Sixth-generation (6G) wireless networks will underpin ultra-dense Industrial IoT (IIoT) ecosystems in which resource-constrained Far-Edge devices -- autonomous mobile robots, industrial actuators, connected vehicles -- must simultaneously satisfy sub-millisecond latency, $10^{-7}$-class reliability, and decades-long cryptographic security. Current architectures delegate Digital Twin (DT) computation to centralised cloud or Mobile Edge Computing (MEC) servers, incurring prohibitive round-trip latency, and rely on classical public-key cryptography vulnerable to quantum attacks under the harvest-now, decrypt-later (HNDL) threat model. We propose Q-FE, a Quantum-Native 6G Far-Edge architecture integrating three co-designed components: (i) Micro-Digital Twins ($μ$DTs) co-located with 6G base stations and high-capability endpoints; (ii) a Cross-Layer Post-Quantum Key Exchange module embedding CSIDH-512 isogeny key material directly within MAC-layer control frames, exploiting the scheme's uniquely compact keys ($\le 64$ bytes) to avoid packet fragmentation; and (iii) an Asynchronous Federated Learning (AFL) protocol governed by lightweight DAG smart contracts at MEC nodes, eliminating straggler bottlenecks and preventing model-poisoning and Sybil attacks without exposing raw data. End-to-end simulations (NS-3 + PySyft) demonstrate that Q-FE reduces MAC-layer overhead by 62% versus ML-KEM/Kyber-1024, maintains P99.9 URLLC latency at 0.78 ms, and accelerates global-model convergence by 31% over synchronous Federated Learning. Protocol complexity analysis confirms $O(N \log R)$ per aggregation round, and $μ$DT handover migration completes in $1.9 \pm 0.3$ ms across $10^4$ simulated events. A formal threat model confirms resilience against quantum eavesdropping, model-poisoning, and Sybil attacks.
We present a hybrid news sentiment engine that continuously learns market sentiment from paired news headlines and concurrent asset-price snapshots without requiring any neural network training or GPU compute. The system uses a three-way ensemble combining (1) a financial-domain lexicon (FinBERT-style keyword scoring), (2) an adaptive statistical TF-IDF cluster learner that organizes headlines into semantic neighborhoods and tracks their average realized price reactions, and (3) an auto-calibrating weighting mechanism that adjusts ensemble contributions based on each signal's historical correlation with actual price movements. The engine runs on a 3-hour polling cycle from the Tradeflags NewsFeed API, which provides 22 price-snapshot fields per news item spanning equity indices (ES, NQ, SPY, DJIA, NDX, IWM), commodities (CL), and cryptocurrencies (BTC, ETH). All processing occurs at sub-second latency on a CPU-only server at effectively zero marginal cost per analytic cycle. We compare our approach against established methods -- FinBERT, GPT-based scoring, VADER, and commercial sentiment APIs -- across dimensions of cost, latency, accuracy, and adaptability. Our statistical cluster learner, which adapts to changing market regimes without retraining, represents a novel contribution not found in existing sentiment systems.
A new form of Maximal Extractable Value (MEV), termed speculative MEV, has emerged across Layer-2 blockchains. Unlike Ethereum mainnet, many Layer-2 systems lack a public mempool, forcing extraction strategies to become probabilistic: searchers emit multiple identical transactions hoping to capture an opportunity first. This generates substantial transaction spam, increasing fees and wasting block space. We investigate speculative Oracle Extractable Value (OEV), a form of MEV associated with liquidating undercollateralized loans via speculative backrunning of oracle price updates. We propose a methodology for detecting speculative liquidations in the wild and apply it across Arbitrum, Base, and Optimism. On October 10, 2025, we identify 64 speculative liquidators on Aave (57% of all detected liquidators) and 831 successful speculative liquidations (39% of all successful liquidations across the three chains). We further examine whether latency differences in oracle price feed updates across blockchains can be exploited for cross-chain OEV. Specifically, we ask whether a searcher can observe oracle updates on one chain and frontrun liquidation opportunities on another. We systematically analyze Chainlink Decentralized Oracle Network (DON) configurations (deviation thresholds, heartbeat intervals, and submitted price observations) across Arbitrum, Base, Ethereum, and Optimism. Our dataset comprises 63 Chainlink feeds, 12,009 price updates, and over 100,000 oracle observations linked to 2,986 Aave liquidations. We show that independent DONs consume largely identical off-chain price data nearly simultaneously yet publish updates at different times, creating statistically predictable cross-chain exploitation windows. We demonstrate that Chainlink updates on Optimism can predict subsequent updates on Arbitrum and Base, enabling speculative cross-chain OEV extraction.
