Ev-Trust: Decentralized Trust for Multi-Agent Systems

• Ev-Trust is a trust framework that leverages verifiable evidence, formal metrics, and decentralized, game-theoretic mechanisms to authenticate multi-agent interactions.
• It employs blockchain-based logging and cryptographic protocols to ensure auditability, security, and resistance to fraud or manipulation.
• The framework integrates evolutionary and incentive-aligned models to dynamically align agent rewards and maintain robust, scalable trust across systems.

Ev-Trust encompasses a class of trust frameworks characterized by rigorous linkage between verifiable evidence, formal trust metrics, and decentralized or game-theoretic mechanisms deployed in agentic, blockchain, or cyber-physical multi-agent environments. Its principal instantiations synthesize evidence-based interaction logging, formal trust computation, incentive-aligned coordination, and—in emerging settings—strategy equilibrium and evolutionary game-theoretic dynamics, all focused on fostering robust, scalable, and auditable trust among heterogeneous autonomous entities.

1. Formal Models and Definitions

The Ev-Trust paradigm formally specifies trust as a function of objectively verifiable past interactions, feedback traces, and explicit trust metrics. In the canonical blockchain-based framework, trust is modeled by maps over the sets of interactions $I$, reviews $X$, and service providers $Y$:

• Partial evidence map: $\varphi: X \rightharpoonup I$ links each feedback to a unique interaction.
• Service projection: $\pi: I \rightarrow Y$ associates interactions with providers.
• Feedback selection: $\omega: I \times X \rightarrow [0,1]$ weights multiple feedbacks per interaction.
• Scoring mechanism: $\mu(\alpha)$, $\alpha: I \rightharpoonup X$ is an injective feedback trace, scored via Bayesian, Dempster–Shafer, or subject logic trust models.

The aggregate trust score for a service $y$ is

$$T(y) = \sigma\bigl(\pi^{-1}(y)\bigr) = \sum_{\alpha: \mathrm{dom}(\alpha) = \pi^{-1}(y)} \mu(\alpha) \prod_{i \in \pi^{-1}(y)} \omega(i, \alpha(i))$$

For evolutionary, agent-based services, aggregate trust between agents $A$ and $B$ is

$$T_t^A(B) = \omega\,T_{t,\,\text{direct}}^A(B) + (1-\omega)\,T_{t,\,\text{indirect}}^A(B)$$

where direct trust is Bayesian-updated on observed outcomes, and indirect trust aggregates over neighbors’ ratings (Yang et al., 18 Dec 2025, Pal et al., 2021).

2. Evidence-Based Trust and Blockchain Implementation

Ev-Trust distinguishes itself by cryptographically binding feedback to verifiable, on-chain evidence. Every feedback must be linked to a digitally signed, timestamped event, e.g., in access-control logs for resource usage, energy transactions in V2G, or financial service exchanges. Blockchain smart contracts enforce the following protocol:

• On-chain interaction transactions represented as $$\mathsf{AccessEvent}(\text{user}, \text{resource}, \text{uid}, \mathsf{sig_{user}})$$.
• Feedback transactions $$\mathsf{FeedbackEvent}(\text{user}, \text{resource}, \text{rating}, \text{uid}, \mathsf{sig_{user}})$$ directly reference interactions via a unique $\text{uid}$.
• Submit-feedback protocol permits a single feedback per unique interaction, enforced by on-chain logic and digital signatures.

The architecture supports distributed trust calculation marketplaces: each provider independently scores available evidence using its preferred trust metric, aligning with user and context requirements (Pal et al., 2021).

3. Decentralized Trust in Cyber-Physical and V2G Systems

Within vehicle-to-grid and broader IoEV platforms, Ev-Trust leverages a cyber-physical blockchain layer, integrating smart charging points (SCPs), EV light clients, and permissioned blockchains to guarantee immutability, auditability, and privacy:

• Physical layer: SCPs conduct bidirectional metering, execute energy commands, and expose cryptographically authenticated logs.
• Cyber layer: Each SCP acts as a blockchain node; EVs interact as light clients, providing transaction signatures and querying proof-of-inclusion for their trades.
• Transactions (charge/discharge decisions, SoC readings, price updates) are bundled by SCPs, achieving consensus using fast-path PBFT or standard BFT protocols for high performance and fault tolerance (Chen et al., 2024, Chen et al., 2024).

