Blockchain Papers

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Adam Zahir, Vincent Lefebvre, Mark Angoustures, Milan Groshev · 5 authors

Multi-agent systems (MAS) comprise autonomous software agents that collaborate to perform complex tasks in critical cyber-physical domains, including multi-robot coordination and the Industrial Internet of Things (IIoT). In such distributed environments, a compromised agent may execute modified software while appearing trustworthy, causing other agents to act on false information and corrupting the mission. Agents must therefore establish and maintain mutual trust throughout operation. Remote attestation (RA) is a well-established technique for this purpose, enabling a remote verifier to assess the integrity of a potentially compromised prover device. However, conventional RA approaches face significant limitations in MAS: integrity guarantees are restricted to boot or application-load time, designs rely on centralized trusted verifiers or security hardware, and attestation records lack transparency and auditability. To address these limitations, this paper presents D-MUTRA, a blockchain-based framework that introduces a mutual RA protocol in which agents measure their runtime integrity while verifying that of their peers, acting as both prover and verifier. The framework operates entirely in software and relies on two components: a Security-as-a-Service that instruments agents with lightweight measurement and verification capabilities, and a smart contract that coordinates the attestation protocol in a decentralized and transparent manner. We implement a proof-of-concept on a private Ethereum blockchain using Hyperledger Besu and evaluate it in a swarm robotics scenario built with Robot Operating System (ROS) and the Gazebo simulator. Results show that D-MUTRA enables agents to continuously attest one another, detects malicious software modifications, and scales to large deployments with negligible overhead on protected applications.

Hongru He, Akihiro Fujihara

High-performance Byzantine Fault Tolerant (BFT) blockchains are designed to achieve high throughput and low latency, yet their observed block time distributions often reveal complex behaviors arising from networking, pipelining, and deployment heterogeneity. In this paper, we diagnose HotStuff-based high-performance BFT consensus by modeling block times through a quorum-based multicast framework that links each block interval to quorum formation latency. We capture multimodal block time distributions using mixture models, where each component represents a distinct network condition characterized by effective transfer rate of block information. The proposed model is fitted to the bulk of mainnet block time data, while tail decay is analyzed separately to assess asymptotic behavior. Applying this methodology to Hyperliquid and Aptos mainnets, we find that Hyperliquid is well explained by a unimodal distribution, consistent with a relatively homogeneous validator deployment. In contrast, Aptos exhibits persistent multimodal structure and a pronounced shift following a consensus upgrade, reflecting heterogeneous deployments and diverse communication paths. These results demonstrate that mixture modeling of block time provides a practical and informative diagnostic tool for analyzing and monitoring high-performance BFT consensus.

Arthur Chagas, Pedro Bento, Yan Aquino, Arthur Buzelin · 6 authors

Volatility forecasts are commonly evaluated with aggregate accuracy metrics such as RMSE and MAE, but these metrics can hide conditional failures that matter for risk management. This paper proposes a model-agnostic audit framework for evaluating whether volatility forecasts remain reliable across latent market regimes. We learn time-series representations of market-state windows, cluster them into regimes using only training information, assign regimes out of sample, and compare aggregate forecast behavior with regime-conditional bias, tail-underprediction, and underprediction-sensitive economic losses. Applied to daily volatility forecasting across cryptocurrency and ETF assets, the audit shows that models with competitive aggregate accuracy can still exhibit substantial regime-specific bias and severe tail underprediction. The results suggest that volatility forecasting should be evaluated not only by average error, but also by where and how forecasts become unreliable. Our framework shifts forecast evaluation from asking which model is most accurate on average to identifying the market regimes in which apparently accurate forecasts fail conditionally. Reproducibility: https://github.com/arthurchagas1/Latent-Regime-Bias-Auditing-for-Volatility-Forecasting

