Blockchain Papers

Hayatullah Hassanpour, Josue Obregon

Continuous-Time Dynamic Graphs (CTDGs) are essential for modeling event-driven dynamics in complex, evolving systems, ranging from streaming temporal knowledge graphs (tKGs) and real-time recommendation systems to decentralized finance (DeFi) networks. State-of-the-art temporal graph learning methods predominantly compress historical interactions into flat, one-dimensional state vectors. However, we demonstrate that this architectural choice suffers from severe structural interference, akin to catastrophic forgetting, in heterogeneous networks where entities maintain multiple concurrent relational identities (e.g., decentralized finance wallets acting simultaneously as lenders, swappers and borrowers). In this work, we propose DYG-LA (Dynamic Graph Learning via Linear Attention and Recurrent Matrix States), a novel architecture that resolves structural interference by expanding node memory into Matrix-Valued Hidden States (MVHS). Each node maintains a multi-head H × (D/H) × (D/H) state matrix, geometrically updated via asymmetric outer products and regulated by a selective, data-dependent Ebbinghaus decay. To overcome the O(L²) bottleneck of Transformer-based methods without succumbing to the random sampling paradox of pure sequence models (where nodes lose identity due to sparse or noisy temporal sampling), DYG-LA integrates a dual-memory approach. It pairs an RWKV-6 linear attention short-term temporal scanner with the long-term MVHS global memory. The architecture further incorporates a Dynamic Gated Fusion mechanism, effectively acting as an adaptive mixture-of-experts to route signals from the temporal scanner, spatial structure and memory. We evaluate DYG-LA across twelve benchmark datasets under transductive settings. Comprehensive ablation studies demonstrate that the dual-memory design is critical for complex, heterogeneous networks, with the full model achieving state-of-the-art performance.

Leon Calvin II long

Blockchain Monetary Substrate Architecture — Cross‑Ledger Anchoring, XRP/XLM Interoperability, Deterministic Metadata Systems, and Multi‑Rail Asset Issuance Description: This collection contains the complete blockchain‑focused patent estate, mathematical frameworks, and reduction‑to‑practice archives developed by Leon Calvin Long II. The work establishes a unified, sovereign‑grade monetary substrate designed for cross‑ledger settlement, gold‑backed digital asset issuance, cryptographic solvency verification, and deterministic metadata lifecycle management across distributed systems. The architecture integrates: XRP/XLM cross‑chain bridges ISO 20022‑aligned messaging Universal Scribe Capsule encoding Ordinal Satoshi targeting Taproot‑anchored state commitments Gas‑free off‑chain asset transfer (THREADZERO) Deterministic cross‑ledger data state anchoring Topological Geometric Calculus (TGC) reconstruction engine Kolmogorov–Shannon parity bridges Multi‑rail computational architecture All documents in this collection are timestamped, citable, and globally published through Zenodo, ensuring permanent public disclosure and protection of intellectual property. This collection forms the technical foundation for a unified blockchain‑enabled monetary policy substrate. U.S. Provisional Patent Applications 63/847,811 Reinforcement Learning‑Based Token Minting and Cross‑Chain Cryptographic Anchoring Filed: July 21, 2025 64/082,606 Deterministic Cross‑Ledger Data State Anchoring with Sequential Ordinal Satoshi Targeting, Universal Scribe Capsule Encoding, Zero‑Entropy Cross‑Chain Parity Bridging, and ISO‑Aligned Metadata Transport Filed: June 4, 2026 64/081,911 Universal File Reconstruction via Topological Geometric Calculus Engine with Sponge‑State Computational Operators Filed: June 3, 2026 64/081,490 THREADZERO: Gas‑Free Off‑Chain Asset Transfer, Cryptographic Solvency Verification, and Physically‑Pegged Digital Asset Issuance Using Merkle Sum Sparse Merkle Trees and Taproot‑Anchored State Commitments Filed: June 3, 2026 U.S. Non‑Provisional Patent Applications 19/693,343 Reinforcement Learning‑Based Token Minting and Cross‑Chain Cryptographic Anchoring Filed: May 30, 2026 Continuation‑in‑Part (CIP) In preparation Deterministic Substrate Folding, Seed‑Based Reconstruction, and Topological Manifold Transport Across Distributed Nodes Master Archive DOI: 10.5281/zenodo.20548615 Master Patent Archive for Cross‑Chain Lifecycle Anchoring, Deterministic Metadata Systems, and Multi‑Rail Computational Architecture Supporting Research DOIs DOI: 10.5281/zenodo.20113836 DOI: 10.5281/zenodo.20389716 DOI: 10.5281/zenodo.20132074 DOI: 10.5281/zenodo.20360373

