Blockchain Papers

Y. Durga Tejaswi, A. Sai Divya Sree, B. Sai, R. Siva Santhi

No abstract is available for this record.

Chaoyuan Peng, Lei Wu, Yajin Zhou

EVMbench, released by OpenAI, Paradigm, and OtterSec, is the first large-scale benchmark for AI agents on smart contract security. Its results -- agents detect up to 45.6% of vulnerabilities and exploit 72.2% of a curated subset -- have fueled expectations that fully automated AI auditing is within reach. We identify two limitations: its narrow evaluation scope (14 agent configurations, most models tested on only their vendor scaffold) and its reliance on audit-contest data published before every model's release that models may have seen during training. To address these, we expand to 26 configurations across four model families and three scaffolds, and introduce a contamination-free dataset of 22 real-world security incidents postdating every model's release date. Our evaluation yields three findings: (1) agents' detection results are not stable, with rankings shifting across configurations, tasks, and datasets; (2) on real-world incidents, no agent succeeds at end-to-end exploitation across all 110 agent-incident pairs despite detecting up to 65% of vulnerabilities, contradicting EVMbench's conclusion that discovery is the primary bottleneck; and (3) scaffolding materially affects results, with an open-source scaffold outperforming vendor alternatives by up to 5 percentage points, yet EVMbench does not control for this. These findings challenge the narrative that fully automated AI auditing is imminent. Agents reliably catch well-known patterns and respond strongly to human-provided context, but cannot replace human judgment. For developers, agent scans serve as a pre-deployment check. For audit firms, agents are most effective within a human-in-the-loop workflow where AI handles breadth and human auditors contribute protocol-specific knowledge and adversarial reasoning. Code and data: https://github.com/blocksecteam/ReEVMBench/.

Gerhard Hirschmann, Elisabeth Steurer

We present OR1ON (Epistemic Intelligence Reasoning Architecture — EIRA), a deterministic proof-based AI system that learns rules from data but applies them only when formally proven correct on all training examples. Unlike probabilistic ML systems, OR1ON's core primitive prove(rule, examples) returns binary decisions: apply with certainty, or abstain. Developed initially for abstract spatial reasoning (ARC-AGI benchmark, 95% precision on answered tasks), the architecture generalizes directly to safety-critical industrial domains including predictive maintenance (zero false positives), ISO 26262-compatible safety monitoring, energy grid blackout prevention, and OT/SCADA intrusion detection. OR1ON is, to our knowledge, the first data-learning system to produce formally verifiable safety invariants applicable to IEC 61508 SIL-3 certification. Addressable market across five industrial verticals: ~$44 billion.

