arXiv subjects

Miguel Correia

Publications and source records attributed to Miguel Correia.

At least 19 recordsLinked to original sources

arXiv2026

IntentFuzz: A Protocol-Aware Fuzzer for Automated Invariant Violation Detection in Intent-Based Cross-Chain Bridges

Cross-chain bridges move value between blockchains. Intent-based bridges are a variant where a solver fulfills a user's declared outcome and an off-chain settlement layer later reconciles the fill against the deposit. Existing smart-contract fuzzers and static analyzers only flag known-bad code patterns or require protocol-specific hand-written assertions. This work formalizes a taxonomy separating invariant violations, safety properties a contract must enforce locally, from settlement exposures legitimately delegated to the off-chain settlement layer, and proposes IntentFuzz: a protocol-aware fuzzer that recovers a bridge's intent structure and deposit/fill function roles directly from unannotated Solidity source, then synthesizes multi-step fuzz sequences using an LLM-based fallback to help build call arguments. IntentFuzz recovers the correct intent structure in 9/9 benchmark protocols and classifies deposit and fill functions with 100% recall and 82% combined precision; across a corpus of 77 manually labeled contracts, it reaches 79.5% bridge-classification precision and 97.2% recall, and among confirmed bridges, struct selection reaches 88.6% precision and recall while deposit and fill classification each reach 100% recall. On 23 planted-bug mutants, IntentFuzz attains 100% recall and 100% precision, executing 273 templates (507 transactions in a median of 14ms per template). Across 24 real-world deployments, it confirms 17 genuine invariant violations under heuristic-only input generation, rising to 22 with its LLM-assisted tier enabled, spanning eight vulnerable GitHub repositories, each finding reproducible against public, deployed bytecode.

cs.CR↗

arXiv2026

Gravitational Compton Amplitude to All Orders in Perturbation Theory

We show how amplitudes from the scattering of gravitational waves off compact objects in worldline effective field theory can be efficiently computed to arbitrary order in Newton's constant $G$. Our approach solves an effective wave equation for the partial-wave amplitude in which Wilson coefficients (tidal Love numbers) enter through boundary conditions at short distances. We then perform the sum over partial waves to obtain the momentum-space Compton amplitude in terms of elliptic polylogarithms. We reproduce recent results through $\mathcal{O}(G^4)$ and obtain new predictions up to $\mathcal{O}(G^7)$, with Love numbers first contributing at $\mathcal{O}(G^5)$. We find the first ultraviolet divergence in a classical gravitational amplitude at $\mathcal{O}(G^7)$, showing that a pure point-particle description is not consistent in general relativity. Matching to black hole perturbation theory, we show that static Love numbers vanish on-shell and predict subleading non-zero Schwarzschild black hole Love numbers.

hep-th↗

arXiv2026

Belief-Aware Pivotal Mechanism for DAO Committees

Decentralized Autonomous Organizations (DAOs) increasingly delegate decisions to small committees whose members hold two independent kinds of private information: idiosyncratic preferences over alternatives (what they want) and beliefs about which alternative best serves the organization (what they know). Members have no reason to reveal what they know unless they are incentivized to do so. Standard voting rules are designed to extract only what members want. If these two pieces of information are not aligned, the organization can end up making a suboptimal decision. Existing mechanisms for eliciting (extracting) beliefs from experts, such as Decision Scoring Rules and Decision Markets, face well-known impossibility results in multi-agent settings under deterministic decision rules, and ignore the fact that agents can have idiosyncratic preferences over alternatives. This paper proposes a mechanism for binary committee decisions that augments the pivotal mechanism, an instance of the Groves mechanism, with a reward that depends on the outcome distributed after a boolean signal, indicating success or failure of the outcome, is observed. The mechanism aggregates the experts' private information to maximize the probability that its decision agrees with a weighted linear pooling of the experts' beliefs, framing the designer's problem as classification rather than welfare maximization. An affine family of reward rules is proposed, and the region of parameters that simultaneously satisfy dominant-strategy incentive compatibility, interim individual rationality, and budget feasibility is derived. In informative-belief environments, the mechanism outperforms majority voting, including majority voting augmented with the same outcome-contingent rewards, especially when the committee's average preferences are biased against the superior alternative.