Key trust enhancements in V2G include:

• Pseudonymous trade and payment, supporting privacy via one-time wallet addresses.
• Immutable, cryptographically auditable logs validated by edge devices.
• Automated penalty enforcement and settlement via smart contracts.

4. Game-Theoretic and Evolutionary Trust Mechanisms

Ev-Trust frameworks employ rigorous game-theoretic foundations to align participant incentives, minimizing possibilities for fraud, manipulation, or adversarial exclusion. In V2G and agentic environments, trust-driven interactions are modeled as Stackelberg or evolutionary games:

• Stackelberg game in V2G: Aggregators (leaders) set real-time charge/discharge prices; EVs (followers) best-respond based on state-of-charge, utility functions, price thresholds, and battery degradation (Chen et al., 2024, Chen et al., 2024).
• Agentic evolutionary trust: Populations of requestors and providers evolve their strategies (e.g., honest, low-quality, fraud, decline), with replicator dynamics steering the system toward stable equilibria where high-trust behaviors dominate. Trust calculations (direct and indirect) influence strategy choices via continuation value terms, enforcing dynamic, feedback-driven evolution of trust (Yang et al., 18 Dec 2025, Lim et al., 2023).

In formal multi-agent services, stability requires that the honest-high-quality equilibrium be locally asymptotically stable, i.e.,

$$\pi_H(1,1)-\pi_Y(1,1) > 0,\qquad \pi_{SH}(1,1)-\pi_{SL}(1,1)>0$$

Nodes or agents that persistently deviate from trustworthy behavior are excluded through collective adaptation dynamics.

5. Privacy, Security, and Regulatory Considerations

Ev-Trust architectures are designed to simultaneously guarantee evidence verifiability, privacy, and compliance:

• Privacy mechanisms: Pseudonymization (rotating addresses or session-based IDs), optional zero-knowledge proofs or ring signatures (for unlinkability), and light-client protocols minimize information revealed to third parties (Chen et al., 2024, Asokraj et al., 2022).
• Security properties: Mutual authentication, defense against replay and man-in-the-middle attacks, resistance to Sybil fraud (via permissioned validator sets), and non-repudiation for all evidence records.
• Regulatory recommendations: Absence of interoperable standards and liability/audit frameworks is identified as a deployment barrier. Policy guidance includes sandboxing, standardized smart-contract languages, identity management, and cross-chain oracle interoperability (Chen et al., 2024).

6. Empirical Results and Comparative Performance

Ev-Trust instantiations consistently demonstrate high scalability, fault tolerance, and economic efficiency:

System	Throughput (tx/s)	Latency (ms)	Privacy Model	Provable Fairness
PBFT V2G (baseline)	~650	~75	Pseudonymous	Yes
Fast-path PBFT V2G	~900	~45	Pseudonymous	Yes
Agentic IoEV (AAI)	>200	<minutes	Pseudonymous, LLM-RAG	Yes

• V2G deployments: Charging costs reduced by up to 26%; discharging revenue increased by 216%; auxiliary service coverage 48–98% of demand; all with sub-second consensus on thousands of micro-transactions (Chen et al., 2024, Chen et al., 2024).
• Multi-agent services: Malicious strategy participation drops below 10%, with honest agents achieving near-optimal revenue and exclusion of fraudsters within ~34 rounds (Yang et al., 18 Dec 2025).
• IoEV: State-of-Health (SoH) detection accuracy 96.1%, attack detection up to 98.6%; actionable explanations with BARTScore >0.9 maintain high human trust (Dif et al., 8 Sep 2025).

7. Significance and Perspectives

The Ev-Trust framework marks a rigorous synthesis of evidence-based verification, decentralized computation, and incentive-compatible dynamics for trust establishment in both cyber-physical and digital multi-agent environments.

This approach achieves:

• Transparent, irrefutable auditability for all interactions.
• Alignment of self-interested agent actions with global objectives through mechanism design.
• Deployment models that scale to thousands of agents/validators without sacrificing performance, security, or privacy.
• A modular, formally grounded basis for integrating trustworthiness as a first-class property in critical infrastructures and open service systems.

A plausible implication is that Ev-Trust mechanisms provide a model for future agentic, decentralized systems to robustly resist manipulation, sustain high social welfare, and deliver formal verifiability to users and regulators alike (Pal et al., 2021, Chen et al., 2024, Yang et al., 18 Dec 2025, Dif et al., 8 Sep 2025).