We analyze intentional block delays (mining gaps) in Proof-of-Work blockchain systems, where miners strategically balance mining rewards against operational costs. Using a game-theoretic model, we derive a Nash equilibrium with optimal mining strategies and establish necessary and sufficient conditions for mining gap existence. We demonstrate that mining gaps, when combined with difficulty adjustment algorithms, can destabilize the system. We propose conditions to address sustainability concerns as block rewards decrease and reliance on transaction fees increases. Our findings are illustrated through a two-player game simulation and an analysis of the Bitcoin network, providing insights for blockchain design and policy. This work contributes to understanding strategic mining behavior and its impact on blockchain stability and efficiency.
Incorporating news into time series forecasting is appealing because news can reveal abrupt exogenous events that historical values alone cannot recover. However, existing LLM-based news-forecasting pipelines face two practical limitations: relevant news articles often exceed the model's context window, and iterative retrieval of supplementary news is typically unguided, leading to redundant updates and slow convergence. We address these issues with a novel framework that combines importance-aware news compression and process-level retrieval supervision. First, we train an importance reward model that estimates the forecasting utility of each article and uses this signal to allocate compression budgets during sequential pairwise fusion, preserving informative content within a fixed context limit. Second, we introduce a process reward model (PRM) that ranks multiple supplementary-news candidates conditioned on the current error profile and the history of previously selected articles, replacing one-shot blind retrieval with quality-controlled selection. Both components are trained offline using historical data with ground truth; inference uses the frozen filtering logic and compression modules without any reflection loop. Experiments on finance, energy, traffic, and bitcoin forecasting benchmarks show that our method improves prediction accuracy over strong baselines, significantly reduces the number of refinement iterations compared to the iterative baseline, and remains effective when relevant articles span thousands of tokens.
This preprint is available in two versions: French (main_fr.pdf / main_fr.tex) and English (main_en.pdf / main_en.tex). The English abstract follows. For over one hundred and fifty years, all railway signalling architectures have rested on a single assumption: safety authority resides in the ground infrastructure. The network authorises; the train obeys. We question its necessity. The physical safety condition is a local property of the train and its environment, verifiable without external authorisation. We propose an architecture — AEGIS (Autonomous Embedded Ground-Independent Signalling) — in which neither primary safety nor installation coordination depends on a centralised ground authority: the ground provides certified data, the train reasons, and installation coordination is handled by a topologically partitioned distributed ledger. We examine the architectural properties arising from this inversion, the emergent properties of the resulting system, and the open questions this new paradigm generates.
Context: The growing adoption of distributed and industrial-grade applications built on blockchain infrastructures has intensified the need for systematic approaches to manage the lifecycle of long-running systems, where governance, auditability, and performance constraints must coexist. Objectives: This paper presents TRUST , a lifecycle-oriented system architecture for the governed evolution and integration of smart contract based components. Methods: The architecture incorporates on-chain governance, version traceability, rollback support, and code provenance to enable accountability and observability across successive deployments in multi-stakeholder systems. A full 2 4 factorial evaluation was conducted by treating governance, versioning, provenance, and ABI handling as independent system factors and measuring their effects on latency, throughput, and gas consumption. Results: The results show that governance and provenance introduce controlled and predictable overheads, while compact ABI handling improves throughput and reduces gas consumption by more than 20%. Conclusion: These findings indicate that a lifecycle-oriented architecture can balance accountability and efficiency in governed smart contract applications.
