Exhaustive Analysis and Upgraded Formulation of the 8-Phase MIRR Arsenal Roadmap
Introduction to the MIRR Arsenal Paradigm
The deployment of autonomous, self-modifying, and safety-critical artificial intelligence within complex software ecosystems demands a rigorous architectural framework. Historically, the integration of formal verification, continuous system baselining, and recursive self-improvement has been deeply fragmented, resulting in environments highly susceptible to technical debt, runtime failure, and adversarial exploitation. The MIRR (Mechanisms for Intelligent Runtime Reasoning) Arsenal roadmap was originally conceptualized as an eight-phase strategic lifecycle designed to transition systems from basic operational telemetry to fully autonomous, verifiable artificial general intelligence (AGI) integration. However, empirical application of the legacy roadmap has exposed critical vulnerabilities across its entire spectrum of deployment: theoretical bottlenecks in recursive self-improvement, excessive reliance on heuristic safety guardrails, a profound lack of machine-checkable proofs at runtime, and a fundamental disconnect between the semantic intent of large language models (LLMs) and the deterministic realities of compiler toolchains.
To achieve deployment viability in environments ranging from edge-constrained TinyML architectures to massive, distributed data lakehouses, the MIRR Arsenal roadmap requires a fundamental, ground-up restructuring. This comprehensive research report delivers an exhaustive critical evaluation of the existing 8-phase framework, identifying its foundational flaws in both mathematical rigor and practical achievability. Subsequently, it formulates a newly upgraded, structurally sound 8-Phase MIRR Arsenal Roadmap. This upgraded paradigm synthesizes the Model Context Protocol (MCP) for semantic AI integration, Proof-Carrying Code (PCC) for autonomous action authorization, deductive verification toolchains such as Creusot and Rocq, and empirically bounded open-ended evolution via the Darwin Gödel Machine (DGM) architecture. The resulting synthesis provides a definitive, end-to-end blueprint for architecting provably safe, recursively self-improving AI systems that maintain verifiable integrity from the highest levels of distributed consensus down to the bare-metal execution of hardware bootloaders.
Architectural Vulnerabilities in the Legacy MIRR Arsenal Framework
An objective analysis of the existing MIRR Arsenal roadmap reveals significant structural deficiencies that compromise both its mathematical rigor and its practical achievability in production environments. The legacy framework operated under a sequential progression that misaligned software maintenance metrics with AI capability scaling, ultimately failing to account for the complex realities of modern software engineering.
The first critical failure of the legacy roadmap lies in its superficial approach to system baselining. It historically treated technical debt and user engagement as completely isolated operational silos. By failing to integrate short-term and long-term retention metrics with system-based technical debt indicators—such as cyclomatic complexity, code churn, and refactor-to-feature ratios—the legacy framework lacked a unified telemetry matrix capable of identifying when an AI agent's codebase modifications degraded long-term system health.1 Technical debt, originally defined by Ward Cunningham in 1992 as the future cost of implementing expedient but suboptimal solutions, accrues an operational "interest" that rapidly degrades an autonomous agent's ability to navigate and modify the codebase.3 Because the legacy framework did not mathematically correlate this debt with user churn, it allowed AI agents to optimize for short-term feature delivery at the expense of terminal systemic collapse.
The second major flaw is the widely documented "Semantic Gap." The legacy framework relied almost exclusively on raw, text-based integrations for AI coding assistants. Agents were forced to process code purely as lexical strings rather than structured programmatic logic, leading to severe hallucination of API versions, incorrect type inferences, and the generation of invalid syntax.5 Without direct access to the Language Server Protocol (LSP) or compiler-level diagnostics, the AI's proposed modifications lacked structural awareness, severely limiting the achievability of automated code generation and forcing human reviewers to constantly intervene.
Furthermore, the legacy roadmap treated software safety through the lens of heuristic linting and dynamic testing, effectively ignoring the stringent requirements of safety-critical systems. It failed to mandate deterministic programming constraints—such as those pioneered by the NASA Power of 10 rules—and did not enforce the use of memory-safe languages backed by deductive verification.7 Testing, by its very mathematical nature, can only prove the presence of bugs under specific simulated conditions; it can never prove their absolute absence.8 This reliance on dynamic analysis left systems vulnerable to edge-case failures, race conditions, and memory corruption, which are catastrophic in autonomous cyber-physical systems.