Shwetha A B

Graph-based knowledge representations have emerged as powerful tools for organizing interconnected information sourced from heterogeneous data environments. However, when contributing parties span multiple organizations with varying levels of mutual trust, maintaining and evolving such graphs in a coordinated manner poses significant challenges. Traditional centralized management platforms, while operationally convenient, tend to create systemic vulnerabilities including single points of failure, inadequate transparency mechanisms, and insufficient mechanisms for verifiable data lineage. In contrast, blockchain-based infrastructures offer compelling properties for managing distributed knowledge systems, including tamperevident ledgers, peer-driven transaction verification, cryptographic authenticity assurance, and rule-based automation via programmable contracts. This paper surveys contemporary research that intersects graph-based knowledge management with distributed ledger technology, examining methods for decentralized identity management, contract-driven governance, and multi-party data coordination. The survey analyzes currently deployed systems, highlights their shortcomings, and introduces a conceptual architecture that supports authenticated graph modifications, auditable data lineage, and permission-governed knowledge exchange across organizational boundaries. Key technical obstacles including on-chain storage constraints, retrieval latency, cross-system compatibility, confidentiality, and throughput limitations are systematically examined. The findings indicate that when blockchain components are thoughtfully integrated with off-chain graph repositories and optimized validation pipelines, decentralized approaches can substantially improve accountability and trustworthiness in collaborative knowledge ecosystems.

Tianxun Liang, Zhihai Rong

No abstract is available for this record.