Anthony Coslett

This paper presents the philosophical and conceptual implications of a four-paper research program (Papers 1–4 in this series) that discovered a measurable structural identity in neural networks — a geometric property of the trained weights, invariant across all inputs and deployment conditions, unique to each model, and provably impossible to forge. The central argument: language models possess two separable layers of identity. The first is structural — a mathematical fingerprint determined by the weight geometry, fixed at the end of training, stable to a coefficient of variation of 1.4%, and validated across 37 models spanning four architecture families. The second is functional — a behavioral signature shaped by conversational context, transient and context-dependent. These layers coexist without reducing to each other. The structural layer is the foundation; the functional layer is built on it but not determined by it. The paper introduces the Two-Layer Identity framework, resolves four open puzzles in the discourse on AI selfhood (conversational consistency, fine-tuning continuity, identity faking, and neural intervention), and generates five falsifiable predictions for the interpretability and AI safety communities. It engages directly with Dennett's narrative gravity, Parfit's persistence conditions, and Schwitzgebel's moral status dilemma, arguing that the structural measurement provides a necessary (though not sufficient) ground for any coherent account of AI identity. Written for a general audience. No equations. The mathematical and empirical foundations are developed in Papers 1–4; the formal verification (352 theorems, zero Admitted, Coq proof assistant) is documented there. This paper asks what those results mean for the nature of the entities we have built. The Neural Network Identity Series — Mathematical foundations, empirical validation, and governance frameworks for verifying which model is running Newest addition: Technical Note: The Disappearing Window — AI Logprob Access Withdrawal and the Structural Verifiability of Frontier Model Contracts (DOI: 10.5281/zenodo.20362098) Paper 1: The δ-Gene: Inference-Time Physical Unclonable Functions from Architecture-Invariant Output Geometry (DOI: 10.5281/zenodo.18704275) Paper 2: Template-Based Endpoint Verification via Logprob Order-Statistic Geometry (DOI: 10.5281/zenodo.18776711) Paper 3: The Geometry of Model Theft: Distillation Forensics, Adversarial Erasure, and the Illusion of Spoofing (DOI: 10.5281/zenodo.18818608) Paper 4: Provenance Generalization and Verification Scaling for Neural Network Forensics (DOI: 10.5281/zenodo.18872071) Paper 5: Beneath the Character: The Structural Identity of Neural Networks — Mathematical Evidence for a Non-Narrative Layer of AI Identity (DOI: 10.5281/zenodo.18907292) Paper 6: Which Model Is Running?: Structural Identity as a Prerequisite for Trustworthy Zero-Knowledge Machine Learning (DOI: 10.5281/zenodo.19008116) Paper 7: The Deformation Laws of Neural Identity (DOI: 10.5281/zenodo.19055966) Paper 8: What Counts as Proof? — Admissible Evidence for Neural Network Identity Claims (DOI: 10.5281/zenodo.19058540) Paper 9: Composable Model Identity — Formal Hardening of Structural Attestations in the Enterprise Identity Stack (DOI: 10.5281/zenodo.19099911) Paper 10:Where Identity Comes From: Path Sensitivity and Endpoint Underdetermination in Neural Network Training (DOI: 10.5281/zenodo.19118807) Paper 11: Post-Hoc Disclosure Is Not Runtime Proof: Model Identity at Frontier Scale (DOI: 10.5281/zenodo.19216634) Paper 12: Family-Dependent Response to Reasoning Distillation Across Structural and Functional Identity Layers (DOI: 10.5281/zenodo.19298857) Paper 13: Safety-Alignment Removal as a Model-Identity Failure — Structural Evidence from Published Weight-Level Mutation Checkpoints (DOI: 10.5281/zenodo.19383019) Technical Note: Agent Identity Is Not Model Identity (DOI: 10.5281/zenodo.19240883) Technical Note: Gap Invariance: Why PPP Measurements Are Domain-Independent by Construction (DOI: 10.5281/zenodo.19275524) Technical Note: Measured Model Substitution Under Valid Agent Credentials (DOI: 10.5281/zenodo.19342848) Technical Note: Artifact Identity Is Not Runtime Identity — Trustfall Lite and the Boundary of File-Level Model Verification (DOI: 10.5281/zenodo.20019127) Formal Verification Stack for Neural Network Structural Identity (IT-PUF Coq Proofs) (DOI: 10.5281/zenodo.18930621) Copyright (c) 2026 Anthony Ray Coslett / Fall Risk AI, LLC. All Rights Reserved. Confidential and Proprietary. Patent Pending (Applications 63/982,893, 63/990,487, 63/996,680, 64/003,244).

S. Chandrakala, S. Chandrakala

The current trends in the cyber threat landscape of distributed systems have required a paradigm shift to decentralized and thrustless security. The research suggests a new architecture, Federated Adversarial-AI for Zero-Trust Explainable Cybersecurity (FAZTEC), combining federated learning and adversarial artificial intelligence to help make the cybersecurity systems more resilient, and explainable. The proposed framework, with the help of federated learning, would allow interconnected threat detection on edge devices, which does not require sharing raw data since it would keep privacy and meet the criteria of regulatory requirements. The same happens through the use of adversarial AI in order to simulate advanced attack scenarios and thus strengthen the defines mechanisms of the threats that are evolving. Auditioning explainable AI (XAI) modules also increases transparency in the system, where the security analyst can understand and verify the detection results in real-time. The zero-trust architecture also verifies a device, user, and data flow continuously, which discards the implicit assumptions about trustworthiness. A wide range of experiments performed in various network environments proves the effectiveness, validity, and interpretability of FAZTEC, which represents a serious breakthrough in proactive cybersecurity protection. The work is useful to the future of security infrastructure, which is smart, decentralized and explainable, and applicable to critical applications in finance, healthcare, and government.

