CapIX is a decentralized physical infrastructure network (DePIN) designed to aggregate volatile, untrusted consumer-grade hardware and public container fleets into a single high-performance compute fabric. This technical report presents a practical dual-runtime execution model that addresses the core multi-tenant security challenges on untrusted permissionless hosts. On hardware-virtualization-capable bare-metal nodes, CapIX deploys lightweight micro-VMs isolated by hardware Trusted Execution Environments (TEEs). On standard unprivileged edge containers, it utilizes strict process-level sandboxing, syscall filtering allow-lists, and direct GPU passthrough to achieve native CUDA/ROCm execution performance. The system coordinates fleet routing using a transparent, multi-factor weighted geometric scoring engine resistant to economic manipulation. To achieve scalable, lightweight state verification without the prohibitive overhead of full zero-knowledge proofs on every job, the architecture introduces an interactive bisection fraud game that isolates deterministic instruction disputes to a single step for targeted zkVM proof generation, relying on probabilistic canary spot-checking for non-deterministic workloads.
Open access
2 source records
Security and Verification in Computing
Physical Unclonable Functions (PUFs) and Hardware Security
Aletheia is a knowledge substrate organized around a write-time admission gate: a fact is accepted only if it does not structurally contradict what the base already holds. The gate inherits a Lean 4 soundness proof, so the admitted store stays acyclic, asymmetric, type-disjoint, and temporally consistent under any stream of typed edges. We bind the proof to the implementation by differential testing over 104 adversarial inputs, zero divergences. We first tried to build a partial-truth disinformation detector on this gate. Measurement refused. On real political claims almost nothing decomposes into the gate’s six relations: 0 of 155 atoms were gate-testable, and where it did fire it lost to a cold language model, 0 of 21 against 17. Most real disinformation violates truth, not structure, so a structural gate is the wrong instrument. We retract the detector claim. What remains is a guarantee rather than a rate. Each catch names the axiom it violated; the verdict is bit-exact and carries a machine-checked admission proof; and a safety property whose core is now machine-checked in Lean holds that no finite feed of self-asserted credibility can mint a false endorsement, conditional on authority granted upstream (0 of 210 adversarial sequences, against 140 of 210 for a credibility-naive baseline). A frontier model matches our hit-rate on constructed distortions, and a reasoning model matches even our one structural edge, so we claim no detection advantage. We claim instead that the jurisdiction of a structural guarantee can be measured, and we measure it across two regimes: where the base lets it adjudicate, and where it abstains.
Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existing LLM-based methods struggle to generate this knowledge automatically: prompt-based methods rely on manually crafted detection rules, while fine-tuning requires massive labeled datasets that are inherently scarce in this domain. We present EvoVuln, an automated framework that reformulates vulnerability detection as a procedural knowledge evolution problem, synthesizing and refining detection logic using only a minimal number of labeled samples. To achieve this, EvoVuln introduces two key mechanisms. First, a Runtime with an Inversion of Control (IoC) architecture compiles detection rules into Executable Policies. This strictly decouples deterministic control flow from LLM semantic reasoning, ensuring faithful logical adherence and producing dense diagnostic telemetry for precise error localization. Second, a two-phase evolution pipeline refines the rule via abductive semantic debugging without any parameter updates: Cold Start bootstraps and stress-tests an initial rule using auto-synthesized corner cases; Few-Shot Evolving then grounds the policy in real-world semantics using only five vulnerable and five safe examples per vulnerability type. Evaluated across five real-world vulnerability types, EvoVuln achieves a 71% macro-average F1-score, outperforming all baselines. The evolved procedural knowledge is portable across models: it enables a lightweight, low-cost model to surpass a much larger zero-shot model by 19 percentage points, and transfers to other LLMs without retraining, at a one-time evolution cost under $50.
