the identity, this protocol provides cryptographic guarantees for operator identity, agent configuration integrity, and complete execution provenance, thereby effectively preventing unauthorized operation and code substitution. We implement and evaluate a complete prototype system, demonstrating the practical feasibility of blockchain-based identity management and zkVM-based authentication protocol.

cs.NI↗

arXiv2025

HiveMind: Contribution-Guided Online Prompt Optimization of LLM Multi-Agent Systems

Recent advances in LLM-based multi-agent systems have demonstrated remarkable capabilities in complex decision-making scenarios such as financial trading and software engineering. However, evaluating each individual agent's effectiveness and online optimization of underperforming agents remain open challenges. To address these issues, we present HiveMind, a self-adaptive framework designed to optimize LLM multi-agent collaboration through contribution analysis. At its core, HiveMind introduces Contribution-Guided Online Prompt Optimization (CG-OPO), which autonomously refines agent prompts based on their quantified contributions. We first propose the Shapley value as a grounded metric to quantify each agent's contribution, thereby identifying underperforming agents in a principled manner for automated prompt refinement. To overcome the computational complexity of the classical Shapley value, we present DAG-Shapley, a novel and efficient attribution algorithm that leverages the inherent Directed Acyclic Graph structure of the agent workflow to axiomatically prune non-viable coalitions. By hierarchically reusing intermediate outputs of agents in the DAG, our method further reduces redundant computations, and achieving substantial cost savings without compromising the theoretical guarantees of Shapley values. Evaluated in a multi-agent stock-trading scenario, HiveMind achieves superior performance compared to static baselines. Notably, DAG-Shapley reduces LLM calls by over 80\% while maintaining attribution accuracy comparable to full Shapley values, establishing a new standard for efficient credit assignment and enabling scalable, real-world optimization of multi-agent collaboration.

cs.MA↗

arXiv2025

VeriLoRA: Fine-Tuning Large Language Models with Verifiable Security via Zero-Knowledge Proofs

Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy, particularly in untrusted environments. Although parameter-efficient methods like Low-Rank Adaptation (LoRA) significantly reduce resource requirements, ensuring the security and verifiability of fine-tuning under zero-knowledge constraints remains an unresolved challenge. To address this, we introduce VeriLoRA, the first framework to integrate LoRA fine-tuning with zero-knowledge proofs (ZKPs), achieving provable security and correctness. VeriLoRA employs advanced cryptographic techniques -- such as lookup arguments, sumcheck protocols, and polynomial commitments -- to verify both arithmetic and non-arithmetic operations in Transformer-based architectures. The framework provides end-to-end verifiability for forward propagation, backward propagation, and parameter updates during LoRA fine-tuning, while safeguarding the privacy of model parameters and training data. Leveraging GPU-based implementations, VeriLoRA demonstrates practicality and efficiency through experimental validation on open-source LLMs like LLaMA, scaling up to 13 billion parameters. By combining parameter-efficient fine-tuning with ZKPs, VeriLoRA bridges a critical gap, enabling secure and trustworthy deployment of LLMs in sensitive or untrusted environments.

cs.CR↗

arXiv2025

Proactive DDoS Detection and Mitigation in Decentralized Software-Defined Networking via Port-Level Monitoring and Zero-Training Large Language Models

Centralized Software-Defined Networking (cSDN) offers flexible and programmable control of networks but suffers from scalability and reliability issues due to its reliance on centralized controllers. Decentralized SDN (dSDN) alleviates these concerns by distributing control across multiple local controllers, yet this architecture remains highly vulnerable to Distributed Denial-of-Service (DDoS) attacks. In this paper, we propose a novel detection and mitigation framework tailored for dSDN environments. The framework leverages lightweight port-level statistics combined with prompt engineering and in-context learning, enabling the DeepSeek-v3 Large Language Model (LLM) to classify traffic as benign or malicious without requiring fine-tuning or retraining. Once an anomaly is detected, mitigation is enforced directly at the attacker's port, ensuring that malicious traffic is blocked at their origin while normal traffic remains unaffected. An automatic recovery mechanism restores normal operation after the attack inactivity, ensuring both security and availability. Experimental evaluation under diverse DDoS attack scenarios demonstrates that the proposed approach achieves near-perfect detection, with 99.99% accuracy, 99.97% precision, 100% recall, 99.98% F1-score, and an AUC of 1.0. These results highlight the effectiveness of combining distributed monitoring with zero-training LLM inference, providing a proactive and scalable defense mechanism for securing dSDN infrastructures against DDoS threats.