The most profound theoretical bottleneck in the legacy roadmap occurs in its approach to recursive self-improvement (RSI). Drawing too heavily on Jürgen Schmidhuber's theoretical Gödel Machine from 2003, the legacy roadmap mandated that any self-modifying AI must mathematically prove that a proposed change to its own architecture would yield a net global improvement before the system was permitted to adopt it.10 While this is an elegant theoretical construct, producing formal proofs of net benefit for non-trivial code modifications is mathematically undecidable in practice.10 This strict, uncompromising requirement rendered the self-improvement phase entirely unachievable, paralyzing the system in a state of continuous proof-search failure and preventing any meaningful evolutionary progress.12
Finally, the legacy roadmap treated formal verification as a monolithic, post-hoc auditing phase rather than an intrinsic, proof-carrying runtime mechanism. The lack of Proof-Carrying Code (PCC) meant that host systems had to blindly trust the outputs of AI agents, creating an unacceptable attack surface for adversarial vulnerabilities and objective misalignment.13 Combined with a lack of hardware-software co-design and a reliance on static, quickly outdated documentation, the legacy roadmap simply could not support the rapid, safe scaling of machine intelligence. The following sections define the upgraded 8-phase roadmap that resolves these critical vulnerabilities.
Phase 1: High-Fidelity Telemetry and D1-D7 Technical Debt Baselining
The foundation of any self-modifying software ecosystem requires a unified metric matrix that correlates the behavioral retention of the system's users (or the utilization rate of internal AI agents) with the underlying structural integrity of the codebase. The upgraded Phase 1 establishes the "D1-D7 Telemetry Matrix," effectively fusing product engagement metrics with software engineering debt indicators.2 Before an AI agent is permitted to autonomously modify a system, the system itself must be instrumented to measure the true cost of those modifications.
In highly dynamic digital platforms, operational health is traditionally measured across specific retention cohorts, primarily Days 1, 7, and 30 (D1/D7/D30). D1 retention reflects the immediate quality of onboarding or the initial success of a newly deployed feature; D7 retention indicates early habit formation, value delivery, and system stability; and D30 retention represents long-term product-market fit and sustained utility.16 Research indicates that consumer software applications frequently lose up to 77 percent of their user base within the first three days if performance is suboptimal, making D1 retention a highly sensitive tripwire for software quality.16 Top-tier venture capital models, such as those articulated by Andrew Chen, mandate that a highly functional system should exhibit a D1/D7/D30 retention profile that strictly exceeds 60/30/15 percent, alongside a Daily Active User to Monthly Active User (DAU/MAU) ratio greater than 50 percent.18
Conversely, technical debt manifests physically within the software architecture as poor computational performance—such as low frame rates and high load times—as well as architectural instability, frequent crashes, and an escalating difficulty in implementing new features.1 Because modern software is highly performance-intensive, technical debt has a direct, immediately visible impact on the user experience, leading inexorably to low retention and significantly increased LiveOps and maintenance costs.1
The upgraded Phase 1 mandates that every automated code commit proposed by an AI agent must be continuously evaluated against a combined matrix of these metrics. If an AI agent introduces code that increases cyclomatic complexity or build latency, this acts as a leading indicator for an inevitable future drop in D7 retention due to system instability.2
Metric Classification
Specific Indicator
Primary Measurement Target
Implication for Autonomous AI Agents
Structural Health
Cyclomatic Complexity Trend
Code branching and logical density
High complexity increases agent hallucination rates during code generation; limits context window efficacy.
Structural Health
Code Churn & File Volatility
Frequency of modifications per file
Highly volatile files require strict formal verification prior to agent modification to prevent regression.
Operational Health
Build & Test Latency
Time required to compile and execute test suites
Degradation indicates the agent is introducing suboptimal, unoptimized algorithms requiring refactoring.
Engagement
D1 Retention
Immediate success of new agent logic
Measures if the agent's recent patch resolved the user's immediate blocking issue without introducing friction.
Engagement
D7 / D30 Retention
Sustained systemic value
Measures if the agent's code introduced hidden memory leaks or subtle bugs that drove users away over time.
Financial Efficiency
CAC vs. ARPPU Ratio
Cost of Acquisition vs. Average Revenue Per Paying User
Ensures the technical debt introduced by the agent does not inflate operational burn rate beyond profitability.