Sengbandith Khinnithuck, Fang Huang, Sengbandith Phonethana

Centralized governance frameworks in developing economies frequently generate a fundamental tension between national policy coherence and the operational responsiveness required by diverse local labor markets. In Laos, this tension is acutely manifest within the administration of Vocational Education (VE), where provincial and district authorities bear expanding implementation responsibilities without commensurate transfers of fiscal authority, administrative capacity, or technical expertise. This study critically examines the administration models operationalized within local VE governance in Laos, identifies the structural constraints limiting their effectiveness, and proposes an evidence-grounded five-component governance reform framework. Employing a sequential explanatory mixed-methods design, the study integrates quantitative survey data from 781 stakeholders across all provinces, comprising local government officials, VE administrators, educators, and industry representatives, with semi-structured qualitative interviews and systematic policy document analysis. Quantitative findings reveal that administrative model typology (centralized, decentralized, hybrid) accounts for less than 2% of the variance in perceived governance effectiveness, whereas process-level governance qualities, institutional autonomy, stakeholder participation, decentralization depth, and resource adequacy jointly account for 56% of the variance in perceived suitability. Public-Private Partnership (PPP) strength emerges as the strongest predictor of institutional effectiveness. Qualitative findings illuminate the mechanisms underlying these patterns: local authorities function as pragmatic brokers, navigating structural rigidities through informal networks and discretionary project-based initiatives that remain institutionally unsustainable and inequitably distributed. The study identifies a centralization-responsiveness paradox wherein ceremonially adopted decentralization policies coexist with functionally centralized operations, generating accountability ambiguities and inequitable governance costs. Grounded analytically in institutional theory, decentralization theory, and human capital theory, the proposed framework integrates reconstituted multi-level governance architecture, institutionalized demand-side engagement, equitable financing, systematic capacity development, and rigorous performance accountability. Implications are discussed for policymakers, development partners, and VE administrators in comparable transitional economies.

