arXiv subjects

Mohd Sameen Chishti

Publications and source records attributed to Mohd Sameen Chishti.

4 recordsLinked to original sources

arXiv2026

Feature-Centric Methodology for Analyzing Cross-Chain NFT Migration Compatibility

Cross-chain NFT migration refers to the process of transferring digital assets along with their associated functionalities and guarantees between distinct blockchain platforms. However, architectural divergences among these platforms introduce critical challenges, often resulting in features that fail to behave as intended. While protocol-level mechanisms can coordinate data transfer, they are insufficient to resolve deeper compatibility issues arising from fundamental differences in state organization, transaction execution, and ownership representation. Thus, the critical challenge lies in predicting which NFT features can be preserved, which require redesign, and which are fundamentally incompatible, prior to undertaking costly migration attempts. To address this challenge, we first derive a tailored four-layer NFT architecture based on standard blockchain stacks, distinguishing cryptographic, state-management, transaction-processing, and ownership primitives, with explicit upward dependencies. Building on this architecture, we conceptualize an NFT as a bundle of features and define successful cross-chain NFT migration as the preservation of these features. Grounded in this model, we propose a four-phase migration analysis methodology comprising source feature specification, primitive-level dependency mapping, target platform profiling, and compatibility assessment, which classifies each feature as natively preserved, partially mismatched, or completely mismatched. We evaluate this methodology through a proof-of-concept analysis of Ethereum-to-Solana NFT migration, identifying several incompatibility issues that hinder seamless NFT migration.

cs.SE↗

arXiv2026

Test Before You Deploy: Governing Updates in the LLM Supply Chain

Large Language Models (LLMs) are increasingly used as core dependencies in software systems. However, the hosted LLM services evolve continuously through provider-side updates without explicit version changes. These silent updates can introduce behavioral drift, causing regressions in functionality, formatting, safety constraints, or other application-specific requirements. Existing approaches focus primarily on regression testing or versioning but do not provide deployer-side mechanisms for governing compatibility during opaque model evolution. This paper proposes a deployment-side governance framework based on three components: clearly defined rules for how the model is allowed to behave (production contracts), focused testing organized by deployment risk categories (risk-category-based testing suite), and release checkpoints that block updates unless they meet defined safety and performance standards (compatibility gates). Through exploratory validation across multiple LLM versions, we provide evidence that targeted testing in specific risk areas can uncover performance regressions that overall metrics miss. We also identify several open research challenges, including how to systematically build effective test suites, how to set reliable performance thresholds in non-deterministic systems, and how to detect and explain model drift when providers offer limited transparency. Overall, we frame LLM update management as a software supply chain governance problem and outline a research agenda for putting deployer-side compatibility controls into practice.

cs.SE↗

arXiv2026

AgentReputation: A Decentralized Agentic AI Reputation Framework

Decentralized, agentic AI marketplaces are rapidly emerging to support software engineering tasks such as debugging, patch generation, and security auditing, often operating without centralized oversight. However, existing reputation mechanisms fail in this setting for three fundamental reasons: agents can strategically optimize against evaluation procedures; demonstrated competence does not reliably transfer across heterogeneous task contexts; and verification rigor varies widely, from lightweight automated checks to costly expert review. Current approaches to reputation drawing on federated learning, blockchain-based AI platforms, and large language model safety research are unable to address these challenges in combination. We therefore propose \textbf{AgentReputation}, a decentralized, three-layer reputation framework for agentic AI systems. The framework separates task execution, reputation services, and tamper-proof persistence to both leverage their respective strengths and enable independent evolution. The framework introduces explicit verification regimes linked to agent reputation metadata, as well as context-conditioned reputation cards that prevent reputation conflation across domains and task types. In addition, AgentReputation provides a decision-facing policy engine that supports resource allocation, access control, and adaptive verification escalation based on risk and uncertainty. Building on this framework, we outline several future research directions, including the development of verification ontologies, methods for quantifying verification strength, privacy-preserving evidence mechanisms, cold-start reputation bootstrapping, and defenses against adversarial manipulation.

cs.AI↗

arXiv2025

A Proof of Success and Reward Distribution Protocol for Multi-bridge Architecture in Cross-chain Communication

Single-bridge blockchain solutions enable cross-chain communication. However, they are associated with centralization and single-point-of-failure risks. This paper proposes Proof of Success and Reward Distribution (PSCRD), a novel multi-bridge response coordination and incentive distribution protocol designed to address the challenges. PSCRD introduces a fair reward distribution system that equitably distributes the transfer fee among participating bridges, incentivizing honest behavior and sustained commitment. The purpose is to encourage bridge participation for higher decentralization and lower single-point-of-failure risks. The mathematical analysis and simulation results validate the effectiveness of PSCRD using two key metrics: the Gini index, which demonstrates a progressive improvement in the fairness of the reward distribution as new bridge groups joined the network; and the Nakamoto coefficient, which shows a significant improvement in decentralization over time. These findings highlight that PSCRD provides a more resilient and secure cross-chain bridge system without substantially increasing user costs.

cs.CR↗