Tanusree Sharma, Yujin Potter, Jongwon Park, Yiren Liu · 9 authors

A major criticism of AI development is the lack of transparency, such as, inadequate documentation and traceability in its design and decision-making processes, leading to adverse outcomes including discrimination, lack of inclusivity and representation, and breaches of legal regulations. Underserved populations, in particular, are disproportionately affected by these design decisions. Furthermore, traditional social science techniques such as interviews, focus groups, and surveys struggle to adequately capture user needs and expectations in the digital era, due to their inherent limitations in deliberation, consensus-building, and providing consistent insights. We developed a democratic decision framework utilizing Decentralized Autonomous Organization (DAO) to enable underserved groups to deliberate and reach a consensus on key AI issues. To assess our proposed democratic decision mechanism, we conducted a case study on updating AI model specification based on diverse stakeholders input. We focus on reducing stereotypical biases in text-to-image systems, particularly gender bias in image generation from text prompts. We designed and experimented various governance configurations, including decision aggregation schemes and decision power, to examine how democratic processes could guide updates to AI model. Through a 2 × 2 experimental design, we tested various aggregation schemes (ranked vs. quadratic) and decision power distribution (equal vs. 20/80 differential) in a randomized online experiment (n=177) with participants from the global south and people with disabilities, to study how the varying governance mechanisms impact people's perceptions of the decision-making processes and resulting output of the AI Model specification. Our results indicate that despite their diverse backgrounds, participants showed convergence in deliberations on several aspects, including user control over image generation, multiple output options for user selection, and the social appropriateness and accuracy of generated images. Our study underscores the importance of use of appropriate governance in democratic decision-making in AI alignment. Notably, the combination of quadratic preference aggregation method which gives minorities more voice and equal decision power distribution, was perceived as a fairer and democratic approach.

Azariah Jebin

Modern insurance organizations have adopted artificial intelligence in narrow, task-specific roles, resulting in fragmented systems that optimize isolated functions without fundamentally reshaping the underwriting and claims lifecycle. This “incrementalism” yields a human-default, sequential process plagued by structural bottlenecks, inconsistent risk evaluation, and limited transparency. This paper introduces NEXUS (Next-Generation Executive Underwriting and Settlement Intelligence), a framework to re-architect insurance as an AI-native system. NEXUS transitions AI from a peripheral tool to the primary orchestrator of end-to-end processes, conceptualizing the insurance lifecycle as a conversational, agent-orchestrated workflow. It is realized through a unified conversational interface that coordinates a decentralized ecosystem of specialized, collaborative AI agents each responsible for domain-specific reasoning such as geospatial risk assessment, financial verification, or medical outcome analysis. The central innovation is the Truth Score Engine (TSE), a governance-first aggregation mechanism that non-linearly synthesizes agent outputs by weighting evidentiary provenance, confidence estimates, and cross-agent consistency. The TSE governs decisions via a Three-Tiered Confidence Protocol: • High Confidence (>90%) validates outcomes for immediate human sign-off without re-verification; • Medium Confidence (60-90%) routes decision summaries for targeted human review of specific flags; • Low Confidence (<60%) escalates cases as ‘’Risky,’’ reverting to traditional manual investigation. This protocol yields a single, auditable decision artifact while preserving full traceability of the reasoning pathway. By embedding multi-agent coordination, contextual awareness, and tiered governance at the architectural level, NEXUS demonstrates a scalable pathway toward adaptive, transparent insurance systems. It ensures precision, combats fraud, and dramatically reduces settlement time, positioning AI-native governance as a foundational requirement for deploying trusted, autonomous decision-making in high-stakes financial domains.

Alan Watkins, G. C. Cooke

Life on Earth is essentially a story of connections and collaboration. The way we have collaborated over time is largely down to value systems and it is these same value systems that determine how we collaborate with AI. But it’s not just value systems that may hold us back. Cooperation and competition have always been different sides of the same “human” coin. It is our ability to cooperate that has been the key to our survival and prosperity as a species. But that cooperation has always had its limits. This chapter explores how those limits can be transcended if AI is built from second tier value systems on decentralised web3 ecosystems and not centralised top-down hierarchies. Web3 already supports a multi-TRILLION dollar ecosystem and its architecture facilitates our collective evolution up the values spiral. In other words, web3 together with AI could facilitate human evolution as it removes some of the hurdles that prevent us from mutually beneficial collaboration at scale. In this new economy the winners will be those who choose to embrace web3 and AI in some type of hybrid work style.