Richard Yegian

When we consider the PoW (proof-of-work) in the Bitcoin blockchain, how is the work calculated? How does this work convert to energy quantities? This paper demonstrates that in the Bitcoin blockchain, "Proof-of-Work" (PoW) is not a complex calculus equation, but rather a probabilistic brute-force search. Miners repeatedly run block header data through a cryptographic hash function, tweaking variables until they output a number that meets a strict network threshold. In the Bitcoin blockchain, Proof-of-Work (PoW) is a probabilistic brute-force search where miners repeatedly run block headers through a double SHA-256 hash function to find an output below a global target threshold. The mathematical "work" is quantified by the network Difficulty (D), requiring roughly D × 2³² expected hashes per block. To convert this cryptographic effort into physical energy, the global network hashrate is first derived by dividing total block hashes by Bitcoin’s 10-minute target block time (600 seconds). This computational rate is then bridged to the physical world using hardware efficiency—measured in Joules per Terahash (J/TH)—multiplied by operational time. Because modern semiconductor ASICs operate roughly seven orders of magnitude above the absolute thermodynamic limits outlined by Landauer's principle, nearly all electricity consumed by this cryptographic pipeline directly converts into waste heat. The calculation of this work, how it translates mathematically to network metrics, and how those metrics convert into physical energy quantities is the discussion of this paper.<b>Part 1: How the "Work" is Calculated</b><b>1. The Hashing Puzzle (Double SHA-256)</b>A miner constructs a block header containing transaction data, a timestamp, the hash of the previous block, and a changing variable called a nonce. They pass this header through the SHA-256 algorithm twice:<br>H(x) = SHA-256(SHA-256(Block Header))The resulting output is a 256-bit unsigned integer, typically represented as a 64-character hexadecimal string.<b>2. The Target (</b><b>T</b><b>)</b>The network enforces a global threshold called the Target (T). For a block to be accepted, the hash output interpreted as a massive 256-bit integer must satisfy:<br>Hash Output ≤ T<br>Because the output of a cryptographic hash function is completely random and uniformly distributed, miners cannot predict the output. Finding a valid hash is essentially a Bernoulli trial (like rolling a die with an astronomical number of sides).<b>3. Mathematical Definition of Difficulty (D)</b>Because the Target T is a massive 256-bit number that changes every 2,016 blocks, Bitcoin uses a human-readable metric called Difficulty (D), scaled relative to a baseline "genesis" target (T_max).<br>T_max = 0x00000000FFFF0000000000000000000000000000000000000000000000000000The difficulty formula is D = T_max/TAs the network gains more miners, T drops (becomes smaller), making hashes harder to find, which increases D.<br>The expected number of hashes E[hashes] required to find a valid block at a given difficulty is proportional to D:E[hashes] = D × 2³² × T/T_max (scaled to baseline expectations)<br>More simply, the total expected hashes per block is roughly:Expected Hashes ≈ D × 4.295 × 10⁹<b>Part 2: From Computational Work to Energy Quantities</b>Energy consumption is a byproduct of hardware efficiency operating over a span of time to execute these hash attempts. There is no direct algorithmic conversion from a hash to Joules in the protocol code; instead, the conversion bridges cryptographic operations and thermodynamic hardware efficiency.<b>Step 1: Calculate Total Network Hashrate (H</b>_{<strong>net</strong>}<b>)</b>The global hashrate represents the total number of hashes computed per second across all active machines globally. It is derived directly from the current difficulty (D) and Bitcoin's target block time (t = 600 seconds or 10 minutes):<br>Hashes per block = D × 2³²<br>Network Hashrate (H_net) = D × 2³²/600 [hashes/second or H/s]<b>Step 2: Factor in Hardware Efficiency (EF)</b>ASIC (Application-Specific Integrated Circuit) miners dominate Bitcoin mining. Their electrical efficiency is measured in Joules per Terahash (J/TH) or Watts per Gigashash. Let the aggregate hardware efficiency of the network be denoted as EF (expressed in Joules per Hash, J/H):EF = Total Power Consumption (Watts)/Hashrate (H/s)<b>Step 3: Energy Derivation Formula</b>To calculate the total energy consumed by the entire Bitcoin network over a specific timeframe (e.g., 1 second, 1 day, or 1 year), we multiply the network hashrate by the hardware efficiency and time (t):<br>Energy (E) = H_net × EF × Δ tSubstituting H_net into the equation:<br>E = (D · 2³²/600) × EF × Δ t<br>For example, assume a network difficulty (D) of roughly 80 × 10¹² (80 trillion). Also, assume an average fleet hardware efficiency (EF) of 25 Joules per Terahash (25 × 10⁻¹² J/H). Calculate energy consumed over 1 day (Δ t = 86,400 seconds):Hashes/sec = 80 × 10¹² × 4,294,967,296/600 ≈ 5.72 × 10²⁰ H/sPower (Watts) = (5.72 × 10²⁰ H/s) × (2.5 × 10⁻¹¹ J/H) ≈ 14,300,000,000 W = 14.3 GWEnergy over 1 day = 14.3 GW × 24 hours ≈ 343.2 GWhThe summary of the conversion pipeline may be expressed as<br>Target (T) ⟶ Difficulty (D) ⟶ Network Hashrate (H_net) ⟶× Hardware Efficiency (J/H)⟶ Power (Watts) ⟶× Time⟶ Energy (Joules/kWh)<b>Part 3: Thermodynamic Limits and Efficiency Bounds (Landauer's Principle)</b>To fully connect cryptographic work to physical energy, we can look at the theoretical minimum energy required by the laws of physics to perform computation.<b>1. Landauer's Principle</b>Landauer's principle establishes the minimum possible amount of energy required to erase or irreversibly manipulate a bit of information at a given temperature (T_temp):<br>E_min = k_<em>B</em> T_temp ln(2)k_<em>B</em> is the Boltzmann constant (1.380649 × 10⁻²³ J/K).T_temp is the absolute temperature of the environment (e.g., 300 K).For a single bit modification at room temperature, this absolute thermodynamic floor is roughly 2.8 × 10⁻²¹ Joules per bit.<b>2. Comparing SHA-256 to the Thermodynamic Limit</b>A single SHA-256 calculation involves processing a 512-bit message block through 64 rounds of complex logical operations (bitwise additions, rotations, and shifts), manipulating hundreds of thousands of bits cumulatively.Theoretical minimum energy per hash: Factoring in the sheer number of bit operations inside SHA-256, even a reversibly ideal computer would require thousands of bit manipulations, putting a strict physical floor on a single hash well above Landauer's limit (roughly on the order of 10⁻¹⁹ to 10⁻¹⁸ Joules per hash under optimal theoretical conditions).Actual ASIC efficiency: Modern state-of-the-art ASIC miners (like the Bitmain Antminer S21 series) operate around 15 to 20 J/TH (1.5 × 10⁻¹¹ Joules per hash).Comparing real-world hardware (10⁻¹¹ J/H) to absolute physical limits (10⁻¹⁸ J/H) reveals that current silicon-based semiconductor technology is roughly 7 orders of magnitude away from theoretical thermodynamic efficiency—meaning nearly all energy put into Bitcoin mining converts directly into waste heat.<b>Part 4: Complete Comprehensive Master Equation</b>Combining all components into a single macro-equation, the total daily electrical energy (E_day) consumed by the global Bitcoin network can be calculated directly from the network's current Difficulty (D) and the average hardware efficiency fleet-wide (EF_avg in J/TH):E_day = (D · 2³²/600) × (EF_avg × 10⁻¹²) × 86,400<br>Where:<br>D · 2³² / 600 yields the Network Hashrate (hashes/sec).EF_avg × 10⁻¹² scales Joules-per-Terahash down to Joules-per-Hash.86,400 converts seconds into one full day.This mathematical coupling ensures that as network security (Difficulty D) scales up over time to attract more capital and hashpower, energy consumption scales linearly with it, modulated only by the parallel improvement rate of semiconductor manufacturing efficiency (EF_avg).To recap the end-to-end framework:The Work: Quantified by the difficulty D and scaled via 2³² to determine total expected hashes per block.The Hashrate: Derived by dividing total hashes per block by the target 10-minute block time (600 seconds).The Energy Conversion: Bridged physically using the hardware's efficiency metric (Joules per Terahash, or J/TH) multiplied over time.The Physical Bound: Bounded by thermodynamic limits like Landauer's principle, explaining why modern ASICs produce the massive amounts of waste heat characteristic of the Bitcoin network.