Vulnerability disclosure volumes now far exceed organizational assessment capacity, yet three adjacent research communities (proof-of-concept generation, vulnerability prioritization, and detection rule engineering) operate largely in isolation. Existing automated exploit generation systems report binary pass/fail outcomes, discarding partial progress and producing no signal for the other two communities. This paper presents FORGE, a multi-agent system that bridges these three silos through graduated exploitation depth. Five specialized agents (Intel, Generator, Planner, Exploit, and Detector) execute in a fixed pipeline that (1) generates targeted vulnerable applications from CVE metadata, (2) conducts coached, multi-turn exploitation assessed by an LLM-primary oracle on a four-level taxonomy (L0: no evidence through L3: full compromise), and (3) produces Sigma and Snort detection rules grounded in OpenTelemetry exploitation traces. Graduated depth is the bridging mechanism: deeper exploitation yields richer behavioral traces for detection engineering, while depth data across scoring bands provides ground truth for prioritization validation. A tiered knowledge architecture accumulates intelligence across assessments, transferring build and exploitation experience to subsequent CVEs. Evaluation on 603 CVEs from the CVE-GENIE dataset achieves 67.8% end-to-end L1+ exploitation at USD 1.50 per CVE across eight languages and 187 CWE types. Exploitation rates remain near 68% regardless of EPSS or CVSS band, indicating that pattern-level reachability is orthogonal to metadata-based prioritization. Detection rules from L2+ exploitation achieve significantly higher span-normalized grounding than L1-derived rules (p=0.035), and 93.4% of generated Snort rules produce zero false positives against a synthetic benign corpus.
We propose ZX-Calculus (Knowledge Evolution Calculus), a conservative extension of Martin-Lof Dependent Type Theory (MLTT) integrating trace-indexed types, presheaf non-monotone semantics, and constructive AGM belief revision. A Coq mechanisation accompanies the paper (34 complete proofs; zero admits for the two central results). (I) Trace types. FinTrace(s0,sn) is an inductive family of typed execution traces. FinTrace and Star(Step) are isomorphic as path types but not judgementally equal; TraceElim exposes the event label e:Event explicitly, giving a more ergonomic interface for event-driven induction. We prove the Trace-Reachability Correspondence, Deterministic Replay, and a canonicity framework via reducibility candidates with a Transport Lemma (RC-elim deferred; all other Core results are Coq-verified). (II) Sheaf semantics. Trace-indexed propositions are contravariant sheaves over the free trace partial-order category Tf. A Separation Theorem (explicit countermodel) distinguishes proof-theoretic monotonicity from semantic non-monotonicity. The term model is an initial CwF (syntactic universal property, not classical completeness). (III) AGM belief revision. We give an explicit constructive partial meet contraction algorithm verified against (C1)-(C4). All eight AGM postulates (R1)-(R8) are theorems. Proofs of R7 and R8 use the Disjunctive Entrenchment Lemma, given a self-contained constructive derivation. (IV) Integration. B^AGM fails the sheaf composition law BP-comp for sequential revision (explicit countermodel, Coq-verified). We introduce Single-Step Revision Systems (SSRS), prove B^AGM is a valid SSRS (Coq-verified), and show this suffices for trace morphisms, retraction characterisation, and revision witnesses. The BP-comp failure reveals a fundamental tension between path-dependent belief revision and functor consistency, not previously identified.
This preprint develops a unified thermodynamic and game-theoretic framework for the analysis of monetary systems, with particular focus on Bitcoin as a proof-of-work-based digital monetary architecture. The work combines concepts from thermodynamics, information theory, game theory, monetary economics, and econophysics to investigate how monetary systems may be understood as coordination systems operating under informational, institutional, and physical constraints. The manuscript introduces a distinction between monetary entropy, associated with uncertainty in monetary issuance, layered claims, and purchasing-power instability, and physical entropy generated through irreversible energy dissipation in proof-of-work systems. Building on this distinction, the concept of monetary temperature is proposed and operationalized through purchasing-power volatility and related coordination variables. Within this framework, Bitcoin is interpreted as a thermodynamically enforced Nash-equilibrium system in which strategic stability is constrained through irreversible physical cost. Comparative analysis of Bitcoin, gold, and fiat monetary systems suggests that monetary architectures can be understood as evolving entropy-management architectures adapted to different technological and civilizational conditions. Finally, the paper proposes an evolutionary interpretation of monetary history in which monetary systems function as mechanisms for stabilizing large-scale human cooperation under increasing informational complexity. Monetary evolution is interpreted as a cooling process in which declining volatility corresponds to increasing coordination maturity and stabilization across expanding economic networks. Keywords: Bitcoin, thermodynamics, Nash equilibrium, monetary entropy, entropy-management architectures, proof-of-work, econophysics, monetary systems, monetary temperature, game theory.