Zero-Knowledge Proof (ZKP) is a cornerstone in privacy-preserving computing, addressing critical challenges in domains such as finance and healthcare by ensuring data confidentiality during computation. However, the high computational overhead of ZKP, particularly in proof generation and verification, limits its scalability and usability in real-world applications. Existing efforts to accelerate ZKP primarily focus on specific components, such as polynomial commitment schemes or elliptic curve operations, but fail to deliver an integrated, flexible, and efficient end-to-end solution that includes witness generation on commercial computing platforms.
Abstract Smart contracts are the fundamental building block of decentralized applications (DApps) and decentralized finance (DeFi). However, their immutability makes security flaws exceptionally costly. Despite advancements in vulnerability detection, such as static and dynamic analysis, formal verification, and Solidity language improvements, vulnerabilities continue to result in substantial financial losses, exceeding $2 billion in 2024 alone. This paper presents a comprehensive analysis of smart contract vulnerabilities derived from real-world exploits, systematically categorized into seven distinct types. Each category is illustrated with Solidity code examples and insights from notable exploits. An Enhanced test suite is developed by restructuring the existing solidity-defects-and-bugs suite and supplementing it with new smart contract implementations to address underrepresented vulnerabilities, including flash loan and price oracle manipulation. We evaluate three widely used analysis tools (Slither, Mythril, and 4naly3er) on both the original and Enhanced suites, revealing substantial limitations in detection coverage. To address these limitations, we introduce the Solidity Defects and Bugs Analysis (SDABA), which incorporates advanced analyses and detectors to identify 28 vulnerability variations across both suites. Results on the SDB and Enhanced test suites show that SDABA improves overall precision, recall, and F1-score compared with the evaluated tools. Finally, we release the source code, test suite, and vulnerability reports to support future research in smart contract security.
Jianzhong Su, Mingxi Ye, Jiachi Chen, Yuhong Nan · 7 authors
With the rapid development of decentralized applications, many malicious actors exploit smart contract vulnerabilities for launching attacks. Moreover, as smart contracts utilize more state variables to support complex functionalities, some vulnerabilities require specific states to trigger (marked as vulnerable states), bringing new challenges to the vulnerability detection task. Although many smart contract fuzzers have been proposed for this task, they face limitations due to their inability to efficiently explore smart contract states. To address this challenge, we propose a novel fuzzer, Odyssey, with fine-grained state modeling and exploration, which increases the probability of reaching vulnerable states. We improve the efficacy of the fuzzer with two key mechanisms: (1) modeling an essential state space consisting of the variables related to sensitive operations to compress the exploration scope; (2) designing state-aware exploration strategies to identify test seeds that cover new state scope or cause new state transitions, to improve the efficiency of exploration. To evaluate the performance in vulnerability detection, we adopt Odyssey to a labeled benchmark consisting of 130 vulnerable contracts. Odyssey detects at least 70% more vulnerabilities than other fuzzers. Moreover, we evaluate Odyssey on a dataset that consists of 143 DApps (involving 437 contracts) from real-world security incidents. The experimental results demonstrate that state-aware feedback enhances the ability of Odyssey in state exploration by achieving 19% higher state coverage. Meanwhile, Odyssey totally finds 15 exploits of vulnerabilities from real-world attacks, showing its advantage in detecting real-world vulnerabilities.
Smart contracts underpin a wide range of decentralized applications—from financial services to supply-chain management—but their immutability and direct control of assets magnify the impact of any security bugs. Although many fuzz approaches have been proposed and have demonstrated their effectiveness in uncovering vulnerabilities, existing methods often rely on unguided random mutation scheduling, generate redundant inputs, and fail to adapt to smart contract-specific characteristics. To overcome these challenges, we present FuzzMaster, a feedback-driven fuzzing framework that combines deep reinforcement learning (DRL) with lightweight probabilistic scheduling to steer mutation selection at runtime intelligently. By continuously analyzing execution feedback—code coverage, function-call sequences, and vulnerability signals—FuzzMaster’s DRL agent and probabilistic tables prioritize high-impact mutations and avoid wasted effort on redundant seeds. On standard VeriSmart and SmartBugs benchmarks, FuzzMaster achieves a 66.2% detection rate with 100% precision (versus 46.9% for ItyFuzz and 43.1% for Confuzzius) and uncovers most bugs within the first second of execution. Meanwhile, in real-world Ethereum contracts, FuzzMaster identified 97 vulnerabilities in 6 categories. These results demonstrate that dynamic, vulnerability-aware mutation scheduling can dramatically improve both the efficiency and effectiveness of smart contract fuzz testing.