Yoshiki Ueoka, Nagi, Akari, Sui

Let $\xi(s)=\frac12s(s-1)\pi^{-s/2}\Gamma(s/2)\zeta(s),\qquad$ $F(x)=\frac{\xi'}{\xi}\!\left(\frac1{1-x}\right)=\sum_{m\ge0}f_mx^m,$ and define the symmetric Toeplitz--Hankel coefficients $g_{ij}=f_{|i-j|}-f_{i+j+1}+\delta_{ij}f_0.$ The adjacent matrices $M_n=\begin{pmatrix}g_{nn}&g_{n,n+1}\\g_{n,n+1}&g_{n+1,n+1}\end{pmatrix}$ form the local family in a previously established criterion equivalent to the Riemann hypothesis. We prove an unconditional finite-range positivity theorem for this family. The zero-pair moment representation of $g_{ij}$ expresses $M_n$ as a sum of rank-one polynomial atoms. A finite verification of the Riemann hypothesis up to height $H$ then splits this sum into a positive-semidefinite verified part and an unrestricted high-zero tail. Two rigorously isolated low zeros provide a positive core; every zero above $H$ is controlled in operator norm by an explicit zero-counting estimate. For the polynomial recurrence $B_0(t)=1,\quad B_1(t)=3-t,\quad B_{m+1}(t)=(2-t)B_m(t)-B_{m-1}(t),$ we derive the exact oscillatory form $B_m(4\sin^2\alpha)=\frac{\sin((2m+1)\alpha)}{\sin\alpha}$ and a closed formula for its two-point Christoffel--Darboux kernel. Using the Platt--Trudgian verification height $H=3{,}000{,}175{,}332{,}800$, the first two LMFDB/Platt zero intervals, and directed-rounding MPFR arithmetic, we certify the kernel away from zero at every integer level $0\le n\le99{,}999$. The resulting positive-core lower bound exceeds the adversarial high-zero tail bound by more than ten orders of magnitude. Consequently $M_n\succ0\qquad(0\le n\le99{,}999),$ or equivalently the first $100{,}000$ adjacent Toeplitz--Hankel determinants are strictly positive. No assumption is made about zeros above the verified height. To the best of our knowledge, this is the first finite-height transfer theorem for this adjacent Toeplitz--Hankel family and the first rigorous certification of its initial $100{,}000$ strict inequalities. This is not a proof of the Riemann hypothesis.

Akira Toyohara, Ye Tao, Siqiong Yao

No quasiperfect number ($σ(n) = 2n + 1$) is known, and its number of distinct prime factors is bounded below; the bound $ω\ge 7$ of Hagis--Cohen has stood since 1982, obstructed by a family of ``deep leaves'' on which pure enumeration cannot terminate (the scan bound for the intermediate prime reaches $8 \times 10^8$, and the exponent dimension is unbounded). This paper clears that obstruction with three lemmas at the level of secondary-school algebra --- a discriminant criterion, a quadratic-residue sieve, and a multilinear resolver --- which eliminate the last prime $q$, the intermediate prime $p$, and the exponent dimension respectively, turning a non-terminating search into a finite decision. On this basis all 381 stems of ``$3 \mid n$ and $ω= 7$'' and their $79{,}751{,}212$ deep leaves are eliminated, with the ledger closing exactly and zero solutions throughout; the complementary case ``$3 \nmid n$ and $ω= 7$'' collapses to a single stem, which is eliminated directly, so that the proof does not rest on any theorem whose published record we could not independently re-verify. Together with the machine elimination of $ω\le 6$ (Theorem B4), this yields the main theorem: \emph{any quasiperfect number, if one exists, satisfies $ω(n) \ge 8$} --- the first advance of this bound since Hagis--Cohen 1982. The full computation has been reproduced by seven separately closed ledgers across three algorithmic architectures (CPU and GPU), all with zero solutions and exact ledger closure, and the lemma layer is formalized in Lean (259 theorems, zero \texttt{sorry}). A 2023 preprint of Zemann reported the same bound by a different computation; our audit of its public code found a coverage gap of 35 feasible exponents, so the elimination given here is, to our knowledge, the first complete proof. Code, ledgers, and Lean sources are available from the authors.