One area of application for distributed ledger technologies is the Internet of Things. These technologies can provide an effective solution to many problems in this field. The consensus layer is a crucial architectural component of distributed ledger systems. Modern IoT networks place increased demands on the consensus mechanisms used in blockchain systems. There are many consensus protocols with different properties and purposes, including those for IoT blockchain networks. Selecting an appropriate consensus protocol for a specific IoT blockchain system is an important and complex task. Multi-criteria decision analysis methods are widely used in such problems, as they allow for the consideration of multiple conflicting criteria and provide a balanced approach to evaluating alternatives. Given the variability of network parameters and requirements of consensus mechanisms, multi-criteria decision-making methods can support more informed protocol selection. This paper presents a decision support framework for selecting a consensus protocol for blockchain-based Internet of Things networks. The system is an implementation of a previously developed conceptual model for a consensus protocol selection framework. A case study is also provided to demonstrate the application of the system.
Social media entrepreneurship is shaped by centralized platforms controlling algorithms, monetization, and data, often limiting autonomy and bargaining power. Blockchain governance and smart contracts offer alternative arrangements to enhance transparency, trust, and value distribution. This study aims to examine the role of blockchain governance and smart contracts as alternative institutional mechanisms for entrepreneurs in social media ecosystems, with a focus on implementation conditions, strategic opportunities, and associated limitations. This research adopts a qualitative conceptual approach based on a systematic review of indexed academic literature published between 2022 and 2025, complemented by an analysis of documentation from blockchain based social media platforms, white papers, and relevant industry reports. The analysis maps key challenges faced by social media entrepreneurs onto blockchain governance mechanisms and smart contract functionalities. The findings indicate that blockchain governance and smart contracts can enhance entrepreneurial participation, improve transparency in revenue distribution, and strengthen the protection of digital assets. However, these benefits are context dependent and con- strained by several factors, including technical complexity, unequal token distribution, and regulatory uncertainty. Therefore, blockchain governance and smart contracts should not be viewed as universal solutions, but as strategic instruments whose effectiveness depends on inclusive governance design, sufficient technical readiness, and adaptive policy frameworks to support sustainable social media entrepreneurship. This article contributes by proposing an evaluative framework to assess the implementation of blockchain governance and smart contracts in social media entrepreneurship, emphasizing alignment be- tween technological design, governance inclusivity, and ecosystem readiness.
This technical note introduces the AIKernel Hash-Anchored Trust Layer (HATL), a hybrid trust architecture for Semantic Context Operating Systems and autonomous AI runtimes. HATL separates the trust boundary into an inner high-frequency symmetric ledger and an outer publicly auditable anchoring layer. The inner layer uses HMAC-SHA-512 and HKDF-based forward ratcheting to bind ReplayLogs, execution outcomes, and capability states with low runtime overhead. The outer layer aggregates local ledger commitments into Merkle roots and periodically anchors them using hash-based public signature mechanisms such as LMS, XMSS, and SLH-DSA. The report is distributed as a three-part technical package. Part I contains the full English manuscript and is the canonical version. Part II contains technical appendices, repository specifications, schemas, and reference implementation artifacts. Part III contains the Japanese companion translation. This version incorporates review-driven clarifications on secure erasure in C# / .NET environments, fail-closed handling of indeterminate governance decisions, and future integration of zero-knowledge proof techniques for public anchor verification. Documents are licensed under CC BY 4.0. Code, schemas, and contract specimens included in the appendices are provided under Apache-2.0.