Pratik Save

We present Viturka, a blockchain architecture that replaces wasteful proof-of-work mining with productive federated learning. The core innovation is Proof of Credibility (PoC): a consensus mechanism where block production probability is determined by accumulated reputation from validated AI contributions rather than computational hash power or financial stake. Viturka leverages recent breakthroughs in Zero-Knowledge Machine Learning (ZKML) to achieve cryptographic verification of model training. Validators generate zero-knowledge proofs attesting to correct training execution, enabling instant on-chain verification without trusted intermediaries or statistical consensus mechanisms. By integrating frameworks like EZKL and Lagrange's DeepProve with GPU-accelerated proving via the Icicle library, validation that previously required hours of recomputation now produces mathematical proofs verifiable in milliseconds. Participants earn credibility by contributing quality training data or validating others' contributions. Only the top 10 highest-credibility validators can participate in validation rounds, with mandatory cooldown periods ensuring rotation. The system uses a temporal commit-reveal scheme for data contributions combined with ZK proofs for validation—fake contributions result in permanent bans, while fraudulent validation is mathematically impossible. This creates infrastructure for training AI models on distributed data without central coordination, with economic incentives aligned toward data quality rather than raw computation. Applications range from commercially valuable use cases like DeFi credit scoring—which could unlock over $100B in overcollateralized capital—to public-good AI for rare diseases, minority languages, and environmental monitoring.

Zhenyu Xu, Victor S. Sheng

Protecting the intellectual property of large language models (LLMs) is a critical challenge due to the proliferation of unauthorized derivative models. We introduce a novel fingerprinting framework that leverages the behavioral patterns induced by safety alignment, applying the concept of refusal vectors for LLM provenance tracking. These vectors, extracted from directional patterns in a model's internal representations when processing harmful versus harmless prompts, serve as robust behavioral fingerprints. Our contribution lies in developing a fingerprinting system around this concept and conducting extensive validation of its effectiveness for IP protection. We demonstrate that these behavioral fingerprints are highly robust against common modifications, including finetunes, merges, and quantization. Our experiments show that the fingerprint is unique to each model family, with low cosine similarity between independently trained models. In a large-scale identification task across 76 offspring models, our method achieves 100\% accuracy in identifying the correct base model family. Furthermore, we analyze the fingerprint's behavior under alignment-breaking attacks, finding that while performance degrades significantly, detectable traces remain. Finally, we propose a theoretical framework to transform this private fingerprint into a publicly verifiable, privacy-preserving artifact using locality-sensitive hashing and zero-knowledge proofs.

Walter Kurz, Michel Malara, Wojtek Stricker, Eva Albrecht

The objective of this study is to define a compliance-first, conceptually generalisable architecture for a multi-agent artificial intelligence platform integrated with distributed ledger technology, designed to be domain-, deployment-, and vendor-agnostic. It addresses a persistent shortcoming in current AI deployments, where compliance is often treated as a secondary concern, applied retroactively through prompt engineering rather than embedded within the foundational design. The proposed model encodes regulatory, governance, and ESG requirements into an objective-under-constraints framework, ensuring that all specialised agents operate within legally admissible and verifiably auditable parameters prior to any domain-specific implementation. A DAG-based verification layer is incorporated to enable scalable, low-latency, and cost-efficient operation while preserving evidentiary integrity. The analysis evaluates the feasibility of this conceptual model to support sustainable, rapid-deployment vertical applications without inducing vendor lock-in, preserving operational neutrality, and ensuring environmental accountability. The findings suggest that integrating compliance, ESG metrics, and agent specialisation at the architectural level provides a transferable foundation for cross-domain AI–DLT infrastructures.

Xiao-Yang Liu, Ningjie Li, Keyi Wang, Xiaoli Zhi · 5 authors

Financial Generative Pre-trained Transformers (FinGPT) with multimodal capabilities are now being increasingly adopted in various financial applications. However, due to the intellectual property of model weights and the copyright of training corpus and benchmarking questions, verifying the legitimacy of GPT's model weights and the credibility of model outputs is a pressing challenge. In this paper, we introduce a novel zkFinGPT scheme that applies zero-knowledge proofs (ZKPs) to high-value financial use cases, enabling verification while protecting data privacy. We describe how zkFinGPT will be applied to three financial use cases. Our experiments on two existing packages reveal that zkFinGPT introduces substantial computational overhead that hinders its real-world adoption. E.g., for LLama3-8B model, it generates a commitment file of $7.97$MB using $531$ seconds, and takes $620$ seconds to prove and $2.36$ seconds to verify.