Shi-Mei Ma

A famous result in the theory of combinatorial polynomials is the real-rootedness of the type $D$ Eulerian polynomial $D_n(x)$, which was originally conjectured by Brenti in 1994. By constructing a set of compatible polynomials over $s$-inversion sequences, Savage and Visontai proved this conjecture in 2013. Using matrices preserving interlacing properties of nonnegative polynomial sequences, Bränden also established the real-rootedness of $D_n(x)$. Combining Hermite-Biehler theorem and a result of Borcea and Brändén on Hurwitz stability, Yang and Zhang gave another proof of the real-rootedness of $D_n(x)$. By constructing half Eulerian polynomials of type $D$, Hyatt reproved Brenti's conjecture. As originally suggested by Brenti in 1994, it is possible that the real-rootedness of $D_n(x)$ may be established by using a more precise knowledge of the location of zeros of the types $A$ and $B$ Eulerian polynomials. In this paper, we add more details to the first proof of the real-rootedness of $D_n(x)$ that was provided by the author in 2012, which yields the half interlacing property among the types $A,B$ and $D$ Eulerian polynomials.

Vanesa Daza, Álvaro Montes, Carla Ràfols

Function-hiding functional commitment schemes allow one party to commit to a private function f and later prove f(x)=y for public x and y without revealing additional information about the function. We construct efficient function-hiding functional commitment schemes for arithmetic circuits of bounded size that achieve proof sizes below 1.6 kB—over an order of magnitude smaller than previous constructions—while simultaneously reducing proving and verification times. We achieve these results by introducing a novel information-theoretic interactive proof system called Polynomial Interactive Oracle Proofs with Randomized Indexer (rPHPs). By compiling rPHPs with commit-and-prove zkSNARKs, we are able to leverage relaxed zero-knowledge notions for our building blocks. This approach eliminates the overhead of strict privacy requirements of prior work, directly translating into improved efficiency in both communication and computation.

Ian Staley

No abstract is available for this record.

Majid Altuwairiqi

No abstract is available for this record.

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Confirmations of the ZBCN Report, and promises from a relatively new centralized private company trying to justify its existence in decentralized finance.

Aurore Guillevic, Simon Masson

In 2021, Masson, Sanso, and Zhang introduced the Bandersnatch curve associated to the BLS12-381 pairing-friendly curve, an elliptic curve designed for zero-knowledge proofs requiring circuits with a curve arithmetic. This type of curve is useful for privacy-preserving protocols, and more generally for succinct validity proof using pairing-based SNARKs. An embedded curve is defined over a field whose order is the group order of its associated curve. In this way, the pairing-friendly curve is used to express a zero-knowledge proof (such as a SNARK) of a statement taking place on the embedded curve. Contrary to the previous embedded curves (such as CØCØ, JubJub), Bandersnatch was built with the complex multiplication (CM) method, in order to ensure a very small discriminant (-8, whose magnitude is small), and thus efficient scalar multiplication thanks to the GLV technique. The algorithm provided by Masson, Sanso, and Zhang for searching this type of curves requires computation of Hilbert class polynomials, making the search of curve slow. It was not known whether Bandersnatch was an exceptional curve or whether comparable curves exist, of larger discriminants. This paper highlights the technicalities of the CM method already in use in the 90s to generate curve parameters of chosen order. This old technique allows revisiting the curve search of Bandersnatch, providing a dramatic speed-up improvement. This paper presents two algorithms: one to generate embedded elliptic curves of SNARK-friendly elliptic curves, with a variable discriminant; a second to generate families (parameterized by polynomials) with a fixed discriminant. When the (negative) discriminant is -3 modulo 4, it is possible to obtain a prime-order curve, and form a cycle. To illustrate this, we apply the technique first to generate more embedded curves like Bandersnatch with BLS12-381, such as a curve of discriminant -6673027, defining a plain twist-secure cycle. We also comment on the scarcity of Bandersnatch-like CM curves, and recall that with this generic algorithm, it is only a question of core-hours to find them. Second, we show the link between a paper of Ben Smith in 2015 and the work of Dai, Lin, Zhao, and Zhou in 2023, obtaining prime-order parameterized families of embedded curves of fixed discriminant, such as -3 for BLS and KSS18 curves. With KSS16 curves, the discriminant -4 is also possible (the curve has an even order). The technique can work with any KSS, Scott–Guillevic, Gasnier–Guillevic, or other fixed-discriminant parameterized family of pairing-friendly curves. This paper provides a more general point of view on embedded curves such as Bandersnatch, putting into perspective the works of Masson, Sanso, and Zhang, and Sanso and El Housni. The Python/SageMath scripts are available at https://gitlab.inria.fr/zk-curves/cm-embedded-curves/.