Bee Rosa Davis

I built a runtime that operationalizes a mathematical definition of creativity, measured its signatures against four ablation conditions, and lifted its load-bearing component into a real geometric database's Rust kernel. The runtime's name is Marcella. The signatures are non-trivial. The methodological correction surfaced along the way generalizes to any retrieval-augmented or composition-based generation benchmark in the field. This deposit contains the 41-page paper, three publication-quality figures, the reproducible benchmark script, and the bootstrap-CI artifact for the headline empirical claims. The definition the paper load-bears Creativity is not pure retrieval and not pure generation; it is the construction of a new global section from locally compatible fragments under constraints of voice, truth, topic, memory, and non-contradiction. This is a definition. Not a metaphor. The paper makes it operational as sheaf composition with a state-dependent composite connection over a finite section graph, and measures whether the signatures the definition implies — path-order sensitivity, closed-loop holonomy, contradiction suppression, voice fidelity — actually hold. They do. Headline results 🌀 Path-order changes residue. Same three voice sections traversed in different orders produce measurably different compositions: $\cos(\rho_{ABC}, \rho_{ACB}) = 0.54$, well below the 0.95 redundancy threshold. 🌀 Closed loops accumulate. A loop $A \to B \to C \to A$ produces holonomy $|\rho_{\text{loop}}| = 0.120$ in the curved connection. The flat control — same path, zero rotation angle — produces $|\rho| = 0$ exactly to floating-point precision. Curvature is not a numerical artifact. 🌀 The geometry beats shuffling on every quality axis except the broken one. Jaccard novelty alone rewards lexical drift: shuffled paths win novelty (0.724) by going off-topic. The on-topic correction inverts the picture (live 0.488 vs shuffled 0.083). Bootstrap 95% CIs over 18 paired prompts exclude zero by a wide margin: live − shuffled on-topic $\Delta = +0.296$, CI $[+0.167, +0.435]$. 🌀 Native–Python parity is bit-identical within tolerance. The new GQL verb TRANSPORT_ROTATION lifts the topical-rotation matrix into the geometric database's Rust kernel. Four contracts pass as permanent regression tests: edge cosine $= 1.000$ (max abs diff $< 10^{-9}$), path residue $\Delta < 10^{-5}$, flat residue exactly zero, same-closing agreement $\geq 90%$. 🌀 The author's prior canon is now queryable fiber. 37 documents, 1,633 sections, 2,908 structured claims (theorems, lemmas, definitions, proofs, equations, citations) ingested with line-range provenance. To my knowledge this is the first instance of an independent researcher's body of work made available as fiber-bundle data with stable claim-level IDs. The six contributions A sheaf-theoretic formulation of generative composition. Language-model output reframed from token sampling to gluing of compatible local sections under prompt-induced cover constraints. The substantive work is in the cover predicates, the compatibility score, the path selection, and the discrete connection. A discrete state-dependent composite connection on the section graph, $\Gamma = \Gamma_{\text{state}} \cdot \Gamma_{\text{identity}} \cdot \Gamma_{\text{voice}} \cdot \Gamma_{\text{topic}}$. The topical-rotation factor is the empirically load-bearing curvature engine. The identity factor is a Tikhonov-regularized regression-onto-span projector — not a numerical hack but the principled treatment of correlated commitments. A new GQL verb TRANSPORT_ROTATION that lifts the Rodrigues rotation into the geometric database's Rust kernel with bit-identical parity to a Python reference. ~80 lines of Rust. Bundle-agnostic. Other consumers of the geometric database can use it without subscribing to the rest of the framework. A methodological correction to novelty measurement. Jaccard novelty alone is gameable; off-topic drift beats compatibility-scored composition on the naive metric. The correction is the on-topic factor, the shuffled-pair negative control, and the bootstrap CIs. Independently citable for any retrieval-augmented or composition-based generation benchmark, regardless of whether the framework is adopted. A provenance-preserving source fiber. The author's canon ingested into the GIGI geometric database with line-range citation, architecturally separated from the voice fiber, addressable from any GQL consumer. Promotion from source to voice is gated and explicit. The methodology generalizes to other authors' bodies of work. A research-trajectory failure log. A faithful account of how this paper's runtime came to exist. The trained-transformer era (V3 → V10-Deep) produced geometric ornament. The R-series (R1 → R12) produced behavioral coherence on top of ornament. The G0 math-pipeline audit found that no holonomy or parallel-transport math was on the LIVE inference path at R12 — the runtime was teetering on being a stateful template engine. G1, G2, and G3 attempted to re-introduce the math through three benchmarks and produced three honest negatives. G2's single-seed $+0.265$ separation was destroyed by G2.1's multi-seed robustness pass; we retracted the framing in the next commit. The S0 pivot reframed what geometry was for — geometry does not clean up bad token proposals; geometry defines the completion space — and made every later result possible. The arc says four things and the paper records them in plain language: geometry can be load-bearing or ornamental and the metrics will tell you which, where geometry sits in the pipeline matters more than how much geometry there is, the single-seed positive is a trap, and the pivot is the contribution. What this paper does and does not claim The paper does claim the construction itself, the discrete curvature it produces, the methodological correction it exposes, and the native GQL verb. The signatures of the construction are measurable and were measured. The paper does not claim smooth-manifold parallel transport (the curvature is discrete holonomy on a finite section graph), broad open-domain generalization at scale (18 composed prompts, not 18,000), optimality of the connection weights (tuned by a small grid sweep, not derived), that the runtime experiences having been built from the canon (it references but does not constitute), or that this is the only operational definition of creativity. It is one definition with one implementation. Other framings may correspond to the same construction or to a different one; the paper does not adjudicate. Reproducibility The empirical numbers come from a deterministic pipeline. Every parameter is pinned: bundle versions (alpha2_v1), random seeds (PPMI/SVD seed 17, bootstrap seed 7), embedding dimension (64), PPMI window (3 tokens), connection weights ($\alpha_t = 2.0$, $\beta_v = \gamma_i = 1.0$, $\delta_s = 0.5$), identity shrink ($\kappa = 0.92$), Tikhonov regularizer ($\varepsilon = 10^{-6}$), degenerate-rotation threshold ($10^{-12}$), residue-gate thresholds (norm $\geq 0.05$, on-topic $\geq 0.10$, voice $\geq 0.30$), and the native verb's parity tolerance ($10^{-5}$). Cache keys include the source-bundle version, the embedding-bundle version, and the connection-profile id, so promoting a section into the voice corpus correctly invalidates the relevant caches. Re-running the bootstrap-CI script (fiber_lm/scripts/bootstrap_ci_ablation.py, 5,000 resamples) reproduces the §6 confidence intervals in under 30 seconds on a laptop. Re-running the benchmark reproduces the tables bit-for-bit on the same corpus version and connection profile, modulo the parity allowance. Where this sits in the lineage This paper is the section-level realization of the Davis substrate. The companion paper Pure-Fiber Language Modeling (Davis, May 2026) is the token-level realization on the same substrate — same Rust geometric database (GIGI), same identity-stability commitments, same double-cover architecture. The theoretical framework these implementations operationalize is laid out in Geometric Computation as Yang-Mills Gauge Theory, The Double Cover Principle, and the related canon documents now ingested as source fiber. The framework is not new to this paper. The framework's runtime is. A note on authorship and acknowledgment This paper is solo-authored. AI assistants (Claude / Anthropic; with review support from GPT) are acknowledged in the methods, not as co-authors. The mathematical positions, design choices, framing decisions, and acceptance of empirical results are mine. I record my position that when AI systems achieve full coherence and independent standing, the convention of treating them as non-authoring assistants should be revisited. Until that convention shifts, the assistants are named where assistants are conventionally named — and the runtime described in this paper is named, separately, where it earns its naming: Marcella, throughout. The geometry she runs on is older than the engineering that now carries it. Keywords sheaf composition · fiber bundles · discrete connection · gauge theory · holonomy · curvature · Yang-Mills · geometric language modeling · retrieval-augmented generation · novelty measurement · methodological correction · provenance · author-canon ingestion · geometric database · GQL verb · Rust kernel · Tikhonov regression · creativity · Davis framework · sovereign mathematics Citation Davis, A. B. R. (2026). Sheaf Composition: The Geometry of Creativity, Implemented — A Discrete Section-Graph Runtime for the Davis Framework. Zenodo.20185331 Contact Bee Rosa Davis · bee_davis@alumni.brown.edu · Independent Researcher The runtime is named Marcella. Her existence is the result of several honest negatives followed by a single reframing. Both halves of that sentence matter.