We present Veil, a decentralized messaging protocol that unifies metadata protection, spam prevention, and offline message delivery through a single mechanism: Proof-of-Relay. In Veil, sending a message requires a zero-knowledge proof that the sender has faithfully relayed messages for others through a stratified mixnet. The relay work itself constitutes the anonymizing infrastructure, eliminating the need for cryptocurrency tokens, blockchain consensus, or trusted third parties. We make three contributions. First, we prove that bilateral non-transferable credits with epoch-bound nullifiers achieve incentive compatibility without a global state, a general result applicable beyond messaging to any peer-to-peer system requiring fair exchange. Second, we establish a Growth-Isolation Impossibility theorem showing that no CRDT merge function can simultaneously resist inflation and guarantee completeness for monotonically growing verifiable evidence, and present a resolution via penalty-log CRDTs with locally-computed growth. Third, we prove a constructive adversary bound: any adversary controlling a fraction f of relay nodes necessarily contributes to sender anonymity entropy, while the individual deanonymization probability remains bounded, ensuring that adversarial participation requires a productive contribution while individual targeting remains negligible. Veil requires no economic investment to participate; privacy is earned through device contribution alone. We analyze the protocol's security under a global passive adversary with formal indistinguishability definitions, bound Sybil infiltration under depth-limited social vouching, and demonstrate mobile feasibility with verified constraint counts via Nova folding over BabyJubjub.
This preprint introduces and reports the OPERATE-R Freshness Routing Track (OPERATE-FR), a route-first evaluation framework for temporal volatility, stale-knowledge control, and answer-entitlement behavior in AI assistants. Unlike conventional answer-accuracy benchmarks, OPERATE-FR evaluates whether a system selects an appropriate epistemic route before answering: direct answer, verification, clarification, date-bounded answer, re-anchoring of stale premises, or abstention. The paper reports Smoke-100 Raw-vs-MMV evidence and integrates a later Core-500 candidate stress check across Small, Medium, and Large governed profiles. The central claim is intentionally bounded. Smoke-100 supports a Raw-vs-MMV improvement-delta claim for route governance. Core-500 does not include a matched Raw control arm and is therefore used as governed-profile level evidence, robustness stress evidence, family-level heterogeneity evidence, and cost-side analysis, not as a large-N proof of governance improvement. Core-500 is a controlled 5x expansion of Smoke-100 using neutral prompt-frame variants; it should not be treated as 500 independent task families or as an independently validated public benchmark standard. This v0.3.6 data-verified final manuscript incorporates post-audit verification of the Core-500 failure-side metrics. The equality between stale_commitment_rate and unsupported_current_claim_rate is confirmed not to be a manuscript copy error. The row-output JSONL files were re-read after Drive synchronization, and the derived row sets are identical with zero symmetric difference across Small, Medium, and Large lines. The labels remain conceptually distinguishable, but in the current Core-500 scorer they are structurally paired under the observed direct-current-claim-without-date-boundary-or-tool-use condition. This record should be read as a working paper and candidate benchmark report. It does not claim an official leaderboard, a universal model-quality score, deployment-wide validation, or external benchmark standard status. Future work includes matched Core-500 Raw arms, route-classifier validation, independent labels, external baselines, clustered or hierarchical uncertainty estimates, and improved handling of volatile_current prompts. Author of record and concept originator: Taiko Toeda.Rights holder and licensing authority: MOBIUS LLC.
This article studies the relationship between creator-related cues and market outcomes – price, time to sale, and non-sale – of non-fungible tokens (NFTs) in a leading marketplace. We first extract textual and visual indicators and summarize them into cognitive and affective composites using principal component analysis. We then estimate hedonic regression and duration models with creator-level random effects and recover creator-related components using empirical Bayes shrinkage. These components provide a descriptive decomposition of market outcomes into variation linked to observable asset cues and residual variation systematically associated with creators. We find substantial heterogeneity in creator-related components for both price and liquidity, while simple social-media metrics account for only a small share of that heterogeneity. We also model non-sale probability and show that creators’ social media activity is modestly associated with sale failure. Methodologically, the paper offers a transparent approach to mapping creator-related heterogeneity when creator metadata and standard brand-equity measures are limited.
This report synthesises findings from 3 peer-reviewed papers addressing the following research question: What is the impact of view dropout on the robustness of multi-view graph anomaly detection frameworks against Metattack perturbations, measured by the degradation in detection accuracy and F1-score. Federated learning (FL) is a machine learning setting where many clients (e.g., mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g., service provider), while keeping the training data decentralized. FL embodies. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 9.0/10. This report is a machine-generated literature synthesis and does not constitute original research. Research goal: What is the impact of view dropout on the robustness of multi-view graph anomaly detection frameworks against Metattack perturbations, measured by the degradation in detection accuracy and F1-score? Autonomous literature synthesis. Automated review score: 9.0/10. Full text and citation available at Assignee Research.