Sayed Mohammad Badiezadegan

Cryptographic protocols are evaluated not only by the security properties they achieve, but also by the resources required to execute them. Unlike conventional algorithm analysis, where a single running-time function may be sufficient, protocol analysis usually separates computational complexity, bit complexity, communication complexity, storage complexity, and round complexity. This article develops a systematic methodology for such analysis through three representative case studies: the Schnorr zero-knowledge proof of knowledge, a Diffie–Hellman-based one-out-of-two oblivious-transfer protocol, and Regev-style public-key encryption based on the Learning With Errors problem. For each construction, the protocol is stated formally, correctness is derived, and the dominant computational, communication, and memory costs are calculated step by step. The examples illustrate three qualitatively different bottlenecks: group exponentiation in discrete-logarithm protocols, amortized public-key cost in oblivious transfer, and dense matrix–vector arithmetic in lattice-based cryptography.

Phunchai Charatcharoenwitthaya, Tawesak Tanwandee, Pisit Tangkijvanich, Teerha Piratvisuth · 7 authors

No abstract is available for this record.

Shahla Atapoor, Cyprien Delpech de Saint Guilhem, Al Kindi

This work describes a digital signature scheme constructed from a zero-knowledge proof of knowledge of a pre-image of the Rescue Prime Optimized (RPO) permutation. The proof of knowledge is instantiated using the DEEP-ALI interactive oracle proof and made non-interactive via the Ben-Sasson–Chiesa–Spooner (BCS) transformation in the random oracle model. The resulting construction yields a signature scheme with transparent setup. Our design is motivated by recursive zero-knowledge applications, in which signature verification must itself be efficiently provable inside larger proof systems. To this end, the choice of the RPO permutation, the use of a simple algebraic intermediate representation (AIR), and working over the Goldilocks field are made with the goal of enabling efficient recursive verification and aggregation. The implementation of the scheme computes signatures in 4.6–7.2 ms and verifies them in 0.46–0.52 ms when the BCS transform is implemented with Blake3. When the BCS transform is instead instantiated with the RPO permutation itself, the configuration required when signature verification is to be proven recursively inside a proof system, signing takes 20.9–30.4 ms with Metal acceleration and 59.2–229.2 ms on CPU, while verification takes 5.09–5.79 ms. We validate the recursion-friendliness claim end to end by proving one signature verification inside the Miden zkVM and reporting the recursive prover time and proof size. These speeds are obtained with parameters achieving 113 or 122 bits of average-case security, depending on the chosen preset, against adversaries that can obtain up to <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mn>2</mml:mn> <mml:mrow> <mml:mn>64</mml:mn> </mml:mrow> </mml:msup> </mml:mrow> </mml:math> signatures.

Yoshiki Ueoka, Nagi, Akari, Sui

Let $\xi(s)=\frac12s(s-1)\pi^{-s/2}\Gamma(s/2)\zeta(s),\qquad$ $\frac{\xi'}{\xi}\!\left(\frac1{1-x}\right)=\sum_{m\ge0}f_mx^m,$ and define $g_{ij}=f_{|i-j|}-f_{i+j+1}+\delta_{ij}f_0,\qquad$ $M_n=\begin{pmatrix}g_{nn}&g_{n,n+1}\\g_{n,n+1}&g_{n+1,n+1}\end{pmatrix}.$ The condition $M_n\succeq0$ for every $n\ge0$ is a previously established criterion equivalent to the Riemann hypothesis. We prove an unconditional finite-range extension for this family without scanning individual zero ordinates or individual Christoffel--Darboux values. Writing $q=n+1$, a critical-line zero $\frac12+i\gamma$ contributes a rank-one atom generated by a two-dimensional polynomial vector. In diagonal and anti-diagonal coordinates its exact phase is controlled by $x=2q\arctan\frac1{2\gamma}.$ We use two level-dependent ordinate windows $(q,5q/4],\qquad (2q/5,q/2],$ whose phase slopes have opposite signs. Every cross-window pair has wedge at least $c_*/q^3$, while an explicit zero-counting estimate supplies at least $q\log q/100$ zeros in each window. The resulting moving Gram core satisfies $\lambda_{\min}(A_{q-1}^{\rm mov}) \ge \frac{c_*^2}{10400}\frac{\log q}{q^3}.$ Combining this with the Platt--Trudgian verification height $H=3{,}000{,}175{,}332{,}800$ and an unrestricted high-zero tail estimate gives $M_n\succ0\qquad(0\le n\le2{,}030{,}956).$ Thus the first $2{,}030{,}957$ local inequalities are proved unconditionally. We also show that every fixed finite zero core has smallest eigenvalue with liminf zero, explaining why level adaptation is structurally necessary for this method. To the best of our knowledge, the moving-window frame transfer and this finite-range theorem are new. The result is not a proof of the Riemann hypothesis.