Smart contracts have achieved significant success, however, their security remains a long-standing challenge. The immutability and transparency of smart contracts require establishing a strong mechanism to prevent private leakage and trusted data tampering. Apart from traditional logic and code-level vulnerabilities arising from insufficient control over contract variables and function parameters, smart contracts may store private-dependent information in blockchain records, which is a critical type of vulnerability, but often overlooked in existing security analysis. In this paper, we present an automated approach for synthesizing security policies, named SmartIFSyn, to eliminate information flow vulnerabilities in smart contracts. We formalize the semantics of Solidity, the most widely used smart contract language, and analyze information flow security of Solidity smart contracts from two perspectives: local-variable security and global-interaction security. We present a type system to guide the elimination of local-variable vulnerabilities by inferring a policy and resort to constraint solving to synthesize a desired policy in case that the type system fails. The policy ensures both local-variable and global-interaction security while it is maximally aligned with user preference. Furthermore, the policy can be subsequently converted into enforceable specifications. We implement our approach in a tool and evaluate it on 17,160 real-world Ethereum smart contracts. The experimental results demonstrate the efficacy of our approach, e.g., detected 243 vulnerabilities in 223 real-world Ethereum smart contracts.
Abstract When resource-constrained Internet of Things (IoT) terminals connect to industrial control, sensing, and edge systems, it is necessary to balance low-overhead authentication, credential privacy protection, and cross-platform deployment. Traditional password and USBKEY authentication methods rely on static credentials and certificate mechanisms, which are vulnerable to eavesdropping, replay, and forgery attacks, while simultaneously suffering from privacy leakage and high platform adaptation costs. To address these issues, this paper proposes a hardware-assisted zero-knowledge authentication scheme for resource-constrained terminals. It utilizes a USBKEY as the local trusted hardware carrier and introduces a quadratic constrained zero-knowledge proof protocol under the Generalized Subspace Vector Oblivious Linear Evaluation framework (GSVOLE-2DLC) to construct a session-bound dynamic authentication process. In the registration phase, the scheme binds protocol parameters with user credentials and writes them into the USBKEY. In the authentication phase, the server (acting as the verifier \((\mathcal{V})\)) generates a random challenge, and the USBKEY (acting as the prover \((\mathcal{P})\)) generates temporary proof parameters based on local witness information. Subsequently, the verifier \((\mathcal{V})\) completes the verification through constraint consistency and GSVOLE consistency, thereby avoiding the transmission of original identity credentials over the network. To adapt to terminals with varying computational capabilities, this paper further designs configurable finite field parameters and cross-platform modular arithmetic interfaces, which are implemented in a PowerPC-architecture USBKEY prototype and a host-side verification environment. Experimental results demonstrate that under the parameter configuration of a 64-bit prime field, \((n_C=4)\), \((k_C=3)\), \((d_C=3)\), \((\ell=2)\), and \((t=7)\), the total system authentication time is approximately 0.5476 s, and the verification time for the verifier \((\mathcal{V})\) is 0.0031 s. Protocol performance and functional tests indicate that the proposed scheme can correctly execute identity authentication under the assumed threat model, making it suitable for IoT edge scenarios requiring privacy protection and lightweight authentication.
We present SmarTrim, a new symbolic execution technique for detecting vulnerabilities in smart contracts. Smart contracts require rigorous safety validation since flaws in them can cause significant financial loss. Numerous symbolic execution techniques, which generate vulnerable transaction sequences to trigger and help understand vulnerabilities, have been extensively studied to enhance the security and safety of smart contracts. However, their performance remains unsatisfactory due to the extremely large search space for transaction sequences. To mitigate this issue, SmarTrim introduces a novel technique that safely reduces the search space by detecting and pruning redundant transaction sequences. Experimental results show that SmarTrim greatly outperforms eleven state-of-the-art analyzers in detecting critical vulnerabilities in real-world smart contracts.