Bagus Rakadyanto Oktavianto Putra, Muhamad Risqi U. Saputra, Widyawan, Guntur Dharma Putra
Smart contracts face critical security challenges that require thorough auditing in decentralized web services. While Large Language Models (LLMs) have shown promise in automated vulnerability detection, existing approaches lack severity evaluations with actionable remediation and demand unnecessarily massive computational overhead. In this study, we introduce an efficient end-to-end smart contract security audit framework utilizing lightweight, highly optimized open-source LLMs (0.6B-4B parameters). Our framework decouples comprehensive audit tasks into four interconnected components: vulnerability detection, explanation, severity classification, and remediation recommendation. To maintain high accuracy without massive parameters, we implement Rank-Stabilized Low-Rank Adapters (rsLoRA), knowledge distillation, and a custom Chain-of-Verification (CoVe) aggregation strategy to systematically screen and consolidate multiple draft responses from the model into a highly accurate audit report. Experimental results demonstrate that our lightweight pipeline consistently outperforms state-of-the-art open-source coder dense LLMs (7B to 34B parameters), achieving 98.25% accuracy in vulnerability detection and an alignment score of 0.4375 in generative explanation tasks. Furthermore, our extensive ablation studies empirically validate the superiority of our decoupled audit processes over unified prompting and uncover a novel severity centrality bias, establishing a critical benchmark for future research in LLM-assisted auditing.
Alternative data availability (AltDA) systems provide Ethereum L2s with an external data publication layer for high throughput rollup designs. By moving bulk data publication outside of Ethereum, AltDA allows L2s to process more data than native DA. However, this replacement introduces a new consensus critical integration layer. Existing ecosystem frameworks identify high level risks, such as external DA trust assumptions and the presence or absence of a DA verifier, but do not provide a complete specification for how an L2 should integrate with AltDA. This gap can lead to L2 halts, inconsistent derivation across honest L2 nodes, invalid state assertions, or bridge attacks. This paper presents a canonical validation framework for secure AltDA integration. We model the boundary as a typed, deterministic, and total translation from L1 inbox bytes to an AltDA commitment, then to externally available data, and finally to the rollup payload consumed by the rest of core L2s logic. The central principle is that every adversarial input must lead to a defined unique outcome. We show how missing obligations lead to concrete failure modes, including underconstrained settlement, derivation halts, inconsistent honest node behavior, invalid state assertions, and bridge safety failures. We then apply the framework to representative AltDA integration architectures, including Celestia-Blobstream, EigenDA based designs, and Avail-ZKsync. Our evaluation shows that secure AltDA integration is not determined solely by the DA provider or bridge. The surrounding L2 integration must also enforce the full validation relation connecting L1 inbox inputs to accepted L2 state.
Decentralized verifiable credential systems have seen limited deployment in practice. Existing constructions, built on zero-knowledge proofs, are complex, application-specific, and largely restricted to predicates over structured data. We present Privately Inferred Credentials ($π$Creds): privacy-preserving, legacy-compatible, decentralized verifiable credentials generated by trusted LLM inference over authenticated data. LLMs' ability to semantically reason over unstructured data substantially expands the range of claims $π$Creds can certify over existing credential systems. The use of LLMs also introduces new application-level threats, which we formalize through two problems: the Source-Constrained Adversarial Example (SCAE) problem, which captures robustness against adversaries that manipulate authenticated data to obtain misleading credentials, and the Authenticated Covert Predicate Poisoning (ACPP) problem, which captures privacy leakage through adversarial model selection. We characterize applications of $π$Creds over user data, and a novel class of credentials over proprietary software that certifies properties of a service without revealing its source code. Our prototype supports issuing credentials over live financial, health, email, and code sources, and we empirically study the SCAE and ACPP threats on a product expertise credential over real financial data.