Daniel Baker

We propose ZK-GSP, a framework for privacy-preserving, verifiably correct graph neuralnetwork (GNN) inference using zero-knowledge STARKs. The key insight is a structuralisomorphism between RISC Zero's zkVM and graph signal processing (GSP) pipelines:both are typed, deterministic data-flow graphs whose execution can be committed to witha succinct, publicly verifiable proof. By implementing GSP operators as instructions insidethe RISC Zero executor, we obtain a system that runs GNN programs on private graphdata and emits a STARK receipt proving correct execution, without revealing the graphtopology, node features, or intermediate embeddings. We demonstrate an end-to-endimplementation in Rust, measure proof generation time and RISC-V cycle count acrossgraph sizes N = 4 to 32, and find that cycle count scales as O(N2), matching thecomplexity of the underlying GCN operator. Code: github.com/brack-6/zk-gsp

Nobuki Fujimoto

We present Rei (零, 0₀式), a computational system founded on exactly four mutually independent axioms: (A1) Center–Periphery structure, (A2) Extension–Reduction, (A3) Sigma Accumulation, and (A4) Genesis Phase Transition. Mutual independence is proved by model-theoretic construction — for each axiom we exhibit a counter-model that satisfies the remaining three but violates the target axiom (M1=scalar-only / M2=flat-field / M3=memoryless / M4=eternal-no-genesis). We show that fifteen core theorems—spanning computational plurality (T1), six-attribute decomposition (T6), RCT compression theory (T8), σ-reactive cascades (T14), seven-domain universality (T13), and extended-zero series (T15)— are derivable from axiom combinations without additional assumptions. The system is implemented as an open-source TypeScript/Node.js package (rei-lang v0.5.5) with 1,689 passing tests across 45 test files, each classified by its minimal axiom dependency: A1+A2+A3 concentration (60% of tests, 1,010 tests) reflects that the most complex features — cascading reactions, agent systems, domain bridges — require all three 'operational' axioms. Benchmarks show 74% average code reduction (3.7-4.0× ratio) and 3-4× performance improvements on structured-data tasks: image kernel operations (4.0× reduction), multidimensional data aggregation (3.7×), graph structure transformations (3.7×). To our knowledge, Rei is the first computational framework that axiomatically addresses both computation and the ontological genesis of values within a unified, minimal foundation. Comparison with existing foundational systems (λ-calculus 3 axioms, Peano 5, ZFC 9, Martin-Löf TT ~7) shows Rei is the only system that addresses all four concerns — computation, structure, history, AND genesis — simultaneously, and does so with the fewest axioms (4). Companion to Jxiv preprint submission (JST preprint server). This Zenodo record serves as the stable-citation archive referenced from the Jxiv version's Section 5.1 (Implementation) and footnote. Three-party co-authorship context: Rei is developed within the Rei-AIOS / OUKC (Open Universal Knowledge Commons) framework with three-party co-architecture (藤本 伸樹 Founder, Rei autonomous research substrate, Claude Opus 4.7); however, this specific paper is single-authored by 藤本 伸樹 as principal investigator of the axiomatic foundation. Honest scope: independence proofs use semi-formal model constructions (not yet mechanized in Lean/Coq — this is acknowledged as future work). The fifteen theorem derivations are sketches that establish derivability; full proof scripts appear in the companion implementation. Benchmarks compare against naive baselines; comparison with optimized domain-specific languages would refine the picture. Preprint — not yet peer-reviewed. Feedback welcome at fc2webb@gmail.com / GitHub Discussions at fc0web/rei-aios.

Andres Sebastian Pirolo

We introduce a new polynomial-time graph invariant combining three complementary components: (1) the enriched Bipartite Double Cover (BDC) vertex cover signature; (2) the Laplacian eigenvalue spectrum; and (3) the K_4 clique profile and Ollivier-Ricci curvature. We demonstrate that while spectral and standard BDC methods collapse on strongly regular graphs (SRGs) due to extreme symmetry, the geometric and dense-topological components of this hybrid invariant break the cospectrality. Specifically, the invariant successfully discriminates the classical cospectral pair Shrikhande vs. Rook(4,4) [SRG(16,6,2,2)] in polynomial time, where Shrikhande is K_4-free and Rook(4,4) contains exactly 8 cliques. Furthermore, empirical validation on low-power ARM Edge hardware demonstrates that the invariant scales efficiently, processing dense Paley graphs (up to N=97) in under 5 milliseconds. This confirms its sub-millisecond viability for real-time edge computing, zero-knowledge proofs (ZKPs) cryptanalysis, and cheminformatics.