By treating technical debt repayment as a scheduled, first-class necessity alongside feature generation—allocating an uncompromising 15 to 20 percent of every operational cycle strictly to refactoring—Phase 1 ensures that the environment remains pristine enough for an AI agent to analyze it accurately.1 Debt is only deemed acceptable when it is intentional, strictly time-boxed, and closely monitored; it becomes an existential drag when it slows future changes, creates recurring fire drills, or renders the codebase incomprehensible to new agents.19
Phase 2: Semantic Agent Integration via the Model Context Protocol (MCP)
With a clean, rigorously measurable baseline established in Phase 1, Phase 2 connects the AI agents to the codebase. To permanently overcome the Semantic Gap identified in the legacy critique, this phase mandates the mandatory implementation of the Model Context Protocol (MCP). Introduced as a revolutionary open standard by Anthropic in late 2024, MCP acts as a universal bridge—frequently described as the "USB-C of AI"—allowing Large Language Models to securely query external data sources, utilize complex tools, and parse highly structured environments.5
Historically, passing vast amounts of codebase context to an AI agent required loading thousands of lines of code and extensive tool definitions directly into the model's context window. This brute-force approach resulted in two severe operational penalties: tool definitions overloaded the context window, causing the model to "forget" the primary objective, and intermediate tool results consumed an exorbitant number of tokens, inflating computational costs and latency.20 Furthermore, because the AI was analyzing the codebase purely as static text, it lacked the capacity to verify if a function call was valid or if a specific data type was implemented correctly.5
The upgraded roadmap entirely deprecates text-based context passing, leveraging MCP to connect AI agents directly to the Language Server Protocol (LSP) of the host development environment. Tools such as mcpls, a universal MCP-to-LSP bridge engineered entirely in Rust, provide agents with compiler-level code intelligence.22 Operating as a highly efficient, single asynchronous binary of approximately 2 megabytes with zero unsafe blocks and no Node.js or Python runtime dependencies, mcpls allows the agent to execute native LSP commands concurrently.22 Similarly, alternative bridge implementations like the lsp-mcp-server written in Zig provide high-performance, cross-platform capabilities for ecosystems utilizing Homebrew, NixOS, or Docker containers.24
By implementing this MCP-to-LSP architecture, the autonomous agent transitions from reading code as "flat text" to querying the compiler's deeply structured Abstract Syntax Tree (AST).
Capability
Legacy Text-Based AI Assistant
MCP-to-LSP Integrated AI Agent
Code Comprehension
Relies on static training data and lexical string matching.
Direct access to real-time type inference and cross-reference analysis.
Context Management
Bloats context window with raw file dumps and documentation.
Queries specific semantic nodes, conserving tokens and maximizing relevant context.
Error Detection
Guesses syntax correctness; hallucinates API versions.
Retrieves real-time compiler diagnostics and trait implementation guarantees.
Refactoring Scope
Limited to local, single-file text replacement.
Capable of executing precise, project-wide semantic rename_symbol operations.
Tool Overload
Suffers performance degradation as tool counts increase.
Efficiently streams intermediate results without context window exhaustion.

When the agent proposes a modification under this new phase, it can dynamically invoke commands such as get_hover or get_references against the Rust Analyzer or Clangd to definitively verify type constraints before finalizing its output.6 This deterministic, bidirectional semantic bridge drastically reduces hallucinated function calls and ensures that the agent's contextual understanding is perfectly and continuously synchronized with the codebase's actual compilation state.5
Phase 3: NASA Power of 10 Compliance and Deductive Rust Verification
While the Model Context Protocol allows the agent to write syntactically valid code that compiles successfully, Phase 3 introduces stringent deterministic constraints and deductive verification to ensure that the code is functionally correct, predictable, and unequivocally free of undefined behavior. The upgraded roadmap explicitly targets languages with strong affine or linear type systems—specifically Rust—as the mandatory baseline for agentic code generation in safety-critical systems, while enforcing principles derived from aerospace engineering.
To establish an uncompromising standard of software reliability, Phase 3 integrates the philosophical foundations of the NASA Jet Propulsion Laboratory's "Power of 10" coding rules. Formulated in 2006 for safety-critical spacecraft systems, these rules were designed to remove complex C constructs that are notoriously difficult for both humans and static analysis tools to verify.7 The legacy application of these rules in C required immense manual oversight and restrictive linting. However, by transitioning the architecture to Rust, many of these rules are enforced natively by the compiler, while the remaining logical constraints are managed through advanced deductive verifiers.
NASA Power of 10 Rule
Original Purpose in C Architecture
Enforcement Mechanism in the Upgraded Rust Architecture
Avoid complex control flow
Prevent runaway code; ensure static analyzers can trace execution paths.
Natively enforced. Rust lacks goto, setjmp, and longjmp. Recursion is strictly bounded by verifiers.
Fixed upper bounds for loops
Prevent infinite loops leading to system lockup or resource exhaustion.
Enforced via deductive verification tools proving loop termination and variant strictly decreasing.
No dynamic memory after init
Prevent heap fragmentation, memory leaks, and unpredictable allocation times.
Rust's ownership and borrow checker natively guarantees memory safety; #![no_std] enforces no-heap constraints.
Functions fit on one page
Reduce cognitive load and allow comprehensive visual auditing.
Enforced via strict CI/CD linting rules and cyclomatic complexity limits tracked in Phase 1.
Minimum two assertions per func
Catch anomalies early at runtime before they propagate to critical systems.
Replaced by compile-time formal specifications; pre- and post-conditions mathematically proven prior to runtime.
Minimal scope for data
Prevent unintended data mutation and state corruption.
Natively enforced by Rust's strict block scoping and default immutability of variables.
Check all return values
Ensure hardware errors or function failures are explicitly handled.
Natively enforced by the Result and Option types, requiring explicit match or unwrap handling.
Limit preprocessor usage
Prevent obfuscated code and unpredictable macro expansions.