Certification of digital documents, such as academic credentials, seems a particularly suitable application for the use of blockchain and distributed ledger technologies. Indeed, these technologies enable decentralized certification systems that rely on the immutability and persistence of their distributed ledgers. However, in the absence of a central trusted authority, it is not easy to guarantee the authenticity of the connection between the real identity of an academic institution and the digital identity of the certificate issuer. In this paper, we demonstrate that one of such systems, known as Block.co, has a vulnerability that allows the production of forged certificates that are recognized as valid by the system. Since this is an inherent limitation of the approach used for blockchain-based certification, our attack is likely to be extendable to other systems adopting the same approach.
Open access
3 source records
cs.CR
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
The increasing demand for verifiable computation in privacy-sensitive distributed systems has driven the widespread adoption of Zero-Knowledge Proofs (ZKPs). However, the various kinds of current ZKP frameworks—which include zk-SNARKs, zk-STARKs, Bulletproofs, and folding-based systems—introduce complex trade-offs across proof size, prover cost, and trust assumptions, making system selection challenging in actual practice. This paper presents a systematic, application-oriented survey that connects ZKP design choices with real-world deployment constraints. It provides a comparative analysis of major constructions to evaluate their performance and security properties. Furthermore, these trade-offs are mapped to representative application scenarios, including Layer 1/Layer 2 blockchain scaling, Decentralized Identity (DID), and Verifiable Machine Learning (zkML), explaining how different systems are selected based on application-specific requirements. In addition, the paper discusses emerging paradigms such as hardware acceleration, binary field optimizations, and lookup-based zkVMs, which aim to address the prover bottleneck. Overall, this survey provides a structured understanding of the strengths and limitations of existing ZKP systems and offers insights for the design of scalable and privacy-preserving infrastructures.
Hüseyin Ahmet Cemil Özaslan, Şafak Durukan-Odabaşı
Smart contracts have become a fundamental component of blockchain ecosystems, and their reliability is strongly shaped by the programming languages in which they are written. While prior studies have classified vulnerabilities, fewer have quantitatively examined how language design and secure coding practices affect performance and resilience. To address this gap, this study empirically compares Solidity and Vyper under controlled conditions and complements these experiments with a literature-based evaluation of Rust and Move. Test scenarios included deployment, deposits, withdrawals, arithmetic overflow, reentrancy, and transaction origin misuse. For both vulnerable and secure variants in Solidity and Vyper, metrics such as gas consumption, deployment size, and runtime execution time were collected. The results indicate that deployment costs differ substantially between the two languages (Solidity ≈ 177 k gas vs Vyper ≈ 135 k gas, ~24% lower), whereas runtime performance is mixed: deposit calls are nearly identical (Δ ≈ 0.02 ms), whereas withdraw shows a noticeable gap (Δ ≈ 4.97 ms) favoring Vyper; nevertheless, these call-level differences remain small relative to the larger deployment-time gap. Importantly, secure coding practices such as explicit arithmetic checks and the Checks–Effects–Interactions pattern eliminate critical vulnerabilities while adding less than 1% to the overall execution cost. Although Rust and Move are considered through a literature-based review, they illustrate alternative approaches that embed security guarantees directly into the language. Based on these observations, this study proposes a measurable framework to understand how different smart contract programming languages vary in terms of security and efficiency, emphasizing the role of language design and secure coding practices in shaping contract development.