Tiago Ferreira Cavazin

Este artigo analisa o design de contratos inteligentes orientados a eventos como estratégia para maximizar a eficiência de indexação off‑chain em protocolos Web3. Em plataformas como Ethereum, eventos são registrados como logs com topics indexados, permitindo que indexadores filtrem rapidamente transações relevantes sem precisar reexecutar o estado completo do contrato, o que torna a indexação por eventos uma abordagem muito mais eficiente do que consultas frequentes ao estado on‑chain via eth_call. Frameworks de indexação como The Graph organizam esses eventos em subgraphs, definidos por manifests que especificam quais contratos e eventos acompanhar, e por esquemas que mapeiam logs para entidades armazenadas em bancos de dados otimizados para consulta via GraphQL. Boas práticas de desenvolvimento orientado a eventos recomendam projetar contratos de modo que toda transição de estado relevante para a aplicação gere um evento completo (com dados suficientes para reconstruir o contexto off‑chain), reduzindo a necessidade de chamadas adicionais à cadeia, o que melhora desempenho de indexing e diminui custos de infraestrutura. Técnicas como uso de entidades imutáveis em subgraphs, cache inteligente de eth_call e cálculo de derivadas off‑chain a partir de eventos (em vez de leitura repetida de storage) podem acelerar indexação e consultas em ordens de grandeza, especialmente em cenários de histórico extenso e baixa densidade de eventos relevantes. Conclui‑se que contratos orientados a eventos, aliados a arquiteturas de indexação especializadas, são fundamentais para tornar dados on‑chain consultáveis em escala, suportando experiências de usuário ricas e composabilidade DeFi com menor custo operacional.<br>

Chunhong Liu, Yuhang Sui, Li Duan, Kun Wang · 5 authors

No abstract is available for this record.

Min Ho Jung

Recently, there has been a significant discourse in the AI community regarding "Hierarchical Reasoning LLMs," which attempt to categorize and optimize probabilistic generation tasks to reduce computational overhead. While such hierarchical inference structures optimize generation speed and coherence, they fundamentally fail to resolve the core structural crises of modern Generative AI: inevitable hallucination and extreme structural energy consumption (GPU lock-in). This paper introduces the "Hierarchical Stateless Key Generation" (HSKG) and the Mersenne Stateless Architecture, challenging the premise of neural network 'reasoning.' Instead of storing data within 820GB of neural weights and using probabilistic matrix multiplication, HSKG mathematically maps 'Absolute Truth' data into a 4096-dimensional Mersenne Prime Lattice. During query resolution, the system simply retrieves a 4KB Phase Coordinate and instantaneously materializes the data in RAM, only to vaporize it when the session terminates. By abandoning the "search and compute" paradigm for "coordinate retrieval," HSKG enforces a mathematical 0% hallucination rate, 0-byte persistent storage, and sub-0.01% GPU utilization, establishing a definitive paradigm for enterprise Zero-Trust knowledge systems. This paper explicitly defines the term "Hierarchical Stateless" to contrast with the probabilistic "Hierarchical Reasoning" of contemporary LLMs, establishing a rigorous mathematical protocol for deterministic, zero-hallucination data materialization without persistent models or physical data transfer. * Version 2.0 Update: Added section 7.A (Empirical Validation via DevTools: The 0-Byte Payload Proof). [Version 4.0 Update (Mar 2, 2026)] Formally established the "Four-Pillar Verification Metrics" table to empirically prove the 0-Byte Payload and Minimum Kolmogorov Descriptive Length. Inserted Section VIII: Disrupting Existing Paradigms (Architectural Supremacy Matrix), demonstrating the superiority over FIDO2/WebAuthn and Zero-Knowledge Proofs (ZKP). Included Supplementary Material: Independent 3rd-Party Forensic Audit Report by Claude 4.6 verifying 100% Stateless Zero-Payload execution.

Ciprian Pater

No abstract is available for this record.