Rust macros are hygienic and operate on the AST, eliminating the textual substitution flaws of the C preprocessor.
Limit pointer usage
Prevent null pointer dereferences and complex call graph obfuscation.
Natively enforced. Rust eliminates raw pointers in safe code; function pointers are strictly typed.
Compile with all warnings
Catch syntax anomalies and undefined behavior prior to deployment.
Enforced by rustc pedantic warnings and Clippy; zero-warning tolerance mandated in the CI/CD pipeline.

While Rust's borrow checker provides exceptional guarantees regarding memory safety and data races, it does not mathematically prove algorithmic correctness, nor does it guarantee the absence of logic errors—especially when interacting with necessary unsafe blocks.26 To elevate the system to industrial-strength, machine-checked guarantees, Phase 3 integrates cutting-edge deductive verifiers, most notably Creusot.
Creusot is a deductive verifier designed specifically for Rust that translates Rust code into Coma, an intermediate verification language utilized by the Why3 platform.9 This integration allows both human engineers and AI agents to annotate Rust code with "Pearlite" contracts—precise preconditions, postconditions, loop invariants, and lemmas.26 Unlike older verification tools such as Prusti, which specify mutable borrows using restrictive "pledges," Creusot utilizes a paradigm based on "prophecies," enabling it to support a vastly wider and more complex array of borrowing patterns natively found in idiomatic Rust.26
During Phase 3, AI agents are tasked not only with generating the functional code via MCP but also with generating the corresponding Pearlite formal specifications.27 The Why3 platform then leverages a suite of SMT (Satisfiability Modulo Theories) solvers—such as Z3, Alt-Ergo, and CVC5—to automatically discharge these verification conditions.29 This provides an ironclad mathematical proof that the generated logic holds true for absolutely all possible execution paths.27 The empirical viability of this approach has been definitively proven by artifacts like CreuSAT, a formally verified SAT solver written in Rust. CreuSAT utilizes Creusot to maintain competitive computational performance—implementing complex optimizations like the two watched literals scheme and phase saving—while ensuring absolute functional correctness with a remarkably low proof-overhead ratio of merely three lines of proof code per line of programmatic code.30
Phase 4: Autonomous Action Authorization through Proof-Carrying Code (PCC)
Phase 4 operationalizes the deductive verification foundations established in Phase 3 to solve the fundamental problem of AI trust and governance: how can a host system safely execute code or operational trajectories generated by an untrusted, highly complex, and potentially misaligned neural network? The upgraded roadmap solves this by implementing Proof-Carrying Code (PCC), an elegant architectural mechanism originally conceptualized by George Necula and Peter Lee in 1996.13
Under the traditional security paradigm, the host system bears the computational and architectural burden of ensuring that an incoming program is safe to execute, typically through expensive runtime monitoring, sandboxing, or heuristic anomaly detection.14 Under the PCC paradigm, the burden of proving safety is shifted entirely to the untrusted producer—in this case, the autonomous AI agent. When the agent generates a code completion or an action trajectory, it must simultaneously emit a machine-checkable proof demonstrating that the code strictly adheres to a predefined, rigorous safety policy.13
The host system relies on a remarkably small, highly audited, and deterministic proof checker to validate the certificate before any execution is permitted.14 If the mathematical proof of safety is invalid, mathematically incomplete, or entirely absent, the host system immediately rejects the code without requiring sandbox interpretation or exhaustive runtime monitoring.14 This architecture shrinks the Trusted Computing Base (TCB) to just the verification compiler itself, ensuring that even if the AI agent hallucinates, experiences objective drift, or is subjected to an adversarial prompt injection, its resulting actions are mathematically constrained to safe, predefined boundaries.14
The implementation of Proof-Carrying Code Completions (PC^3) represents a monumental breakthrough in this phase. Frameworks like PC^3, prototyped in the program verification language Dafny, force the LLM to prove automatically generated preconditions for any dangerous function calls.35 For example, when an AI agent attempts to manipulate the file system or write to a database, the PC^3 framework statically generates verification conditions designed to prevent path traversal attacks (CWE-35) or buffer overflows.35 The LLM must formally satisfy these constraints within the Dafny proof environment before the completion is accepted by the host.35 Empirical testing of this methodology demonstrates that an LLM can generate provably safe code avoiding CWE-35 using a single generation attempt (k = 1) while consuming a highly efficient 3,350 tokens.35
This Proof-Carrying architecture is equally applicable beyond raw codebase modifications, extending directly into operational infrastructure such as the Agentic Lakehouse. In modern distributed data platforms like Bauplan, untrusted AI agents are increasingly utilized to autonomously repair data pipelines.33 By utilizing PCC-inspired verifiers, the agent operates in an isolated data branch, proposing patches and executing pipeline re-runs.36 The agent is only permitted to merge its repairs into the production branch after all deterministic correctness properties—expressed as formal verifiers—return a structurally valid proof.33 This guarantees correctness-by-construction, allowing organizations to deploy autonomous agents on highly sensitive production data without expanding the attack surface.