cs.CR↗

arXiv2025

Agentic Graph Neural Networks for Wireless Communications and Networking Towards Edge General Intelligence: A Survey

The rapid advancement of communication technologies has driven the evolution of communication networks towards both high-dimensional resource utilization and multifunctional integration. This evolving complexity poses significant challenges in designing communication networks to satisfy the growing quality-of-service and time sensitivity of mobile applications in dynamic environments. Graph neural networks (GNNs) have emerged as fundamental deep learning (DL) models for complex communication networks. GNNs not only augment the extraction of features over network topologies but also enhance scalability and facilitate distributed computation. However, most existing GNNs follow a traditional passive learning framework, which may fail to meet the needs of increasingly diverse wireless systems. This survey proposes the employment of agentic artificial intelligence (AI) to organize and integrate GNNs, enabling scenario- and task-aware implementation towards edge general intelligence. To comprehend the full capability of GNNs, we holistically review recent applications of GNNs in wireless communications and networking. Specifically, we focus on the alignment between graph representations and network topologies, and between neural architectures and wireless tasks. We first provide an overview of GNNs based on prominent neural architectures, followed by the concept of agentic GNNs. Then, we summarize and compare GNN applications for conventional systems and emerging technologies, including physical, MAC, and network layer designs, integrated sensing and communication (ISAC), reconfigurable intelligent surface (RIS) and cell-free network architecture. We further propose a large language model (LLM) framework as an intelligent question-answering agent, leveraging this survey as a local knowledge base to enable GNN-related responses tailored to wireless communication research.

eess.SP↗

arXiv2025

EmbedGrad: Gradient-Based Prompt Optimization in Embedding Space for Large Language Models

Effectively adapting powerful pretrained foundation models to diverse tasks remains a key challenge in AI deployment. Current approaches primarily follow two paradigms:discrete optimization of text prompts through prompt engineering, or continuous adaptation via additional trainable parameters. Both exhibit limitations-discrete methods lack refinement precision while parameter-based techniques increase complexity and reduce interpretability. To address these constraints, we propose EmbedGrad, a novel framework that optimizes text prompt embeddings through gradient-based refinement. Our approach uniquely decouples training from deployment:during optimization,labeled examples guide precise embedding adjustments while preserving semantic meaning; during inference, only optimized embeddings integrate with user queries. This enables fine-grained calibration impossible in text space, such as enhancing the reasoning capability of prompts like please reason step by step. Comprehensive evaluations across mathematical reasoning, sentiment analysis, and causal judgment tasks demonstrate EmbedGrad's effectiveness:optimizing this reasoning prompt for Qwen2.5-Math-1.5B increased accuracy from 14.74\% to 58.96\% on mathematical problems. Consistent improvements were observed across model scales (0.5B-14B) and all tasks, with particularly significant gains for smaller models on complex problems like causal judgment. By bridging prompt engineering and parameter efficiency without architectural changes, our work establishes embedding refinement as a powerful new paradigm for task adaptation.

cs.CL↗

arXiv2025

Configurational Entropy and Its Scaling Behavior in Lattice Systems with Number of States Defined by Coordination Numbers

We introduce an exactly solvable lattice model that reveals a universal finite-size scaling law for configurational entropy driven purely by geometry. Using exact enumeration via Burnside's lemma, we compute the entropy for diverse 1D, 2D, and 3D lattices, finding that the deviation from the thermodynamic limit $s_{\infty} = \ln (z)$ scales as $Δs_{N} \sim N^{-1/d}$, with lattice-dependent higher-order corrections. This scaling, observed across structures from chains to FCC and diamond lattices, offers a minimal framework to quantify geometric influences on entropy. The model captures the order of magnitude of experimental residual entropies (e.g., $S_{\mathrm{molar}} = R \ln 12 \approx 20.7 \, \mathrm{J/mol \cdot K}$) and provides a reference for understanding entropy-driven order in colloids, clusters, and solids.