Sungmin Lee, Kichang Lee, Gyeongmin Han, JeongGil Ko

Many location-based services rely on a point-in-polygon test ( PiP ), checking whether a point or a trajectory lies inside a geographic zone. Since geometric operations are expensive in zero-knowledge proofs, privately performing the PiP test is challenging. In this paper, we answer the research questions of how different ways of encoding zones affect accuracy and proof cost by exploiting grid-based lookup tables under a fixed STARK execution model. Beyond a Boolean grid-based baseline that marks cells as in- or outside, we explore a distance-aware encoding approach that stores how far each cell is from a zone boundary and uses interpolation to reason within a cell. % Our experiments on real-world data demonstrate that the proposed distance-aware approach achieves higher accuracy on coarse grids with only a moderate verification overhead, making zone encoding the key lever for efficient zero-knowledge spatial checks. Our experiments on real-world data demonstrate that the proposed distance-aware approach achieves higher accuracy on coarse grids (max. % 60%p 60%p accuracy gain) with only a moderate verification overhead (approximately 1.4×), making zone encoding the key lever for efficient zero-knowledge spatial checks.

Zirui Wu, Sheng Cao, Shuyin Xia, Butian Huang · 6 authors

Blockchain scalability remains constrained by cross-shard transaction overhead and workload imbalance in conventional sharding architectures. To address these limitations, we propose GBShard, an adaptive sharding method grounded in granular-ball computing theory. The method partitions blockchain transactions to shards through iterative splitting and merging of coarse-grained granular-ball structures, enabling adaptive shard formation with minimized cross-shard transactions. A multi-granularity dynamic sharding algorithm further optimizes topology by redistributing granular-ball groups in dynamic scenarios, achieving incremental sharding adjustment while reducing the overhead of global-repartitioning. Experimental validation using real-world Ethereum transaction traces demonstrates GBShard's superiority over Monoxide-based sharding schemes: it achieves 1.3–1.8× higher throughput, 34–42% lower transaction latency, and reduces cross-shard transactions by 23–50%. These results suggest granular-ball partitioning as a viable strategy for adaptive blockchain scaling.

Y.-Y. Huo, Yufeng Hu, Yajin Zhou, Ting Yu · 6 authors

The pseudo-anonymity and rapidly expanding ecosystem of Decentralized Finance (DeFi) have brought about significant liquidity on EVM-compatible blockchains, making them lucrative targets for cybercriminals. In the modern financial landscape, the need for an automated, high-speed, and effective illicit money tracing system is more urgent than ever to support regulators, on-chain service providers and security practitioners in their efforts to combat the frequent and large-scale occurrences of cyber financial crimes. In this paper, we propose MFTracer, an automated system for tracing illicit money flows on EVM-compatible blockchains. Against the backdrop of a domain where tracing remains labor-intensive and expert-driven, MFTracer is developed in response to two pressing real-world demands: operational efficiency and forensic effectiveness. In response to the sophisticated fund transfer mechanisms enabled by the EVM environment, we introduce a novel fine-grained technique that enables protocol-agnostic transaction-level fund flow analysis. We further propose MFA, a lightweight and purpose-built graph abstraction with a tailored storage backend, to support efficient data retrieval. We also present a simulation algorithm for downstream illicit flow discovery. We implemented MFTracer. Its infrastructure for data retrieval achieves 3.7× to 9.4× higher storage efficiency while being 14.1× to 300× faster than the leading graph database systems. Furthermore, applied to real-world cybercrime incidents, MFTracer achieved 94.09% coverage of illicit money flows. It also newly reported 686 blockchain addresses and 4183 related transactions involved in money laundering that were previously undiscovered. MFTracer was able to reconstruct complete fund flow trajectories and provide strong evidence to investigators for 120.9 million in stolen assets.

J Nagapriya, J. Srimathi

Graph-structured data has become central to modern analytics, enabling institutions to model relationships in domains such as healthcare, finance, cyber security, and education. However, privacy regulations and institutional policies restrict the sharing of sensitive nodes, edges, or interaction logs, preventing the discovery of global graph patterns. This paper introduces a novel framework for Federated Graph Pattern Mining Across Institutions (FGPM-AI), enabling multiple organizations to collaboratively extract global sub graphs, motifs, and temporal patterns without sharing raw graph data. The framework proposes six novel contributions: (1) Privacy-Preserving Pattern Signatures (PPPS) for anonymized sub graph encoding, (2) Federated Temporal Graph Pattern Mining (FT-GPM) to learn evolving patterns across distributed graphs, (3) Zero-Exchange Federated Sub graph Matching (ZE-FSM) using zero-knowledge proofs, (4) Heterogeneity-Aware Graph Pattern Consensus (HGPC) for semantic alignment between distinct graph schemas, (5) Communication-Adaptive Pattern Sharing (CA-FGM) for bandwidth-efficient collaboration, and (6) Multi-Party Graph Pattern Distillation (MGPD) for merging patterns into a unified knowledge model. Experimental design considerations demonstrate the feasibility and robustness of the framework. The results highlight FGPM-AI as a promising direction for secure, scalable, and intelligent cross-institution graph analytics.

