arXiv subjects

John deVadoss

Publications and source records attributed to John deVadoss.

3 recordsLinked to original sources

arXiv2026

From Token Interfaces to Token Semantics: A Formal Composition and Conformance Model for Implementation-Neutral Token Specifications

Digital-token standards such as ERC-20, ERC-721, and ERC-1155 have been essential to blockchain adoption because they standardize callable interfaces. Interface standardization, however, does not fully specify token meaning. Two implementations may expose the same transfer function while encoding different assumptions about supply, divisibility, redemption, cancellation, evidence, governance, and lifecycle finality; conversely, two semantically equivalent tokens may be implemented on different ledgers and through different transaction models. This paper presents the InterWork Alliance Token Taxonomy Framework (TTF) as a typed semantic composition model for implementation neutral token specifications. We formalize TTF artifacts, token formulas, behavior and property-set composition, well-formedness constraints, and a layered conformance model covering formula, artifact, message, state, and trace conformance. We further define a reference validation procedure and show how platform neutral control messages can induce conformance obligations and implementation tests. The model is evaluated analytically through document-token, warehouse-receipt, and carbon/digital-MRV case studies, together with representative invalid compositions that interface standards alone do not expose. The analysis shows that token semantics can be specified, compared, validated, mapped, and governed independently of platform binding, providing a foundation for more reliable token interoperability across smart contract platforms, permissioned ledgers, and shared-state systems.

cs.DC↗

arXiv2026

Designing Intelligent Enterprise Agents: A Capability-Aligned Multi-Agent Architecture

Enterprise interest in multi-agent systems has shifted from generic software agents to large-language-model (LLM) based intelligent agents that plan, use tools, maintain contextual memory, inspect intermediate results, collaborate with other agents, and sometimes act in systems of record. This paper revises the enterprise architecture thesis around a design-first claim: governance is necessary, but it cannot be the primary organizing abstraction. The primary abstraction must be agent design - capability boundaries, autonomy allocation, interaction protocols, tool and data authority, state and memory design, verification design, and human interaction design. We propose CEAD (Capability-Aligned Enterprise Agent Design), a reference architecture for intelligent agents that uses service-oriented architecture (SOA) as an exemplar for contracts, registries, loose coupling, and policy-aware integration, while explicitly rejecting the idea that services are agents. It treats microservices as a cautionary precedent: decomposition without design discipline produces distributed complexity, cost, operational fragility, and agent proliferation. We evaluate CEAD over 10,000 enterprise tasks, comparing five architectures: a prompt-first mono-agent, a role-based micro-agent swarm, SOA-brokered agents, a governance-first but design-poor agent grid, and the proposed CEAD architecture. CEAD achieves 70.6% safe success, versus 45.2% for the mono-agent baseline, 23.1% for the ungoverned micro-agent swarm, 58.8% for SOA-brokered agents, and 50.8% for the control-heavy, design-poor grid. The results support the conclusion that design quality is the first-order enterprise concern; governance, security, policy, audit, and assurance should support and enforce good design rather than substitute for it.

cs.MA↗

arXiv2025

A Byzantine Fault Tolerance Approach towards AI Safety

Ensuring that an AI system behaves reliably and as intended, especially in the presence of unexpected faults or adversarial conditions, is a complex challenge. Inspired by the field of Byzantine Fault Tolerance (BFT) from distributed computing, we explore a fault tolerance architecture for AI safety. By drawing an analogy between unreliable, corrupt, misbehaving or malicious AI artifacts and Byzantine nodes in a distributed system, we propose an architecture that leverages consensus mechanisms to enhance AI safety and reliability.

cs.DC↗