cond-mat.stat-mech↗

arXiv2025

Computational Design of Two-Dimensional MoSi$_2$N$_4$ Family Field-Effect Transistor for Future Ångström-Scale CMOS Technology Nodes

Advancing complementary metal-oxide-semiconductor (CMOS) technology into the sub-1-nm angström-scale technology nodes is expected to involve alternative semiconductor channel materials, as silicon transistors encounter severe performance degradation at physical gate lengths below 10 nm. Two-dimensional (2D) semiconductors have emerged as strong candidates for overcoming short-channel effects due to their atomically thin bodies, which inherently suppress electrostatic leakage and improve gate control in aggressively scaled field-effect transistors (FETs). Among the growing library of 2D materials, the MoSi$_2$N$_4$ family -- a synthetic septuple-layered materials -- has attracted increasing attention for its remarkable ambient stability, suitable bandgaps, and favorable carrier transport characteristics, making it a promising platform for next-generation transistors. While experimental realization of sub-10-nm 2D FETs remains technologically demanding, computational device simulation using first-principles density functional theory combined with nonequilibrium Green's function transport simulations provide a powerful and cost-effective route for exploring the performance limits and optimal design of ultrascaled FET. This review consolidates the current progress in the computational design of MoSi$_2$N$_4$ family FETs. We review the physical properties of MoSi$_2$N$_4$ that makes them compelling candidates for transistor applications, as well as the simulated device performance and optimization strategy of MoSi$_2$N$_4$ family FETs. Finally, we identify key challenges and research gaps, and outline future directions that could accelerate the practical deployment of MoSi$_2$N$_4$ family FET in the angström-scale CMOS era.

cond-mat.mtrl-sci↗

arXiv2025

A Study of Semi-Fungible Token based Wi-Fi Access Control

Current Wi-Fi authentication methods face issues such as insufficient security, user privacy leakage, high management costs, and difficulty in billing. To address these challenges, a Wi-Fi access control solution based on blockchain smart contracts is proposed. Firstly, semi-fungible Wi-Fi tokens (SFWTs) are designed using the ERC1155 token standard as credentials for users to access Wi-Fi. Secondly, a Wi-Fi access control system based on SFWTs is developed to securely verify and manage the access rights of Wi-Fi users. Experimental results demonstrate that SFWTs, designed based on the ERC1155 standard, along with the SFWT access right verification process, can significantly reduce Wi-Fi operating costs and authentication time, effectively meeting users' needs for safe and convenient Wi-Fi access.

cs.CR↗

arXiv2025

Dynamic Spectrum Sharing Based on the Rentable NFT Standard ERC4907

Centralized Dynamic Spectrum Sharing (DSS) faces challenges like data security, high management costs, and limited scalability. To address these issues, a blockchain-based DSS scheme has been proposed in this paper. First, we utilize the ERC4907 standard to mint Non-Fungible Spectrum Tokens (NFSTs) that serve as unique identifiers for spectrum resources and facilitate renting. Next, we develop a smart contract for NFST auctions, ensuring secure spectrum transactions through the auction process. Lastly, we create a Web3 spectrum auction platform where users can access idle spectrum data and participate in auctions for NFST leases corresponding to the available spectrum. Experimental results demonstrate that our NFST, designed according to the ERC4907 standard, effectively meets users' secure and efficient DSS requirements, making it a feasible solution.

cs.NI↗

arXiv2025

ExClique: An Express Consensus Algorithm for High-Speed Transaction Process in Blockchains