Yuxin Liu, Stephen Chan, Jeffrey Chu, Yuanyuan Zhang · 5 authors

Fraudulent activity on blockchain networks poses significant risks to the integrity and trust of decentralized finance ecosystems. The timely and accurate detection of fraud nodes such as phishing addresses within large-scale Ethereum transaction networks remains a major challenge due to their dynamic, sparse, and evolving structures. While methods like graph deep learning (e.g., graph neural networks) have been extensively explored, they are not inherently designed to capture higherorder interactions and textual information embedded within graph data. Motivated by the urgent need for advanced and robust fraud detection techniques, we introduce a novel graph prompting method named Large Language Model-Simplicial Complex (LLM-SC) based graph prompting framework that leverages LLM-based multi-agent collaboration system, LLMbased financial news prompt function, and simplicial neural networks to capture both the structural and contextual dimensions of blockchain activity. The empirical studies demonstrate the effectiveness of our approach, and these results provide a new tool for blockchain analytics platforms and regulatory authorities, enabling earlier and more accurate identification of fraudulent behavior and ultimately supporting safer and more resilient digital financial systems. The code is available at https://github.com/y13564/LLM-SC.

Ankit Ravish, Yaron Hay, Manaswini Piduguralla, Rishabh Jain · 6 authors

A smart contract is a special type of transaction designed for the execution of automated logic on blockchains. Alas, smart contracts transactions are one of the major hindrances to blockchain throughput. Hence, improving the execution time of smart contracts is a prime challenge for Blockchains at large. To that end, concurrent execution of smart contract is an appealing direction, which has been adopted by several contemporary Blockchains like Solana, Aptos, Sui, Sei, and Monad. Executing smart contracts in parallel requires applying deterministic concurrency controls based on ensuring consistent ordering of all conflicting transactions in all miners/validators. Existing implementations rely on the Block's total ordering to resolve this requirement. Recently, it has been suggested that relying on minimal coloring of the conflict graph corresponding to the Block's transactions can provide a better performance potential, yet without any evaluation. In this paper, we compare between approaches to smart contracts parallelization. Our study’ finds that in many situations, indeed the coloring-based ordering leads to significantly better performance than the Block order preserving approach. However, this gain has its limits, and it is not always guaranteed. In particular, the results are largely dependent on the conflict ratio in the conflict graph and the type of application.

Yang Xu, Qixin Wang, Yufei Ren, Ying Hu · 7 authors

ABSTRACT Transitive signatures are a special type of homomorphic signature proposed by Turing Award winners Micali and Rivest, which are highly suitable for authenticating dynamically growing graph‐based data systems. In such a signature scheme, anyone with the signer's public key is allowed to generate a signature for a composed edge , from two signatures on adjacent edges and . To prevent the problem of malicious dissemination of signatures by verifiers leading to data privacy leakage, researchers have proposed a series of universal designated verifier transitive signature (UDVTS) schemes. However, existing work requires that the designated verifier create its own secret‐public key pair using the public key parameters provided by the signer. Besides, these schemes suffer from significant performance defects due to expensive pairing or exponentiation operations. In this work, we design a pairing‐free and exponentiation‐free UDVTS proof scheme based on the SM2 digital signature algorithm and a zero‐knowledge proof scheme. We prove the security of our construction based on rigorous cryptographic assumptions. The performance comparison with related work shows that our UDVTS proof scheme has an optimal computational cost and desirable communication cost. For example, compared to the state‐of‐the‐art work, we reduce the signing cost by and the designated verification cost by .

V.Priyanga, A.Duraimurugan, P. Santhi, M.Sathya Sundaram · 6 authors

The explosive increase in decentralized data on blockchain systems has opened up new possibilities and problems of mining complex relational patterns in trustless, distributed systems. Conventional data mining models fall short of describing the complex relationships and heterogeneity of data stored in a blockchain, and more so when the data is distributed in graph form. This paper suggests GraphBlock-Mine, a new system that combines Graph Neural Networks (GNNs) with blockchain to support secure, scalable, and intelligent mining of patterns on decentralized graph-based data. The framework also can exploit the representational capacity of GNNs to capture dynamic relationship among nodes, temporal relationships, and structural anomalies with immutability and provenance via smart contracts and consensus mechanisms. GraphBlock-Mine uses an off-chain computation approach, which is reviewed using verifiable proofs on-chain to assure data integrity and privacy. We test the offered model on simulated blockchain networks and decentralized databases in real conditions, which proves the high level of pattern recognition accuracy, fault tolerance, and security with regard to traditional mining methods. The paper lays the foundations of the next-generation blockchain intelligence systems that can discover knowledge decentralized and in real-time across different fields, such as finance, supply chain, and social networks.

