R-Zero Framework: Autonomy and Zero-Trust

• R-Zero Framework is a novel paradigm combining zero-trust security with autonomous system evolution to enhance decentralized IoT and self-improving LLMs.
• It employs blockchain-based federated learning and adaptive anomaly detection to verify updates and filter malicious data in real time.
• The framework utilizes dynamic trust scoring and self-evolving LLM agents to progressively increase system integrity and performance.

The R-Zero Framework encompasses several distinct lines of research centered on the principles of zero-trust, autonomous system evolution, and high-trust distributed computation. It is applied to domains ranging from decentralized IoT security to self-evolving LLMs trained entirely without human-labeled data. The following sections detail its theoretical foundation, architectural components, optimization methodologies, empirical results, practical implications, and anticipated next steps.

1. Foundational Principles in Zero-Trust and Autonomous System Design

R-Zero adopts a strict “never trust, always verify” paradigm, originally formulated for zero-trust architecture (ZTA) in decentralized IoT systems (Pokhrel et al., 24 Jun 2024). Every participant—be they devices, users, or digital identities—is considered untrustworthy by default, negating reliance on central authorities or perimeter-based security and instead demanding granular, transaction-level authentication and authorization. Autonomous system evolution surfaces in recent research on LLMs (Huang et al., 7 Aug 2025), where models self-improve via self-curricula and agentic adversarial interaction without external supervision.

ZTA implementation occurs through continuous, distributed verification—embedding security policies not only at network endpoints but also into federated learning and dynamic trust computation. The operational baseline replaces static perimeters with programmable, context-aware authentication mechanisms distributed across local devices and model aggregators.

2. Blockchain-Based Federated Learning and Robust Aggregation

A critical layer of the R-Zero Framework in IoT security is the use of blockchain-backed federated learning (Pokhrel et al., 24 Jun 2024). Here, model updates $\Delta M_{(i)}$ are submitted by device $i$ and autonomously verified by smart contracts $SC_j$ via:

$$\text{Verify}(\Delta M_{(i)}) = SC_j(\Delta M_{(i)})$$

Updates passing verification contribute to the global model $M_G$, aggregated through robust functions resilient to malicious updates and adversarial poisoning. The blockchain immutably records approvals and model contributions, achieving verifiable, tamper-resistant provenance. Malicious or anomalous client behaviors are detected and filtered prior to aggregation, securing global learning against both data and model-level attacks.

3. Adaptive Anomaly Detection and Lifelong Learning

R-Zero’s anomaly detection component integrates unsupervised clustering—hyperspherical and hyperellipsoidal techniques—to identify distributional deviation in model updates (Pokhrel et al., 24 Jun 2024). For each update, the system computes:

$$\text{Anomaly}(\Delta M_{(i)}) = \|\Delta M_{(i)} - E[\Delta M]\| > \epsilon$$

where $E[\Delta M]$ represents the expected update and $\epsilon$ is a context-dependent sensitivity threshold. Both local and global anomaly scores are periodically updated and broadcast via blockchain or peer-to-peer communication, enabling distributed detection and remediation even against zero-day attacks and novel failure modes. Lifelong learning mechanisms adapt the sensitivity as network context evolves, preserving collaborative integrity despite adversarial drift or environmental change.

4. Trust Computation System and Dynamic Scoring

Trust within R-Zero is computed dynamically for each device $i$ via a score $T_i(t)$ that updates as a function $f$ or algorithm $L$ of prior trust and contextual observables $C_i(t)$:

$$T_i(t + 1) = f(T_i(t), C_i(t)) \;\; \text{or} \;\; T_i(t + 1) = L(T_i(t), C_i(t))$$

This continuous update mechanism rewards policy-compliant behavior and degrades scores following detected anomalies. Trust scores are distributed and recorded on the blockchain, ensuring that authentication and aggregation operations depend on current, tamper-evident trust attestations.

5. R-Zero as Self-Evolving Reasoning LLM–Zero-Data Training Paradigm

In recent research, R-Zero extends its self-organizing design to LLMs by establishing two co-evolving agents: Challenger and Solver (Huang et al., 7 Aug 2025). The Challenger autonomously generates question tasks—targeting the boundary of the Solver’s capability—while the Solver repeatedly attempts solution. Reward structures for the Challenger involve the uncertainty band (Solver achieves accuracy near $50\%$) and penalties for repetition, ensuring a steady curriculum of increasingly difficult problems.

Optimization leverages Group Relative Policy Optimization (GRPO):

• For each batch, Challenger and Solver rewards are normalized via z-score:

$$\hat{A}_i = \frac{r_i - \text{mean}(r_1, \ldots, r_G)}{\text{std}(r_1, \ldots, r_G) + \epsilon_{norm}}$$

• Policy updated using clipped surrogate loss with KL regularization:

$$L_{GRPO}(\theta) = -\frac{1}{G} \sum_{i=1}^G \min\left\{ \frac{\pi_\theta(x_i)}{\pi_{\theta_\text{old}}(x_i)} \cdot \hat{A}_i, \text{clip}\left(\frac{\pi_\theta(x_i)}{\pi_{\theta_\text{old}}(x_i)}, 1-\epsilon, 1+\epsilon \right) \cdot \hat{A}_i \right\} + \beta \, KL (\pi_\theta \| \pi_{\theta_\text{old}})$$

Pseudo-labeling for the Solver uses majority voting over $m$ answers, and difficulty band selection ensures the continual evolution of both agents. This closed training loop allows models to scale in reasoning capacity without external datasets or human-generated rewards, empirically yielding performance gains of $+6.49$ on math benchmarks and $+7.54$ on general reasoning benchmarks for Qwen3-4B-Base.

6. Empirical Evaluation and Performance

Empirical results demonstrate progressive gains across benchmarks (Huang et al., 7 Aug 2025). For IoT security, blockchain-based federated learning effectively detects and discards poisoned updates (Pokhrel et al., 24 Jun 2024), while co-evolving LLM pairs autonomously enhance reasoning capabilities over independently curated datasets and benchmark suites. Gains are incremental, accumulating with each training iteration and compounding curriculum difficulty.

7. Scalability, Privacy, and Cryptographic Evolution

Scalability is addressed through further refinement of clustering and aggregation to support deployments with thousands of devices (Pokhrel et al., 24 Jun 2024). The adoption of Dirichlet processes for anomaly detection is posited for more flexible, incremental identification of distributional novelty. Privacy is to be enforced through differential privacy mechanisms during both training and trust update, balancing accuracy with confidentiality requirements. The R-Zero Framework anticipates integration of post-quantum cryptographic algorithms—lattice-based, hash-based primitives—to ensure resilience against quantum threats, extending both the blockchain verification and trust computation layers for quantum security readiness.

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The R-Zero Framework integrates robust zero-trust design, autonomous self-supervision, blockchain-secured federated learning, dynamic trust computation, and adaptive anomaly detection. It advances system integrity, collaborative trust, and self-improving intelligence across decentralized and autonomous environments, and its trajectory includes further scalability, privacy, and cryptographic modernization.