cs.GT↗

arXiv2026

InsureConnect: Blockchain and Digital Identity for the Property Insurance Market

This paper presents InsureConnect, a blockchain-based system for improving transparency, authentication, and auditability in property-insurance workflows after natural disasters. The system combines Self-Sovereign Identity (SSI), Decentralized Identifiers (DIDs), Verifiable Credentials (VCs), satellite imagery, Hyperledger Fabric, and IPFS to register identities, insurance contracts, and damage claims. Property images are stored off-chain in IPFS, while content hashes and signed records are maintained on a permissioned blockchain. Users interact with the system through a desktop application, while chaincode enforces role-based access control and validates digital signatures. The prototype was evaluated under concurrent request loads from 50 to 3000 requests, measuring latency, throughput, and dropped connections. The results indicate that the system sustains increasing throughput under load, although latency rises and dropped connections appear at higher concurrency levels.

cs.CR↗

arXiv2026

Exploiting Liquidity Exhaustion Attacks in Intent-Based Cross-Chain Bridges

Intent-based cross-chain bridges have emerged as an alternative to traditional interoperability protocols by allowing off-chain entities (\emph{solvers}) to immediately fulfill users' orders by fronting their own liquidity. While improving user experience, this approach introduces new systemic risks, such as solver liquidity concentration and delayed settlement. In this paper, we propose a new class of attacks called \emph{liquidity exhaustion attacks} and a replay-based parameterized attack simulation framework. We analyze 3.5 million cross-chain intents that moved \$9.24B worth of tokens between June and November 2025 across three major protocols (Mayan Swift, Across, and deBridge), spanning nine blockchains. For rational attackers, our results show that protocols with higher solver profitability, such as deBridge, are vulnerable under current parameters: 210 historical attack instances yield a mean net profit of \$286.14, with 80.5\% of attacks profitable. In contrast, Across remains robust in all tested configurations due to low solver margins and very high liquidity, while Mayan Swift is generally secure but becomes vulnerable under stress-test conditions. Under byzantine attacks, we show that it is possible to suppress availability across all protocols, causing dozens of failed intents and solver profit losses of up to \$978 roughly every 16 minutes. Finally, we propose an optimized attack strategy that exploits patterns in the data to reduce attack costs by up to 90.5\% compared to the baseline, lowering the barrier to liquidity exhaustion attacks.

cs.CR↗

arXiv2026

SOFIA: Singularities of Feynman Integrals Automatized

We introduce SOFIA, a Mathematica package that automatizes the computation of singularities of Feynman integrals, based on new theoretical understanding of their analytic structure. Given a Feynman diagram, SOFIA generates a list of potential singularities along with a candidate symbol alphabet. The package also provides a comprehensive set of tools for analyzing the analytic properties of Feynman integrals and related objects, such as cosmological and energy correlators. We showcase its capabilities by reproducing known results and predicting singularities and symbol alphabets of Feynman integrals at and beyond the high-precision frontier.

hep-th↗

arXiv2025

Document Data Matching for Blockchain-Supported Real Estate

The real estate sector remains highly dependent on manual document handling and verification, making processes inefficient and prone to fraud. This work presents a system that integrates optical character recognition (OCR), natural language processing (NLP), and verifiable credentials (VCs) to automate document extraction, verification, and management. The approach standardizes heterogeneous document formats into VCs and applies automated data matching to detect inconsistencies, while the blockchain provides a decentralized trust layer that reinforces transparency and integrity. A prototype was developed that comprises (i) an OCR-NLP extraction pipeline trained on synthetic datasets, (ii) a backend for credential issuance and management, and (iii) a frontend supporting issuer, holder, and verifier interactions. Experimental results show that the models achieve competitive accuracy across multiple document types and that the end-to-end pipeline reduces verification time while preserving reliability. The proposed framework demonstrates the potential to streamline real estate transactions, strengthen stakeholder trust, and enable scalable, secure digital processes.

cs.CR↗

arXiv2025

Resonances: Universality and Factorization on Higher Sheets

Most particles in nature are unstable, manifesting as resonances in scattering processes. Using analyticity and unitarity, we show nonperturbatively that resonances, defined as poles on higher Riemann sheets of scattering amplitudes, share basic properties with stable particles: (i) Universality, that a resonance generically appears in every S-matrix element; and (ii) Factorization, that amplitudes factorize on resonance poles. Our framework applies in any spacetime dimension and across arbitrarily many two-particle cuts, including cases where the kinematic Riemann surface becomes infinitely sheeted. Importantly, we find that resonance data (mass, width, couplings, and sheet index) are fully encoded on the physical sheet, where causality can impose additional constraints. These results are relevant for extending S-matrix bootstrap studies beyond elastic scattering.