Decentralized Finance (DeFi) applications rely heavily on the order in which transactions are executed, making them susceptible to reordering attacks that enable adversaries to extract Blockchain Extractable Value (BEV). While linear blockchain systems such as Ethereum have inspired extensive research into fair ordering mechanisms, DAG-based consensus protocols have remained largely unprotected despite their growing adoption for scalability and performance. In this paper, we introduce Tilikum, a DAG-based ledger protocol that ensures fair transaction ordering without relying on weak edges. Tilikum achieves ordering linearizability by leveraging median-based timestamp aggregation, or batch order fairness, while maintaining low data redundancy and robust garbage collection. We implemented Tilikum in Rust and evaluated it against representative baselines, namely Narwhal/Tusk, Pompē, Themis and FairDAG. Our results show that Tilikum achieves up to $39\times$ higher throughput than other fair-ordering baselines, while fully blocking state-of-the-art DAG-specific reordering attacks.
We propose cryptographic certificates of validity for agentic AI systems. The core idea is to formally specify a correctness or policy condition as a logical predicate, compile this predicate to a witness-checking problem over polynomial constraints, and use a succinct cryptographic proof system (and optionally zero-knowledge) to certify that the condition holds. This offers a middle ground between formal verification of source code, and cryptographic authentication. An agent's action can be accompanied by an independently checkable proof that it satisfies an agreed formal policy, without requiring the verifier to trust the agent or to re-execute computation. We outline the approach at a high level, give the core mathematical translation, relate the proposal to proof-carrying code, zkVMs, formal methods, and agent governance, and note the specification, auditing, and deployment questions that a full implementation must answer.
Local AI inference for browser tasks—including vision-language processing, speech recognition, and neural translation—requires significant computational resources that may exceed the capabilities of low-power devices such as smartphones, tablets, and older laptops. This paper presents the design of a distributed GPU compute sharing system for the Kathon cryptographic browser that enables peer-to-peer AI inference acceleration across trusted devices using libp2p networking. The system partitions neural network inference workloads across participating peers using tensor parallelism, with encrypted communication channels, verifiable computation proofs, and incentive mechanisms based on the .aioss cryptographic ledger. We address key technical challenges: heterogeneous device discovery with capability advertisement, dynamic workload partitioning for variable peer availability, encrypted inference that prevents input reconstruction, and fault tolerance through redundant computation. Simulated benchmarks across a 16-peer testbed demonstrate 3.8x speedup for Whisper transcription and 4.2x speedup for Qwen 2.5 VL inference on low-power client devices. A security analysis confirms that encrypted inference provides semantic security against honest-but-curious peers. The system enables Kathon to deliver AI features on devices that lack the local compute capacity for real-time inference. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores browser engine, privacy in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
This paper examines the cryptographic foundations of audit ledgers, with particular focus on hash chains, transparency logs, and verifiable data structures as implemented in the 01s Sovereign (Kaiman) operating system's .aioss ledger format. We survey the evolution from simple hash-linked data structures to sophisticated transparency frameworks such as Certificate Transparency (CT), CONIKS, and Trillian, and demonstrate how these technologies converge in the 01s Sovereign OS to create an immutable, verifiable record of all system and AI-assisted decisions. Part of The Anticloud research corpus by Lois-Kleinner Alpasan (ORCID: 0009-0009-2233-6107). This work explores api gateway, ai routing in the context of sovereign AI infrastructure, post-cloud computing architectures, and transparent, blackbox-free systems.
Established threat modelling methodologies (STRIDE, PASTA, Trike, OCTAVE, LINDDUN, attack trees, and adversary-behaviour catalogues such as MITRE ATT&CK) were designed for software products and enterprises with a discernible security perimeter, a single owning organisation, and a clean separation between technical and operational risk. Modern organisations violate all three assumptions: they run on cloud and SaaS control planes they do not own, distribute privileged authority across founders, contractors, vendors, signers, committees, and automation, and expose value through human approval ceremonies and supply-chain edges rather than a network boundary. The dominant failures are authorised-but-malicious actors, collusion across nominally independent parties, control-plane and CI/CD compromise, and operational mishandling of high-value actions, which existing methods largely omit. We present TRACE, a methodology that treats threat actors, roles, assets, critical invariants, and trust/authority edges as first-class, evidence-linked objects spanning three layers: protocols, systems, and organisations. We compare nine widely used frameworks across ten dimensions, show where each falls short in distributed, cloud-first, zero-trust settings, and specify TRACE: its core model, three application pillars, sequential gated workflow, and an evidence-and-traceability discipline for human-AI co-working in which language models accelerate coverage while senior reviewers retain judgement over invariants, severity, and collusion. TRACE was developed through Web3 security practice but is stack-agnostic. We discuss its relationship to zero trust architecture and accountable Byzantine consensus, its limitations, and open questions around empirical validation.