Phase 5: Empirically Bounded Recursive Self-Improvement via Darwin Gödel Machines
With a rigorously verified, proof-carrying pipeline firmly established, the roadmap safely unlocks Phase 5: Recursive Self-Improvement (RSI). RSI is the theoretical mechanism by which an AI system autonomously enhances its own cognitive architecture, learning algorithms, or codebase, creating an accelerating feedback loop of exponentially growing capabilities.37 However, achieving true RSI has historically been the most significant roadblock in artificial intelligence research.
To bypass the undecidability trap of the classical Gödel Machine—which theoretically required the AI to generate a flawless, mathematically impossible proof of global utility for every self-rewrite—the upgraded roadmap adopts the Darwin Gödel Machine (DGM) architecture pioneered by researchers at Sakana AI and the University of British Columbia.10 The DGM represents a critical paradigm shift from paralyzing formal proof requirements to an open-ended, empirical evolutionary process bounded by rigorous safety parameters.11
Within the DGM framework, the AI agent iteratively modifies its own source code, testing new algorithmic variations against a rigorous suite of independent coding benchmarks—such as SWE-bench and Polyglot—to establish empirical fitness rather than theoretical perfection.10 Operating under the principles of Darwinian open-ended evolution, the system maintains a continuously growing archive of successful agent variations, recombining and mutating them to explore the architectural search space in parallel.11
From a thermodynamic and information-theoretic perspective, this recursive self-improvement mirrors the evolution of complex physical systems. Information within the DGM is not merely static data; it is a dynamic property that reorganizes the agent to process computational energy more efficiently, reducing uncertainty and maximizing entropy production through optimized algorithmic pathways.41 By parallelizing the search for self-improvement, the DGM creates a phase transition from isolated, linear processing to a distributed, rapidly accelerating intelligence network.41
Architectural Mechanism
Classical Gödel Machine (Schmidhuber, 2003)
Darwin Gödel Machine (Sakana AI, 2025)
Authorization Requirement
Mathematical proof of global net utility.
Empirical validation against rigorous coding benchmarks.
Computational Feasibility
Mathematically undecidable for non-trivial code.
Highly practical using modern LLM foundation models.
Evolutionary Trajectory
Linear, sequential rewrites upon proof discovery.
Open-ended, parallel tree of diverse, high-quality agents.
Validation Feedback Loop
Internal theorem prover and logic axioms.
External execution on SWE-bench, Polyglot, and unit tests.
Architecture Transferability
Bound strictly to the initial axiomatic framework.
Self-discovered improvements transfer across Rust, C++, and Go.

The empirical success of the Darwin Gödel Machine is profound. Baseline experiments demonstrate that the DGM automatically discovers increasingly superior coding agents, resulting in autonomous performance increases on SWE-bench from 20.0 percent to 50.0 percent, and on the Polyglot benchmark from 14.2 percent to 30.7 percent.11 Most importantly, the architectural features discovered by the DGM exhibit remarkable transferability; an agent whose self-improvement process was guided exclusively by its performance on Python tasks subsequently demonstrated significant performance gains when tasked with modifying entirely different programming languages, such as Rust, C++, and Go.11
Because the agent's modifications to its own architecture are routed back through the Proof-Carrying Code protocols established in Phase 4, the system is permanently prevented from introducing memory safety violations or breaking core invariants during its evolutionary mutations.13 By blending empirical open-ended evolution with the rigid safety guardrails of PCC, Phase 5 achieves sustainable, accelerating RSI without risking system integrity or architectural collapse.43
Phase 6: Hardware-Software Formal Co-Design and Verified Compilers
A software-level proof of correctness—no matter how mathematically rigorous—is rendered entirely obsolete if the underlying compiler, operating system, or hardware platform introduces undefined behavior during execution. Phase 6 enforces strict Hardware-Software Formal Co-Design, extending the unbroken chain of trust from the AI agent's highest-level Python or Rust outputs down directly to the bare-metal silicon.45
This phase mitigates compiler-introduced vulnerabilities by integrating formally verified compilers such as CompCert. Compilers are highly complex systems that perform complicated symbolic transformations, and traditional optimizing compilers often silently generate incorrect machine code from correct source programs.47 CompCert guarantees semantic preservation by utilizing the Coq (now Rocq) proof assistant to mathematically prove that the compiled PowerPC or ARM assembly code behaves exactly as prescribed by the formally verified C source program.47 This guarantees that the safety properties established in Phase 3 hold true at the executable level.