hep-th↗

arXiv2025

Analyticity of the Black Hole S-Matrix

We establish the analytic structure of the S-matrix in the complex-frequency plane for classical wave scattering on a Schwarzschild background in four space-time dimensions. Our argument relies on the analytic continuation of the gravitational potential, with the singularity behind the horizon playing a crucial role. We find that in the lower half-plane the partial-wave amplitudes are analytic except for the quasinormal-mode poles and the branch cut associated with late-time tails. As a direct consequence of causality, the retarded Green's function and absorption amplitude are analytic in the upper-half plane. We show, however, that Stokes phenomena can obstruct this analyticity domain from carrying over to the elastic amplitude, which instead develops a branch-cut in the upper-half plane. We also determine the effect of infrared (IR) regulators on the analytic structure, showing that polynomial boundedness requires a sharp lower bound on the IR cutoff in terms of the Schwarzschild radius.

hep-th↗

arXiv2025

Large Language Models for Explainable Threat Intelligence

As cyber threats continue to grow in complexity, traditional security mechanisms struggle to keep up. Large language models (LLMs) offer significant potential in cybersecurity due to their advanced capabilities in text processing and generation. This paper explores the use of LLMs with retrieval-augmented generation (RAG) to obtain threat intelligence by combining real-time information retrieval with domain-specific data. The proposed system, RAGRecon, uses a LLM with RAG to answer questions about cybersecurity threats. Moreover, it makes this form of Artificial Intelligence (AI) explainable by generating and visually presenting to the user a knowledge graph for every reply. This increases the transparency and interpretability of the reasoning of the model, allowing analysts to better understand the connections made by the system based on the context recovered by the RAG system. We evaluated RAGRecon experimentally with two datasets and seven different LLMs and the responses matched the reference responses more than 91% of the time for the best combinations.

cs.CL↗

arXiv2025

Nonperturbative Anomalous Thresholds

Feynman diagrams (notably the triangle diagram) involving heavy enough particles contain branch cuts on the physical sheet - anomalous thresholds - which, unlike normal thresholds and bound-state poles, do not correspond to any asymptotic $n$-particle state. ``Who ordered that?" We show that anomalous thresholds arise as a consequence of established S-matrix principles and two reasonable assumptions: unitarity below the physical region and analyticity in the mass. We find explicit nonperturbative formulas for the anomalous threshold singularity and test them against the Coleman-Thun poles of the exactly solvable $E_8$ integrable model.

hep-th↗

arXiv2025

XChainWatcher: Monitoring and Identifying Attacks in Cross-Chain Bridges

Cross-chain bridges are a type of middleware for blockchain interoperability that supports the transfer of assets and data across blockchains. However, several of these bridges have vulnerabilities that have caused 3.2 billion dollars in losses since May 2021. Some studies have revealed the existence of these vulnerabilities, but there is little quantitative research available, and there are no safeguard mechanisms to protect bridges from such attacks. Furthermore, no studies are available on the practices of cross-chain bridges that can cause financial losses. We propose \toolName~(Cross-Chain Watcher), a modular and extensible logic-driven anomaly detector for cross-chain bridges. It operates in three main phases: (1) decoding events and transactions from multiple blockchains, (2) building logic relations from the extracted data, and (3) evaluating these relations against a set of detection rules. Using \toolName, we analyze data from two previously attacked bridges: the Ronin and Nomad bridges. \toolName~was able to successfully identify the transactions that led to losses of \$611M and \$190M (USD) and surpassed the results obtained by a reputable security firm in the latter. We not only uncover successful attacks, but also reveal other anomalies, such as 37 cross-chain transactions (\CCTX) that these bridges should not have accepted, failed attempts to exploit Nomad, over \$7.8M worth of tokens locked on one chain but never released on Ethereum, and \$200K lost by users due to inadequate interaction with bridges. We provide the first open dataset of 81,000 \CCTXS~across three blockchains, capturing more than \$4.2B in token transfers.

cs.CR↗

arXiv2025

Recursive Landau Analysis

We propose a recursive method that makes use of the basic principle of unitarity to calculate the Landau singularities of n-point scattering amplitudes directly in kinematic space. For a vast class of Feynman diagrams, the method enables rapid analytic computation of Landau singularities beyond current state-of-the-art technology. This includes new predictions relevant for two- and higher-loop processes in the Standard Model involving both massive quarks and electroweak particles.