This research investigates critical challenges in Transformer-based smart contract auditing systems, with a specific focus on inference instability and class imbalance in CodeBERT-based binary vulnerability classification. Layer-2 blockchain networks introduce highly complex architectures that increase the risk of smart contract exploits, where traditional static analysis tools such as Slither often produce large volumes of noisy, rule-based alerts. Recent advancements in pre-trained Transformer models, particularly CodeBERT, have demonstrated strong capabilities in semantic code understanding and vulnerability detection. However, during deployment of a fine-tuned CodeBERT-base model, we observe significant performance and stability issues. Initial inference experiments show a 100% false-positive rate, primarily attributed to severe class imbalance in the slither-audited-smart-contracts dataset and model sensitivity to specific smart contract patterns such as raw Ether transfer functions. In addition, system-level execution profiling reveals a silent segmentation fault during model initialization. Further investigation using Windows OS logs identifies dependency conflicts between PyTorch and PyArrow (via Hugging Face Datasets), particularly related to C++ DLL load-order issues. Experimental analysis demonstrates that modifying dependency import order, prioritizing PyArrow initialization, and enforcing strict model.eval() state management significantly improves inference stability. These findings highlight important architectural and deployment considerations for Transformer-based blockchain security systems and provide practical insights for improving the robustness of automated smart contract auditing pipelines in Layer-2 Web3 ecosystems.
This work presents a comprehensive study of entropy-based metrics for evaluating blockchain systems, focusing on on-chain ledger immutability, off-chain data integrity, and computational dynamics within blockchain virtual machines (BVMs). We develop a unified framework that models blockchain states as probabilistic distributions, quantifying uncertainty through Shannon entropy and examining its evolution under varying adversarial fractions. Extensive simulations demonstrate that on-chain entropy exhibits near-exponential decay, reflecting the cumulative reinforcement of honest consensus, while off-chain entropy remains static, highlighting the limitations of conventional data storage. Furthermore, the BVM is analyzed in terms of computation entropy, establishing its Turing completeness and demonstrating that smart-contract state evolution mirrors the information dynamics of arbitrary Turing machines. Our results provide quantitative evidence that entropy serves as both a theoretical and operational measure of immutability, tamper evidence, and protocol resilience. The proposed entropy framework offers practical tools for monitoring ledger integrity, detecting tampering, and assessing computational complexity, bridging the gap between information-theoretic principles and distributed ledger applications. This study advances both the theoretical understanding and practical evaluation of blockchain security, providing a principled methodology for analyzing distributed systems under adversarial conditions.
We extend the Lindblad Cryptography Protocol (LCP) — previously applied to consensus and decentralized finance — to the problem of verifying real-world data on-chain. Existing oracle protocols solve the immutability of records on-chain but inherit a structural weakness at the data ingestion layer: the data still originates in software, run by a trusted operator, and can be fabricated at the source before being recorded. We show that hardware with silicon-derived unforgeable identity (SRAM PUF + BCH fuzzy extractor) can sign measurements directly, producing attestations that are cryptographically verifiable by any third party without trust in the operator. We demonstrate end-to-end validation on mainnet using a live commodity price (West Texas Intermediate crude oil) sourced from the U.S. Energy Information Administration, signed by a physical node, and verified by a publicly accessible mathematical check. We further describe the generalization of this primitive across five application verticals: agriculture, energy, mining and resource extraction, Real-World Asset (RWA) tokenization, and verified ad delivery. The Lindblad Oracle complements existing oracle protocols (Chainlink, API3, UMA) by providing a hardware-anchored root of trust at the data-origination layer, beneath their data-distribution layer.