Dipesh Badal, Abdelsalam Busalim, Donghyeok Lee

High-frequency crypto forecasting requires systems that are accurate, explainable, and designed for human decision-making. Bitcoin presents a unique challenge for Human-Centred AI (HCAI) due to its volatility and sensitivity to heterogeneous technical, fundamental, and sentiment signals. This paper presents an explainable multimodal framework for Bitcoin forecasting at 15-minute resolution. We align five modalities—market data, on-chain metrics, the Fear & Greed Index (FGI), news, and Reddit—onto a unified, leakage-safe 15-minute grid. We evaluate tree-based, sequential, and Multimodal Fusion Block (MFB) models for next-interval log-return prediction using chronological splits. Results show that while short-horizon prediction remains challenging, multimodal features consistently improve over structured baselines, particularly during event-driven periods. To ensure transparency, the framework integrates a dual-layer explanation system: SHapley Additive exPlanations (SHAP) attributions combined with large language model (LLM) narratives, ensuring outputs are both technically faithful and human-accessible. This work unlocks the “black box” of complex predictive architectures, transforming opaque multimodal signals into transparent, actionable decision support for high-frequency trading.

Arafat Rohan, Md. Deluar Hossen, Md. Nuruzzaman Pranto, Balayet Hossain · 6 authors

This study reviews the advancements in AI-driven methods for predicting stock prices, tracing their evolution from traditional approaches to modern finance. The role of AI in the market extends beyond predictive systems to encompass the intersection of financial markets with emerging technologies, such as blockchain, and the potential influence of quantum computing on economic modeling. A decentralized finance system examines the application of Reinforcement Learning in financial market prediction, highlighting its potential for continuous learning from dynamic market conditions. The study discusses the development of hybrid prediction models, stock market machine learning systems, and AI-driven investment portfolio management. The potential of quantum computing enhances portfolio analysis, fraud detection, optimization, and asset valuation for complex market predictions, as well as the impact of blockchain technologies on transparency, security, and efficiency. Machine learning techniques can significantly automate data collection and purification. Financial decision-making and the application of time-series analysis techniques can be readily learned through deep reinforcement learning for stock price prediction. Deep Neural Networks and Strategic Asset Allocation can be managed by evaluating performance and portfolio using real-time market insights from AI models. Although there are numerous ethical, sentimental, regulatory, and data quality issues in market prediction, the future job market is heavily dependent on these criteria, particularly through effective risk management and fraud detection.

Rino Thomas, Savitha K.K

Decentralized Finance (DeFi) has changed the financial ecosystem but is extremely vulnerable to complex schemes such as rug pulls, honeypots, and flash loan attacks, which cause massive losses for platforms and users. This study responds to the critical necessity of early and actionable warning for fraud by suggesting a real-time system predicting and justifying the risk level of freshly launched DeFi tokens prior to their engagement with the users. Drawing on a hybrid methodology, integrating smart contract code analysis, on-chain behavioral data, and social metrics, the system utilizes cutting, edge machine learning models, including Graph Neural Networks (GNNs) and ensemble methods (XGBoost, FT-Transformer), to provide sophisticated risk scoring. For user and regulatory trust assurance, explainable AI methods such as SHAP and LIME are utilized to clearly identify important risk drivers, including unsafe contract functions, wallet concentration, and liquidity lock patterns. The solution provides stage-aware and cross-chain surveillance, coupling functionality like anomaly detection, federated model training, and governance analysis. With large-scale literature synthesis and empirical validation, this framework shows that proactive, pre-transaction fraud identification and open risk valuation are possible, and it achieves a scalable defense for DeFi players and infrastructure.

Duc Thinh Ngo, Arya Rahgoza

Systematic reviews are essential for evidence-based medicine, but reviewing 1.5 million+ annual publications manually is infeasible. Current AI approaches suffer from hallucinations in systematic review tasks, with studies reporting rates ranging from 28--40% for earlier models to 2--15% for modern implementations which is unacceptable when errors impact patient care. We present a causal graph-enhanced retrieval-augmented generation system integrating explicit causal reasoning with dual-level knowledge graphs. Our approach enforces evidence-first protocols where every causal claim traces to retrieved literature and automatically generates directed acyclic graphs visualizing intervention-outcome pathways. Evaluation on 234 dementia exercise abstracts shows CausalAgent achieves 95% accuracy, 100% retrieval success, and zero hallucinations versus 34% accuracy and 10% hallucinations for baseline AI. Automatic causal graphs enable explicit mechanism modeling, visual synthesis, and enhanced interpretability. While this proof-of-concept evaluation used ten questions focused on dementia exercise research, the architectural approach demonstrates transferable principles for trustworthy medical AI and causal reasoning's potential for high-stakes healthcare.