Proof of Authority (PoA) plays a pivotal role in blockchains for reaching consensus. Clique, which selects consensus nodes to generate blocks with a pre-determined order, is the most popular implementation of PoA due to its low communication overhead and energy consumption. However, our study unveils that the speed to process transactions by Clique is severely restricted by 1) the long communication delay of full blocks (each containing a certain number of transactions) between consensus nodes; and 2) occurrences of no-turn blocks, generated by no-turn nodes if an in-turn block generation fails. Consequently, Clique struggles to support distributed applications requiring a high transaction processing speed, e.g., online gaming. To overcome this deficiency, we propose an Express Clique (ExClique) algorithm by improving Clique from two perspectives: compacting blocks for broadcasting to shorten communication delay and prohibiting the occurrences of no-turn blocks. For performance evaluation, we implement ExClique by modifying Geth of Ethereum, the software implementing Clique, and deploy a permissioned blockchain network by using container technology. The experimental results show that ExClique achieves a substantial enhancement in transactions per second (TPS). Specifically, it boosts TPS by 2.25X in a typical network with 21 consensus nodes and an impressive 7.01X in a large-scale network with 101 consensus nodes when compared to Clique.

cs.DC↗

arXiv2024

Unseen Attack Detection in Software-Defined Networking Using a BERT-Based Large Language Model

Software defined networking (SDN) represents a transformative shift in network architecture by decoupling the control plane from the data plane, enabling centralized and flexible management of network resources. However, this architectural shift introduces significant security challenges, as SDN's centralized control becomes an attractive target for various types of attacks. While current research has yielded valuable insights into attack detection in SDN, critical gaps remain. Addressing challenges in feature selection, broadening the scope beyond DDoS attacks, strengthening attack decisions based on multi flow analysis, and building models capable of detecting unseen attacks that they have not been explicitly trained on are essential steps toward advancing security in SDN. In this paper, we introduce a novel approach that leverages Natural Language Processing (NLP) and the pre trained BERT base model to enhance attack detection in SDN. Our approach transforms network flow data into a format interpretable by language models, allowing BERT to capture intricate patterns and relationships within network traffic. By using Random Forest for feature selection, we optimize model performance and reduce computational overhead, ensuring accurate detection. Attack decisions are made based on several flows, providing stronger and more reliable detection of malicious traffic. Furthermore, our approach is specifically designed to detect previously unseen attacks, offering a solution for identifying threats that the model was not explicitly trained on. To rigorously evaluate our approach, we conducted experiments in two scenarios: one focused on detecting known attacks, achieving 99.96% accuracy, and another on detecting unseen attacks, where our model achieved 99.96% accuracy, demonstrating the robustness of our approach in detecting evolving threats to improve the security of SDN networks.

cs.CR↗

arXiv2024

Programming on Bitcoin: A Survey of Layer 1 and Layer 2 Technologies in Bitcoin Ecosystem

This paper surveys innovative protocols that enhance the programming functionality of the Bitcoin blockchain, a key part of the "Bitcoin Ecosystem." Bitcoin utilizes the Unspent Transaction Output (UTXO) model and a stack-based script language for efficient peer-to-peer payments, but it faces limitations in programming capability and throughput. The 2021 Taproot upgrade introduced the Schnorr signature algorithm and P2TR transaction type, significantly improving Bitcoin's privacy and programming capabilities. This upgrade has led to the development of protocols like Ordinals, Atomicals, and BitVM, which enhance Bitcoin's programming functionality and enrich its ecosystem. We explore the technical aspects of the Taproot upgrade and examine Bitcoin Layer 1 protocols that leverage Taproot's features to program non-fungible tokens (NFTs) into transactions, including Ordinals and Atomicals, along with the fungible token standards BRC-20 and ARC-20. Additionally, we categorize certain Bitcoin ecosystem protocols as Layer 2 solutions similar to Ethereum's, analyzing their impact on Bitcoin's performance. By analyzing data from the Bitcoin blockchain, we gather metrics on block capacity, miner fees, and the growth of Taproot transactions. Our findings confirm the positive effects of these protocols on Bitcoin's mainnet, bridging gaps in the literature regarding Bitcoin's programming capabilities and ecosystem protocols and providing valuable insights for practitioners and researchers.