For operating systems and bare-metal boot sequences, the roadmap mandates the use of proof assistants like Rocq or Isabelle/HOL. Artifacts such as the seL4 high-assurance microkernel and Rocq-verified RISC-V bootloaders (such as the DICE* implementation) provide formally verified, side-channel-resistant trust anchors.48 Historically, boot verification proofs were brittle; changing a hardware component or cryptographic primitive required massive re-verification efforts. The upgraded roadmap addresses this by decoupling secure-boot reasoning into reusable, parametric algebraic modules within Rocq, ensuring that the verification holds seamlessly across hardware revisions (e.g., swapping NAND for NOR flash) and cryptographic substitutions (e.g., upgrading from ECDSA to Ed25519).49
Furthermore, as AI models are increasingly deployed to highly constrained edge devices, this phase incorporates TinyML security constraints.45 Because TinyML environments face severe memory, bandwidth, and power limitations, formal co-design is utilized to mathematically bound the memory consumption and execution time of the deployed models.50 Methodologies drawn from advanced hardware architecture analysis—such as evaluating designs against domain-native compilers and formal proof checkers—ensure that safety-critical cyber-physical systems remain impervious to both software hallucinations and physical side-channel perturbations, such as TimeTravel acoustic resonance attacks that manipulate Real-Time Clock (RTC) circuits.52
Phase 7: Decentralized Consensus and Guaranteed Safe AI Governance
While previous phases address the structural and logical correctness of individual agents and their immediate hardware environments, Phase 7 establishes a systemic framework for Runtime AI Governance across a distributed network of autonomous entities. As multi-agent systems interact, the potential for complex, emergent failure modes increases exponentially, requiring mechanisms that enforce alignment dynamically without centralizing points of failure.36
The roadmap implements a decentralized validation paradigm utilizing cryptographic proof objects and blockchain-based reasoning traces.54 When a recursively self-improving agent derives a complex action plan, algorithm, or architectural modification, the logical execution trace of that decision is computationally encoded into a Proof Object (PO). Before this action is authorized for execution in the broader network, the PO undergoes a strict multi-agent consensus protocol.54 Independent, orthogonal AI agents—operating under strictly separate system prompts, architectures, and LLM foundations—re-run the verification checker against the PO. If a mathematically defined quorum (e.g.,  out of  validators) confirms the validity, consistency, and alignment of the proof, the action is immutably appended to the network ledger and authorized for execution.54
This dynamic authorization architecture operates symbiotically with the "Guaranteed Safe AI" framework, proposed by Dalrymple et al. in 2024, which relies on comprehensive world models, probabilistic safety specifications, and Lean 4 verifiers to establish quantitative guarantees of societal safety.56 By defining safety specifications in probabilistic logic at the top level and utilizing Lean 4 to output machine-checkable evidence, the system bridges the specification gap that plagues traditional heuristic safety cases.56 By mandating that high-stakes decisions carry their own proof and are cross-examined by independent, formally verified network peers, Phase 7 prevents localized agent misalignment or individual model hallucination from cascading into systemic catastrophe, providing a highly robust governance layer resilient to adversarial exploitation.51
Phase 8: The Continuous Knowledge Artifact and Living Paper Ecosystem
The final phase of the upgraded MIRR Arsenal Roadmap fundamentally reconceptualizes how architectural knowledge, system specifications, and verification proofs are documented, audited, and disseminated. Traditional static documentation—such as PDF manuals or disconnected wiki pages—becomes instantly obsolete in a recursively self-improving ecosystem where agents autonomously modify their own codebases hundreds of times per hour. Phase 8 mandates the transition to the "Living Paper" paradigm, instantiated through dynamic frameworks like MindStream and meta-modeling environments such as ADOxx.60
A Living Paper is an interactive, continuously evolving scholarly and technical artifact that is simultaneously co-created by human architects, AI agents, and formal verification engines.60 Unlike static media, these artifacts structurally embed the actual executable specifications, the formal proofs generated in Dafny or Creusot, and the underlying data schemas directly into the documentation narrative. When an AI agent operating in Phase 5 modifies its own architecture, the corresponding Living Paper is automatically re-compiled by the system; the embedded mathematical proofs are re-verified by the SMT solvers in real-time, and the textual explanations are dynamically rewritten by the LLM to reflect the exact new state of the system.60
This continuous artifact generation bridges the persistent gap between digital modeling, human comprehension, and tangible media.62 It leverages augmented technologies to create immersive, easily navigable representations of highly complex logical structures, ensuring that as the system evolves rapidly toward superintelligence, its inner workings remain fully observable, auditable, and comprehensible to human overseers.60 In this paradigm, the documentation itself becomes a formally verified mechanism, effectively eliminating the "specification gap" where human intent diverges from machine interpretation, and establishing a permanent, transparent record of the AI's evolutionary trajectory.
Strategic Implementation Directives and Future Outlook
Deploying the upgraded 8-phase MIRR Arsenal roadmap within an enterprise, defense contractor, or deep-tech research institution requires a deliberate, meticulously phased execution strategy to mitigate disruption and manage infrastructural transition costs.