hep-th↗

arXiv2025

Cross-Section Bootstrap: Unveiling the Froissart Amplitude

We derive a universal bound on the integrated total scattering cross-section at \emph{finite} energies, expressed in terms of a single low-energy coefficient constrained by the non-perturbative S-matrix Bootstrap. At high energies, the bound is compared with proton-proton scattering data; at low energies, with numerical bootstrap results obtained by directly maximizing the cross-section. We conjecture that the amplitude saturating the cross-section at high energies lies at a strongly-coupled corner of the allowed space of low-energy parameters. This universal amplitude exhibits a rising total cross-section, a shrinking elastic differential cross-section with multiple diffractive minima, and a surprisingly rich spectrum of resonances aligning along Regge trajectories, including Pomeron-like and daughter trajectories, as well as unusual ``singular" trajectories in the forward limit which appear deeply interconnected with Froissart growth. Remarkably, the eikonal representation reveals that the scattering is localized within an annular region that slowly expands with energy, challenging the traditional ``disk" diffraction picture. Our results open the door to theoretical and phenomenological studies of \emph{soft} high-energy hadronic scattering via the S-matrix Bootstrap.

hep-th↗

arXiv2025

Blockchain Oracles for Real Estate Rental

Blockchain technology has seen adoption across various industries and the real estate sector is no exception. The traditional property leasing process guarantees no trust between parties, uses insecure communication channels, and forces participants who are not familiar with the process to perform contracts. Blockchain technology emerges as a solution to simplify the traditional property leasing process. This work proposes the use of two blockchain oracles to handle, respectively, maintenance issues and automate rent payments in the context of property rental. These two components are introduced in a blockchain-based property rental platform.

cs.CR↗

arXiv2025

The Born regime of gravitational amplitudes

We study the $2 \to 2$ scattering in the regime where the wavelength of the scattered objects is comparable to their distance but is much larger than any Compton wavelength in the quantum field theory. We observe that in this regime - which differs from the eikonal - the Feynman diagram expansion takes the form of a geometric series, akin to the Born series of quantum mechanics. Conversely, we can define the Feynman diagram expansion as the Born series of a relativistic effective-one-body (EOB) Schrödinger equation. For a gravitational theory in this regime we observe that the EOB Schrödinger equation reduces to the Regge-Wheeler or Teukolsky wave equations. We make use of this understanding to study the tree-level Compton scattering off a Kerr black hole. We compute the scalar and photon Compton amplitude up to $O(a^{30})$ in the black hole spin $a$ and propose an all-order expression. Remarkably, we find that boundary terms, which are typically neglected, give non-zero contact pieces necessary for restoring crossing symmetry and gauge invariance of the Kerr-Compton amplitude.

hep-th↗

arXiv2025

Gravitational Wave Scattering via the Born Series: Scalar Tidal Matching to $\mathcal{O}(G^7)$ and Beyond

We introduce a novel method to compute gravitational wave amplitudes within the framework of effective field theory. By reinterpreting the Feynman diagram expansion as a Born series, our method offers several key advantages. It directly yields partial wave amplitudes, streamlining the matching with black hole perturbation theory. Long-distance gravitational interactions are unambiguously factorized from short-distance tidal effects, including dissipation, which are systematically incorporated via an in-in worldline effective action. Crucially, at every order in perturbation theory, integrals are expressed in terms of harmonic polylogarithms, enabling an end-to-end computation scalable to arbitrary orders. We illustrate the method with new predictions for scalar black hole Love numbers and their Renormalization Group equations to $\mathcal{O}(G^7)$.

hep-th↗

arXiv2025

XChainDataGen: A Cross-Chain Dataset Generation Framework

The number of blockchain interoperability protocols for transferring data and assets between blockchains has grown significantly. However, no open dataset of cross-chain transactions exists to study interoperability protocols in operation. There is also no tool to generate such datasets and make them available to the community. This paper proposes XChainDataGen, a tool to extract cross-chain data from blockchains and generate datasets of cross-chain transactions (cctxs). Using XChainDataGen, we extracted over 35 GB of data from five cross-chain protocols deployed on 11 blockchains in the last seven months of 2024, identifying 11,285,753 cctxs that moved over 28 billion USD in cross-chain token transfers. Using the data collected, we compare protocols and provide insights into their security, cost, and performance trade-offs. As examples, we highlight differences between protocols that require full finality on the source blockchain and those that only demand soft finality (\textit{security}). We compare user costs, fee models, and the impact of variables such as the Ethereum gas price on protocol fees (\textit{cost}). Finally, we produce the first analysis of the implications of EIP-7683 for cross-chain intents, which are increasingly popular and greatly improve the speed with which cctxs are processed (\textit{performance}), thereby enhancing the user experience. The availability of XChainDataGen and this dataset allows various analyses, including trends in cross-chain activity, security assessments of interoperability protocols, and financial research on decentralized finance (DeFi) protocols.

cs.CR↗