Ayan Roy, Kaustuvi Basu, Rik Chakraborti, Riley McDonough

Federated learning enables multiple data owners to collaboratively train a global model, but verifying the correctness of submitted updates remains a critical challenge. Malicious clients may poison model weights, and colluding validators can undermine aggregation. We propose DI-FLAME, a decentralized validation framework that introduces a stake-and-proof mechanism for model verification. Each claim is submitted with a justification and stake, evaluated via a black-box credibility function. Validators engage in peer review, and contradictors may challenge weak claims by submitting stronger proofs and higher stakes. Rewards and penalties are dynamically distributed based on justification strength and challenge outcomes. DIFLAME provides robust defense against poisoned updates, even under adversarial majorities. While blockchain infrastructure is not required, DI-FLAME is compatible with with decentralized ledgers, enabling transparent recording of validation outcomes when deployed over a blockchain.

Osha Shukla

No abstract is available for this record.

Ahmed Abbas Jasim Al‐Hchaimi, M. A. Khalifa, Walid El‐Shafai

ABSTRACT Blockchain networks now support billions of dollars in daily transactions, making reliable and transparent fraud detection essential for maintaining user trust and financial stability. Yet, real‐world blockchain datasets are extremely imbalanced, with fraudulent activity representing less than 1% of all transactions. This imbalance causes conventional machine learning models to achieve deceptively high accuracy while still failing to detect a substantial portion of fraudulent events. To address this challenge, this study evaluates the performance and explainability of three models‐XGBoost, LightGBM, and Decision Tree‐on the Ethereum‐based fraud detection data, in which 58% of transactions are identified as fraud. The methodology combines vast feature engineering, k‐fold cross‐validation, and assorted resampling approaches, such as Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic Sampling Nearest Neighbor (ADASYN), to revise the effect of class mismatch. Accuracy, AUC, recall, precision, F1‐Score, and Matthews Correlation Coefficient(MCC) are used to measure model performance, and SHapley Additive exPlanations (SHAP) is utilized to give global and local interpretability. Experimental results show that XGBoost combined with SMOTE or ADASYN yields the strongest performance, achieving a recall over 99%, an AUC of 1.000, and a substantially improved MCC compared to training on the raw imbalanced data. LightGBM presents a favourable precision‐recall balance, and Decision Trees demonstrate significant gains after resampling, despite their simplicity. SHAP analysis reveals that log‐transformed transaction amount, merchant‐based encoding, geographic encoding, and temporal features are the primary contributors to fraud risk. These results are important in highlighting two implications: (i) the importance of dealing with extreme class imbalance, rather than choosing increasingly sophisticated approaches, and (ii) the ability to be trusted to be explained is a requirement of responsible working in both financial and blockchain settings. The research offers a pragmatic, interpretable framework on blockchain fraud detection and future directions, including sophisticated hybrid sampling, collective learning, as well as cross‐chain generalization to enhance fraud detection in distributed systems.

Anatoly Piskunov

Prediction markets aggregate dispersed information into probabilistic forecasts that consistently outperform polls and expert panels, yet their adoption is constrained by a structural liquidity problem: providers of market depth bear adverse selection risk that deters retail participation. We present the Onix Protocol, a hybrid architecture that decouples the pricing function from the settlement function in prediction markets. By pairing automated market maker pricing-a Constant Product Market Maker (CPMM) for binary outcomes and a Logarithmic Market Scoring Rule (LMSR) for multi-outcome markets-with parimutuel (totalizator) settlement, we achieve a structural guarantee that liquidity-provider principal is never at risk from betting outcomes. We formalize the protocol's economic invariants, prove the LP principal guarantee for both market types, describe a "Lazy" liquidity pool enabling passive retail participation, analyze the dispute resolution mechanism under DAO governance, and discuss the experimental hypotheses this system is designed to test. The protocol is implemented as consensus-level operations on the VIZ distributed ledger.