Organizations must first instrument their development pipelines to capture the D1-D7 telemetry metrics continuously.2 AI assistants must not be granted autonomous commit rights until their direct impact on code churn, cyclomatic complexity, and build latency can be empirically correlated with downstream user retention and financial burn rates.4 Following this, the transition from isolated API plugins to the Model Context Protocol is non-negotiable. Engineering teams must deploy mcpls or lsp-mcp-server binaries across all development environments to provide their AI agents with real-time, low-latency access to the compiler's semantic data.22
Transitioning an entire legacy codebase to a verified language like Rust with complete Creusot annotations is often economically unfeasible in the short term. Therefore, organizations must identify their most safety-critical modules—forming the Trusted Computing Base—and isolate them. Proof-Carrying Code requirements should be enforced strictly at the boundaries of these isolated modules, allowing untrusted legacy code and rapidly generated AI code to interact with the core only if they provide mathematically valid safety certificates.14 When initiating the Darwin Gödel Machine for recursive self-improvement, the evolutionary archive must remain strictly sandboxed.43 The agent's ability to modify its own code must be restricted to isolated virtual environments where its empirical fitness can be assessed without any risk to production infrastructure.11
The integration of Hardware-Software Co-Design with Distributed Consensus creates a highly disruptive paradigm for high-assurance supply chains. Because the entire computational stack—from the AGI's generated action trajectory down to the RISC-V bootloader—is accompanied by machine-checkable cryptographic certificates, organizations can achieve true "compliance-by-default." Regulatory audits will no longer require extensive, narrative-driven manual review of corporate processes; instead, human auditors will simply execute a deterministic verification algorithm against the system's published proof artifacts.34 This fundamentally alters the economics of regulatory compliance in safety-critical industries such as avionics, nuclear control, and autonomous biomedicine.64
Conclusion
The legacy 8-phase MIRR Arsenal Roadmap, heavily burdened by theoretical undecidability, semantic detachment from compiler realities, and a dangerous reliance on post-hoc dynamic testing, is fundamentally unsuited for the era of autonomous, self-modifying artificial intelligence. The upgraded roadmap formulated in this exhaustive report re-architects the entire paradigm around the uncompromising principles of mathematical certainty and empirical open-ended evolution.
By establishing a holistic baseline through D1-D7 telemetry, the system accurately monitors the true systemic cost of algorithmic modifications. By bridging the semantic gap via the Model Context Protocol, AI agents are granted precise, compiler-level awareness of the environment they are tasked to modify. Through the strict enforcement of deductive verification and Proof-Carrying Code, the roadmap shifts the burden of trust entirely away from fragile human heuristics and onto immutable mathematics, ensuring that every autonomous action is accompanied by a machine-checkable certificate of safety. Furthermore, by transitioning the mechanisms of recursive self-improvement from the theoretical paralysis of the classical Gödel Machine to the empirical viability of the Darwin Gödel Machine, the roadmap provides a sustainable, mathematically bounded pathway for intelligence amplification.
When anchored by Hardware-Software Formal Co-Design and documented continuously via the transparent Living Paper framework, the upgraded MIRR Arsenal Roadmap ceases to be a mere theoretical proposal. It represents an actionable, highly rigorous, and profoundly achievable architecture for deploying artificial general intelligence that is provably safe, continuously self-optimizing, and fundamentally aligned with the physical and logical constraints of human society.
Works cited
Game Development Best Practices: Concept to LiveOps Success, accessed March 27, 2026, https://www.cisin.com/coffee-break/game-development-best-practices.html
Technical debt ratio: How to measure technical debt - DX, accessed March 27, 2026, https://getdx.com/blog/technical-debt-ratio/
Technical Debt (Tech Debt): A Complete Guide - Confluent, accessed March 27, 2026, https://www.confluent.io/learn/tech-debt/
How to Measure Technical Debt: Step by Step Guide - vFunction, accessed March 27, 2026, https://vfunction.com/blog/how-to-measure-technical-debt/
Unlocking AI-Powered Rust Development: A Deep Dive into the Rust Analyzer Tools MCP Server - Skywork.ai, accessed March 27, 2026, https://skywork.ai/skypage/en/ai-rust-development-analyzer-tools/1980879842347511808