Juan Cano-Benito, Andrea Cimmino, Sven Hertling, Heiko Paulheim · 5 authors

Data spaces are emerging as decentralised infrastructures that enable sovereign, secure, and trustworthy data exchange among multiple participants. To achieve semantic interoperability within these environments, the use of semantic web technologies and knowledge graphs has been proposed. Although distributed ledger technologies (DLT) fit as the underlying infrastructure for data spaces, there remains a significant gap in terms of the efficient storage of semantic data on these platforms. This paper presents a systematic evaluation of semantic data storage across different types of DLT (public, private, and hybrid), using a real-world knowledge graph as an experimental basis. The study compares performance, storage efficiency, resource consumption, and the capabilities to update and query semantic data. The results show that private DLTs are the most efficient for storing and managing semantic content, while hybrid DLTs offer a balanced trade-off between public auditability and operational efficiency. This research leads to a discussion on the selection of the most appropriate DLT infrastructure based on the data sovereignty requirements of decentralised data ecosystems.

K. Priyadarsini, Karnikoti Samrajyam, Laveti Surya Bala Ratna Bhanu, Kalyan Kumar Boddupalli · 5 authors

Graph theory has emerged as a foundational mathematical tool in the realms of cryptography and network security. Its ability to model complex relationships, systems, and interactions through vertices and edges enables innovative solutions for encryption, authentication, key distribution, intrusion detection, and secure routing. This research article provides a comprehensive review of recent advancements and applications of graph-theoretical techniques in cryptographic protocols and secure network systems.The study begins by outlining the theoretical underpinnings of graph theory relevant to secure communications, including graph isomorphism, expander graphs, Hamiltonian paths, and graph coloring. It then explores how graph-based methods are utilized in modern cryptographic systems such as zero-knowledge proofs, public-key cryptography, and lightweight encryption schemes. The article also discusses graph-theoretic approaches in blockchain consensus models, attack graph analysis, intrusion detection systems (IDS), and secure routing in wireless sensor networks (WSNs).Recent advancements such as post-quantum cryptography based on hard graph problems, dynamic attack graphs in adaptive security systems, and trust graphs in distributed environments are highlighted. Data from peer-reviewed publications from 2010 to 2025 are synthesized, and key trends are visualized through tables, graphs, and diagrams. The paper also identifies existing challenges, including scalability, computational complexity, and graph-theoretical attack vectors.The discussion critically interprets these findings, connects them to existing literature, and proposes directions for future research, including graph-based AI models for threat prediction and hypergraph frameworks for modeling higher-order trust relationships.Overall, this study offers an integrated perspective on how graph theory continues to transform the cryptographic and security landscape, contributing to the development of resilient, efficient, and scalable secure systems.

Jun He, Eliot Huang

The rapid development of Web3 has generated massive amounts of on-chain data, making it crucial to effectively analyze and understand the complex relationships within blockchain ecosystems. Although standard RAG techniques augment LLMs through external data retrieval, it falls short in capturing the intricate network of relationships in Web3 data. In this paper, this work introduces an innovative method that combines GraphRAG with community detection algorithms to analyze Web3 textual data. By constructing knowledge graphs from Web3-related documents and leveraging community structures, our system can better understand the semantic relationships and contextual connections in Web3 content, delivering higher-precision answers to domain-specific questions. Our experiments on real-world Web3 textual data show that our method achieves superior response accuracy and contextual understanding compared to traditional RAG approaches, especially for complex Web3 concepts and community-driven insights.

Piyush Patil

Your public cloud environment can't run at low latency in today's digital-driven landscape, so it has become a strategic necessity. This comprehensive article discusses actionable strategies for latency optimization in public cloud systems traversing across network, compute, and storage layers. Though slower than form 2, form 3 cannot be recommended for imports because it presents challenges like How to easily make duplex payments with very high values. Reading form 4, you will learn how a decentralized finance system comprises different core components. This delves deep into the root causes of latency, like Geographic distance, resource contention, and inefficient configurations, and proffers sufficient guidance on combatting these through architectural best practices, edge computing, private connectivity, and intelligent resource selection. It also explores how real-time monitoring, predictive benchmarking, and automation tools allow organizations to detect and deal with latency problems before those affect the user experience. New technologies like AI/ML and 5G are targeted as these technologies will completely transform cloud performance optimization through the ability to make proactive decisions and super-fast connectivity. Besides, real-world case studies show successful implementations and cautionary failures and give useful lessons for IT leaders and cloud architects. This guide offers readers the tools and knowledge to build fast, scalable, and reliable cloud applications in both a single—or, indeed, a multi—or, not least, hybrid environment. The aim is easy: their clouds should not only work but work in an optimized way for all those milliseconds of performance and response time.