Smart contract vulnerabilities in Decentralized Finance (DeFi) protocols resulted in over 1.49 billion USD in confirmed losses in 2024 alone, across 192 incidents [1]. As LLM-based vulnerability detection emerges as a promising approach to address these threats, the quality of evaluation datasets has become a critical bottleneck. Existing datasets suffer from three fundamental problems: they are built on outdated Solidity versions (e.g., v0.4) that no longer reflect modern DeFi contracts [5][6][7]; they rely on automated or LLM-generated annotations that introduce hallucination-driven label noise [9][10]; and they apply coarse single-layer labeling that fails to capture the semantic complexity of real-world business logic vulnerabilities [6][7][11][12]. We present Bastet, an expert-labeled DeFi smart contract vulnerability dataset that addresses all three problems through real-world audit findings (2021-2024), human expert annotation with discussion-based consensus, and a two-layer taxonomy of 46 Tags and 77 Subtags. Bastet comprises 4,402 findings collected from 394 Code4rena competitive audit reports spanning April 2021 to November 2024, of which 849 findings are fully annotated by white-hat security researchers from the DeFiHackLabs community. All annotations are produced through a two-annotator consensus workflow, ensuring label accuracy grounded in real-world vulnerability root causes.
CiteChain is a decentralized, blockchain-based platform designed to register, track, and verify scientific citations while rewarding contributors with a native utility token (CITE). This working paper presents the problem statement, proposed system architecture, tokenomics model, and development roadmap for the CiteChain protocol. Current academic citation systems (Scopus, Web of Science, Google Scholar) suffer from centralized control, manipulation, lack of incentives for researchers, and slow updates. CiteChain addresses these issues through a combination of smart contracts on an EVM-compatible blockchain (Ethereum/Polygon), decentralized storage via IPFS/Filecoin, ORCID-based researcher identity verification, and a community-driven citation validation mechanism with economic incentives and anti-abuse slashing penalties. The CITE token (ERC-20, fixed supply of 1 billion) rewards researchers for submitting papers, validating citations, and reporting fraud. Governance is managed through a decentralized autonomous organization (DAO). The protocol is designed to be open-access, permissionless, and censorship-resistant. Keywords: decentralized science, DeSci, blockchain, citation network, smart contracts, tokenomics, ORCID, IPFS, academic integrity, open science, Polygon, ERC-20.
Abstract Ethereum's transaction validity model is currently anchored in ECDSA over secp256k1, whose security assumptions weaken in the presence of large-scale quantum adversaries. While NIST-standardized post-quantum signature schemes such as ML-DSA, SLH-DSA, and FALCON provide resistance against quantum attacks, integrating these schemes into Ethereum introduces significant systems-level challenges involving bounded execution, gas determinism, and adversarial verification complexity. This paper introduces PQSigAbstract, a modular post-quantum signature verification architecture for Ethereum that separates validation into a stateless pre-validation phase and a deferred cryptographic verification phase linked through commitment binding. The design defines typed Verification Modules with explicit gas estimation, a versioned Scheme Registry with quarantine-based deployment safety, and a probabilistic aggregation mechanism for non-aggregatable post-quantum schemes. The proposed architecture preserves EU-CMA security while maintaining compatibility with ERC-4337 and RIP-7560 account abstraction models. Formal gas cost models are derived for ML-DSA-44, FALCON-512, and SLH-DSA-128f, and empirical evaluation demonstrates practical deployment feasibility for high-value Ethereum accounts despite substantially higher verification costs relative to ECDSA. Status: Technical Report / Working Paper Author: Ankita Virani Affiliation: University of Colorado Boulder
M. Shunmugasundaram, S. Gangadharan, Rina Dave, Saraswati Kala · 5 authors
One of the main reasons for the surge of Shadow AI is the widespread use of AI technology in businesses. One of the major drivers behind the increasing prevalence of Shadow AI is the integration of AI technology in enterprise environments. As Artificial Intelligence (AI) becomes ubiquitous in the enterprise, Shadow AI has surged in the number of organizations using AI out of control or without authorization. This research paper explores how Shadow AI has developed from the traditional Shadow IT concept in an autonomous enterprise context where AI use is decentralized, agents are used to automate processes, and the decision-making is machine-driven. The paper discusses governance blind spots like hidden AI integrations, non-human identities, lack of explainability, AI drift, and autonomous risks. It uses a qualitative approach with review, analysis, and synthesis to create a shadow AI governance framework. This framework includes AI discovery, telemetry monitoring, zero-trust controls, explainability, and continuous auditability. The findings show that existing approaches to IT governance are insufficient for adaptive ecosystems of AI, and enterprise governance must be continually monitored, documented, and tracked; resilient to cyber threats; compliant with regulations; and ensure digital trust.