Declan Murphy

Zero-knowledge proofs enable a prover to convince a verifier that a statement is true, without revealing the underlying witness data. This primitive naturally lends itself to privacypreserving systems, where hiding the witness prevents the verifier from learning sensitive information. That said, zero-knowledge proofs can also be used in systems where the witness is not necessarily confidential but is not readily available to the verifier. One such use case is image provenance, where signed images are transformed before being distributed. Since the original image is not available to the user, the digital signature cannot be verified without a zero-knowledge proof. In this use case, zeroknowledge proofs enable verification of the authenticity of the image’s source, the integrity of the image contents, and that only permitted transformations were applied. In this work we present an end-to-end prototype system that implements this provenance framework and several optimizations. One of our key optimizations is a packing scheme for reducing the number of Poseidon sponge absorb and permutation operations by ≈31×. We also show that this packing scheme reduces the median prover runtime by ≈40× and the median verifier runtime by ≈22×. We also introduce a chain of trust that removes digital signature verification from the circuit. Finally, we introduce custom PNG chunks that embed the required information in the captured images.

Justin Malonson

Autonomous artificial intelligence agents increasingly act across decentralized systems, yet existing authorization models provide limited mechanisms for constraining delegated authority, proving policy compliance, and assigning accountability for machine-initiated actions. This paper presents a policy-enforced identity and accountability framework for cryptographic governance of autonomous AI agents. The proposed architecture binds each agent to a verifiable decentralized identity, machine-readable authorization policies, delegated capability constraints, and tamper-evident action records. Before an action is executed, the framework evaluates identity validity, policy scope, contextual conditions, delegation depth, expiration, and revocation status. Approved actions generate cryptographically verifiable receipts that link the agent, authorizing principal, applicable policy, execution context, and resulting state transition without requiring disclosure of unnecessary sensitive information. The framework also supports attenuated delegation, enabling subordinate agents to receive narrower permissions than their parent agents while preventing privilege amplification. A formal threat model evaluates impersonation, policy substitution, replay attacks, unauthorized delegation, audit-log manipulation, and compromised agent behavior. Security analysis indicates that the architecture strengthens provenance, non-repudiation, least-privilege enforcement, and post-execution auditability across heterogeneous decentralized environments. The proposed approach provides a foundation for governing autonomous agents in blockchain networks, distributed applications, machine-to-machine systems, and multi-agent infrastructures where conventional access control is insufficient. It shifts AI governance from trust-based supervision toward verifiable, policy-bound, and cryptographically accountable execution.

Saskia Hufnagel, Colin King, Alina-Theresa Schnedl, Milind Tiwari

Non-fungible tokens (NFTs) bring many opportunities for artists, investors, and creators, but they also have a dark side with significant potential for use in financial crimes. Drawing on relevant caselaw, a systematic review and topic modeling of literature, we map common examples of NFT-related crime, including fraud, money laundering, theft, and market-related offenses. This empirical review lays the groundwork for the core contribution of this article, that is, application of the “crime triangle” to NFT-related crime. Recognizing heterogeneity in NFT-related crime, we detail five scenarios where such crime can occur and analyze these in the context of the crime triangle (inner and outer). This enables us to identify potential gaps and vulnerabilities in current crime prevention strategies. Given challenges in policing cybercrime, and specifically NFT-related crime, we argue that the crime triangle provides a useful heuristic tool for understanding the nature of NFT-related crime and for preventing such crime from happening.

E D Baymurzina, A S Simakova, V D Poroshina, V N Yurina

No abstract is available for this record.