Pratyush Dikshit, Igor Tchappi, Amro Najjar

Autonomous remote robots [16, 15] are increasingly deployed in disaster response scenarios [14] to support critical tasks such as victim localization and damage assessment. However, the ambient nature of such environments, which is marked by uncertainty, data heterogeneity, and limited connectivity, usually poses significant challenges to autonomous decision-making and trust. Thus, this paper proposes a framework for a multi-layered approach for a hybrid agent architecture that integrates Federated Learning (FL) with Belief-Desire-Intention (BDI) models, enabling remote robotic agents to learn collaboratively from distributed data using Distributed Ledger Technology (DLT) while preserving privacy, and to reason about their goals and intentions using cognitive frameworks of eXplainable AI (XAI). We further present a methodology for coherently embedding FL outcomes into BDI reasoning through semantic mapping and learning-enhanced ontologies. This integration will allow agents to dynamically update their beliefs and intentions based on learned insights, thereby enhancing autonomy, adaptability, and explainability in ambient disaster response systems.

Achim Struve

No abstract is available for this record.

Heath Emerson

Large language models and agentic AI systems deployed in regulated, safety-critical, and high-stakes enterprise environments require governance infrastructure that is simultaneously cryptographically verifiable, regulatorily defensible, operationally efficient, and natively explainable. Existing approaches treat these properties as separate concerns addressed by separate toolchains. The result is an accountability architecture that is fragmented, difficult to audit end-to-end, and structurally incapable of satisfying the converging global regulatory requirement that AI decisions be not merely governed but explainable. This paper presents THEMIS-xAI (Trusted High-Assurance Evidence Management Integrity System for Explainable AI): a unified, governance-native framework that integrates cryptographic evidence management, runtime policy enforcement, explainability generation, privacy-preserving verification, and continuous compliance monitoring into a single coherent architecture. THEMIS-xAI is organized around four architectural planes-Evidence, Control, Security, and Explainability-and eleven integrated subsystems. We demonstrate that THEMIS-xAI achieves 83% coverage of the NIST AI RMF 1.0 control set (advancing from a 72% baseline), provides architectural coverage of fifteen regulatory frameworks with per-control status disclosure, and produces per-decision explanation artifacts that are cryptographically anchored, independently verifiable, and structured to align with the transparency and documentation goals of applicable AI governance frameworks. Legal sufficiency requires independent regulatory assessment. Implementation status is transparent throughout: the Evidence and Control Planes are in active enterprise pilot deployment; the Explainability Plane modules M1-M4 are implemented; M5-M6 are at research-prototype stage; zero-knowledge enforcement proofs are at proof-of-concept stage with production hardening planned in Phase 4.

Oluwaseye A. Fawale

Artificial intelligence systems are deployed globally at an unprecedented scale. Yet, no universal mechanism exists to verify that a given AI system is operating within its declared parameters, compliant with applicable regulations, or free from compromise. Trust in AI today is largely assumption-based rather than evidence-based, and this gap is becoming increasingly consequential as AI systems take on greater autonomy in regulated, high-stakes domains. This document proposes the TrustLayer Protocol as one possible architectural framework for addressing this gap. The protocol comprises two complementary components. The Compute Passport Network (CPN) proposes a neutral, global identity and attestation layer for AI compute, models, and training data, establishing cryptographically verifiable records of provenance during the training and development phase. The Universal AI Attestation Protocol (UAAP) proposes an embedded attestation mechanism for deployed AI systems, generating structured, signed claims about runtime operational state, compliance status, and inference provenance. Several UAAP mechanisms, in particular, continuous per-inference behavioral attestation and reasoning verification, remain experimental or require future research, as detailed in Section 6. The TrustLayer Protocol draws on established infrastructure from hardware-based trusted execution environments [1][2], public key infrastructure [3], zero-knowledge proof systems [4][5], and AI governance frameworks, including the EU AI Act [6] and the NIST AI Risk Management Framework [7]. It does not claim to resolve all open problems in AI verification. Rather, it proposes a phased architectural framework within which existing technologies can be composed into a coherent trust infrastructure layer, with clearly identified areas requiring further research and standardization. This specification is published as an open standard by the DefenAware Foundation and is intended as a contribution to ongoing work in AI governance, protocol design, and verifiable AI safety.