cs.CR↗

arXiv2024

Implementing NAT Hole Punching with QUIC

The widespread adoption of Network Address Translation (NAT) technology has led to a significant number of network end nodes being located in private networks behind NAT devices, impeding direct communication between these nodes. To solve this problem, a technique known as "hole punching" has been devised for NAT traversal to facilitate peer-to-peer communication among end nodes located in distinct private networks. However, as the increasing demands for speed and security in networks, TCP-based hole punching schemes gradually show performance drawbacks. Therefore, we present a QUIC-based hole punching scheme for NAT traversal. Through a comparative analysis of the hole punching time between QUIC-based and TCP based protocols, we find that the QUIC-based scheme effectively reduces the hole punching time, exhibiting a pronounced advantage in weak network environments. Furthermore, in scenarios where the hole punched connection is disrupted due to factors such as network transitions or NAT timeouts, this paper evaluates two schemes for restoring the connection: QUIC connection migration and re-punching. Our results show that QUIC connection migration for connection restoration saves 2 RTTs compared to QUIC re-punching, and 3 RTTs compared to TCP re-punching, effectively reducing the computational resources consumption for re-punching.

cs.NI↗

arXiv2024

Decentralized Physical Infrastructure Network (DePIN): Challenges and Opportunities

The widespread use of the Internet has posed challenges to existing centralized physical infrastructure networks. Issues such as data privacy risks, service disruptions, and substantial expansion costs have emerged. To address these challenges, an innovative network architecture called Decentralized Physical Infrastructure Network (DePIN) has emerged. DePIN leverages blockchain technology to decentralize the control and management of physical devices, addressing limitations of traditional infrastructure network. This article provides a comprehensive exploration of DePIN, presenting its five-layer architecture, key design principles. Furthermore, it presents a detailed survey of the extant applications, operating mechanisms, and provides an in-depth analysis of market data pertaining to DePIN. Finally, it discusses a wide range of the open challenges faced by DePIN.

cs.NI↗

arXiv2024

A deep-learning-based MAC for integrating channel access, rate adaptation and channel switch

With increasing density and heterogeneity in unlicensed wireless networks, traditional MAC protocols, such as carrier-sense multiple access with collision avoidance (CSMA/CA) in Wi-Fi networks, are experiencing performance degradation. This is manifested in increased collisions and extended backoff times, leading to diminished spectrum efficiency and protocol coordination. Addressing these issues, this paper proposes a deep-learning-based MAC paradigm, dubbed DL-MAC, which leverages spectrum sensing data readily available from energy detection modules in wireless devices to achieve the MAC functionalities of channel access, rate adaptation and channel switch. First, we utilize DL-MAC to realize a joint design of channel access and rate adaptation. Subsequently, we integrate the capability of channel switch into DL-MAC, enhancing its functionality from single-channel to multi-channel operation. Specifically, the DL-MAC protocol incorporates a deep neural network (DNN) for channel selection and a recurrent neural network (RNN) for the joint design of channel access and rate adaptation. We conducted real-world data collection within the 2.4 GHz frequency band to validate the effectiveness of DL-MAC, and our experiments reveal that DL-MAC exhibits superior performance over traditional algorithms in both single and multi-channel environments and also outperforms single-function approaches in terms of overall performance. Additionally, the performance of DL-MAC remains robust, unaffected by channel switch overhead within the evaluated range.

cs.NI↗

arXiv2024

DEthna: Accurate Ethereum Network Topology Discovery with Marked Transactions

In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schemes cannot accurately infer the Ethereum network topology with a low cost. To address this challenge, we propose the Distributed Ethereum Network Analyzer (DEthna) tool, which can accurately and efficiently measure the Ethereum network topology. In DEthna, a novel parallel measurement model is proposed that can generate marked transactions to infer link connections based on the transaction replacement and propagation mechanism in Ethereum. Moreover, a workload offloading scheme is designed so that DEthna can be deployed on multiple distributed probing nodes so as to measure a large-scale Ethereum network at a low cost. We run DEthna on Goerli (the most popular Ethereum test network) to evaluate its capability in discovering network topology. The experimental results demonstrate that DEthna significantly outperforms the state-of-the-art baselines. Based on DEthna, we further analyze characteristics of the Ethereum network revealing that there exist more than 50% low-degree Ethereum nodes that weaken the network robustness.

cs.NI↗