Bharathi Jagadeesan

A rival has arrived on the unitarity triangle, and the confrontation is unusually clean. Arkani-Hamed, Figueiredo, Hall and Manzari (AHFHM, arXiv:2607.27315) observe that the CKM unitarity-triangle angles lie close to simple fractions of π — (α, β, γ) ≈ (π/2, π/8, 3π/8) — and propose sparse “9-link” Yukawa textures with a spontaneously broken CP phase quantized in multiples of π/8. This note shows the One-Octonion Brane-Bulk (OOB) framework already contains the pattern, with a different and sharper origin. (i) The flat OOB triangle is exactly right-angled. With apex (ρ̅, η̅) = (1/7, √6/7) fixed by the √7 Dehn twist, the apex sides are exactly orthogonal (−6/49 + 6/49 = 0): αflat = π/2 identically, with the right angle split as tan γflat = √6, so γflat = arccos(1/√7) = 67.79° = 3π/8 + 0.29°. The proximity to the π/8 lattice needs no discrete symmetry: an irrational Gudermannian angle grazes it. (ii) One bulk-transit depth warps the observed triangle. α0 = 0.10673 gives (α, β, γ) = (92.01°, 22.63°, 65.36°) — each within 0.4σ of PDG 2026, in a statistical dead heat with the AHFHM anchors today. (iii) The texture-level cross-match. Written in AHFHM's own parametrization (up-frame diagonal; down sector sparsified with the right-handed U(3)dR freedom to a canonical RQ chart), the OOB flavor point lands exactly on their 3π/8-family texture #29 — identical zero pattern and phase slot Yd12 — as its canonical hierarchical representative. The single rephasing invariant is φ = 65.409° = γ + 0.045°: it tracks γ exactly as their leading-order theorem requires, with the next-order texture correction computed here. The same texture chart thus carries two incompatible phase laws: AHFHM-quantized #29 predicts γ ≈ 3π/8 − 0.045° = 67.455°; OOB predicts γ = 65.364°. The mirrored (lower-triangular) gauge reproduces AHFHM's anomalous π/4 histogram peak. Next-generation LHCb/Belle II determinations of γ (sub-degree, 2030s) decide. Framing (stated honestly): the texture identification is a canonical-coordinate statement — the RQ chart's loop phase is a determined, rephasing-invariant function of YdYd† — not a dynamical derivation of the texture; the group-theoretic (Ursa-Major S4 Clebsch-Gordan) route to the magnitudes remains open. No new parameter and no new prediction number is introduced; the note sharpens the framework's long-standing γ stake (Papers CXLIX, CCCII, CCCIII) to the texture level. Verification: one Python gate script and 19 independent Wolfram gates (including symbolic proofs of the exact right angle and tan γ = √6), all passing (supplementary files); one Fugu cross-model pre-publication audit with all confirmed findings repaired. The results are strictly contingent on the established OOB framework — the G2 = Aut(O) reduction, the √7 Ursa-Major twist and apex closure, the Wolfenstein closures λ = √3/(ea*+6) and A = 8/π2, and the Class-I transit depth α0 — none of which is re-derived here. Full symbolic proofs and postulates are consolidated in the BraneBulk Omnibus (concept DOI 10.5281/zenodo.19185171).

Jinyin Song, Hongping Wang, Xiaoqi Li

As the blockchain and decentralized finance (DeFi) ecosystems continue to expand and mature, rug pull scams involving meme coins are occurring with increasing frequency, posing a threat to the security of investors' assets and the healthy development of the industry. Rug Pull scams are characterized by extremely low deployment costs, covert execution, rapid fund transfers, and high detection difficulty. Traditional manual reviews or fixed rules struggle to meet real-time early warning requirements, and existing detection methods generally suffer from issues such as a single feature dimension, inadequate handling of class imbalance, and weak model generalization and interpretability. To address these shortcomings, this paper focuses on the detection of Ethereum-based rug pull scams. First, we clarify their definitions, types, and harm mechanisms, and construct a multi-dimensional feature system based on dimensions such as malicious smart contract design, on-chain transaction anomalies, liquidity manipulation, and social media disclosures. Next, using the "Second Uncle Coin"(token symbol: BOBU) case as an example, we reconstruct the attack process and derive quantitative detection metrics. Subsequently, a risk detection model based on a Multi-Layer Perceptron (MLP) is designed. We employ a combined strategy of SMOTE oversampling and Focal Loss to address the issue of sample imbalance, dynamically search for optimal thresholds to balance precision and recall, and incorporate gradient pruning and early stopping to enhance training stability. Experiments show that the model achieves an accuracy of 0.927, an F1 score of 0.787, and an AUC-ROC of 0.952 on the test set, outperforming traditional methods. Finally, a visualizable web-based detection system is developed using the Flask framework, enabling batch risk assessment, high-risk ranking display, and result export functions.