This manuscript develops Dormant Continuity Theory (DCT), a protocol-relative mathematical framework for reasoning about systems that remain inactive at their protected core while retaining auditable continuity, recovery, diagnostic, and handoff capabilities. The theory formalizes dormant processes using finite transition systems, typed certificates, observable histories, evidence algebra, guarded authorization, replayable resolution, extraction adequacy, and fail-closed classification.DCT addresses practical challenges in long-lived distributed systems, including forked ledger histories, bounded model checking, data availability, zero-knowledge proof soundness boundaries, watcher incentives, MEV-resistant reward mechanisms, resource conservation, guardian corruption, maintenance transitions, and certificate-level HTLC handoff to extinction-style OSCT semantics. The framework distinguishes safety, bounded-griefing, diagnostic routing, and liveness assumptions, avoiding unconditional trustless claims while providing a rigorous finite core for verification and implementation-oriented extensions.
PYCO is the native token of the Lindblad Protocol, emerging as a direct consequence of a network that measures and rewards physical coherence. This paper describes the mechanism by which PYCO is generated, distributed, and consumed within the Spectral Ledger, and establishes the economic properties that result from anchoring token issuance in physical hardware validation. Every PYCO in existence was produced by a physical node running the Lindblad Cryptography Protocol (LCP) stack on real hardware. As of June 2026, over 1,512,000 PYCO have been mined across 35,842+ epochs by physical hardware nodes deployed on mainnet on Arbitrum One.
Traditional distributed consensus mechanisms rely on probabilistic assumptions, economic weighting (Proof-of-Stake), or arbitrary computational work (Proof-of-Work) to secure ledger state transitions. These models leave the application layer inherently vulnerable to Man-in-the-Middle (MITM) attacks, Maximal Extractable Value (MEV) extraction, and semantic exploits against critical infrastructure (SCADA/PLC). This manuscript introduces Proof-of-Rigidity (PoR), a deterministic state-validation framework that locks the consensus machine within a continuous 150-decimal-place geometric manifold ($G_{24}$ volume space). The paper formalizes three core components: The Brittle Acceptance Predicate: A Coq-verified mathematical boundary that enforces an absolute $10^{-80}$ validation tolerance, structurally denying unauthorized state mutations. Mantissa Tail Parity (The MEV Sieve): A mechanism utilizing Canonical Decimal Arithmetic ($\mathbb{D}_{150}$) to mathematically neutralize routing interception and front-running. Capability-Constrained Semantic Policies: A bipartite matrix that structurally subordinates LLM-based ontological analysis to strict cryptographic Role-Based Access Control (RBAC), preventing adversarial paraphrasing against industrial endpoints. By enforcing strict geometric determinism, PoR transforms network security from probabilistic difficulty into mathematical brittleness. Included in this deposit are the Coq formal verification proofs, a Python reference implementation of the Layer-1 substrate, and a computational benchmarking harness demonstrating throughput scalability. LEGAL, ETHICAL, AND SAFE HARBOR DISCLAIMER The mathematical models, formal Coq proofs, and Python reference implementations contained within this deposit are published strictly for academic research, cryptographic peer review, and educational purposes. The architectures described herein represent a theoretical substrate and an experimental prototype. They have not undergone formal, independent security auditing for production deployment. No Warranty (As-Is): The mathematical models and reference code are provided "AS IS", without warranty of any kind, express or implied. The continuous geometric bounds and mechanisms detailed herein are theoretical thresholds; physical hardware limitations, truncation errors, or implementation flaws may affect real-world execution. Limitation of Liability: Under no circumstances shall the author, contributors, or affiliated research entities be held liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, loss of use, data, stablecoin assets, or profits; business interruption; or industrial infrastructure failure) arising in any way out of the use, deployment, or misconfiguration of this protocol. Assumption of Risk: Any entity choosing to implement the $G_{24}$ volume space boundaries, the Topological Shatter mechanics, or any variant of the PoR consensus layer within a live environment does so entirely at their own risk, and is solely responsible for ensuring compliance with all applicable cybersecurity and financial regulations.