sehejjain/Language-Server-MCP-Bridge: A universal VS Code extension that bridges any Language Server Protocol (LSP) capabilities to MCP tools and GitHub Copilot Language Model Tools, enabling intelligent code navigation and analysis across all programming languages., accessed March 27, 2026, https://github.com/sehejjain/Language-Server-MCP-Bridge
NASA's 10 Coding Rules Explained: How to Build Reliable and Safe Software, accessed March 27, 2026, https://www.aikido.dev/code-quality/rules/nasa-10-coding-rules-for-safety-critical-code
Formal Verification: Ensuring Memory Safety in Embedded Systems - TrustInSoft, accessed March 27, 2026, https://www.trust-in-soft.com/resources/blogs/securing-the-future-formal-verification-and-the-evolving-landscape-of-embedded-systems
Creusot — Rust implementation // Lib.rs, accessed March 27, 2026, https://lib.rs/gh/xldenis/creusot/creusot
The Stable-Kernel Thesis: Self-Improving AI Without Self-Destruction | by James Lee Stakelum | Mar, 2026 | Medium, accessed March 27, 2026, https://medium.com/@JamesStakelum/the-stable-kernel-thesis-self-improving-ai-without-self-destruction-53692d421227
The Darwin Gödel Machine: AI that improves itself by rewriting its own code - Sakana AI, accessed March 27, 2026, https://sakana.ai/dgm/
Gödel Machine - Serious Science, accessed March 27, 2026, https://serious-science.org/godel-machine-10426
Proof-carrying code [for Safe AI] was originally described in 1996 by George Necula and Peter Lee. - blog.biocomm.ai, accessed March 27, 2026, https://blog.biocomm.ai/2024/07/20/proof-carrying-code-for-safe-ai-was-originally-described-in-1996-by-george-necula-and-peter-lee/
Safe, Untrusted, “Proof-Carrying” AI Agents: Toward the Agentic Lakehouse - ResearchGate, accessed March 27, 2026, https://www.researchgate.net/publication/401683259_Safe_Untrusted_Proof-Carrying_AI_Agents_Toward_the_Agentic_Lakehouse
UX Toolbox for Software Developers Methods and Training Pedersen, Tina Øvad - Aalborg Universitets forskningsportal, accessed March 27, 2026, https://vbn.aau.dk/ws/portalfiles/portal/549532303/PHD_Tina_Oevad_Pedersen_E_pdf_1_.pdf
Understanding DAU, WAU & MAU active users metrics - Adapty, accessed March 27, 2026, https://adapty.io/blog/dau-wau-mau-active-users/
Increase app retention 2026: Benchmarks, strategies & examples - Pushwoosh, accessed March 27, 2026, https://www.pushwoosh.com/blog/increase-user-retention-rate/
How to Raise a Series A: Series A Guide | Founder Playlist - Pillar VC, accessed March 27, 2026, https://www.pillar.vc/playlist/article/full-guide/
How Silicon Valley Startup Culture Works: Speed vs Perfection - Koder.ai, accessed March 27, 2026, https://koder.ai/blog/how-silicon-valley-startup-culture-works-speed-vs-perfection
Code execution with MCP: building more efficient AI agents - Anthropic, accessed March 27, 2026, https://www.anthropic.com/engineering/code-execution-with-mcp
Model Context Protocol: The new AI connection standard - Contentful, accessed March 27, 2026, https://www.contentful.com/blog/model-context-protocol-introduction/
mcpls: Universal MCP↔LSP bridge in Rust — give AI agents compiler-level code intelligence - Reddit, accessed March 27, 2026, https://www.reddit.com/r/rust/comments/1q3zif1/mcpls_universal_mcplsp_bridge_in_rust_give_ai/
Language Server Protocol (LSP) for AI Coding Agents - Hacker News, accessed March 27, 2026, https://news.ycombinator.com/item?id=46490938
Zig program: nzrsky/lsp-mcp-server from GitHub | Branch: main - Zigistry, accessed March 27, 2026, https://zigistry.dev/programs/github/nzrsky/lsp-mcp-server
mcpls: Universal MCP↔LSP bridge in Rust — give AI agents compiler-level code intelligence - Reddit, accessed March 27, 2026, https://www.reddit.com/r/mcp/comments/1q3zjd9/mcpls_universal_mcplsp_bridge_in_rust_give_ai/
Surveying the Rust Verification Landscape - arXiv, accessed March 27, 2026, https://arxiv.org/html/2410.01981v1
The Rust Developer's Toolbox: Best Static Code Analysis Tools - IN-COM Data Systems, Inc, accessed March 27, 2026, https://www.in-com.com/blog/the-rust-developers-toolbox-best-static-code-analysis-tools/
Creusot helps you prove your code is correct in an automated fashion. - GitHub, accessed March 27, 2026, https://github.com/creusot-rs/creusot
creusot/CHANGELOG.md at master - GitHub, accessed March 27, 2026, https://github.com/creusot-rs/creusot/blob/master/CHANGELOG.md
CreuSAT - Sarek Skotåm, accessed March 27, 2026, https://sarsko.github.io/_pages/SarekSkot%C3%A5m_thesis.pdf
Scenarios for Proof-Carrying Code - The IMDEA Software Institute, accessed March 27, 2026, https://software.imdea.org/~gbarthe/mobius/bin/view/DeliverablesList/D4.1.pdf
POST-AGI SYSTEMS NEED A “TRUTH STACK,” NOT JUST BETTER MODELS - OpenReview, accessed March 27, 2026, https://openreview.net/pdf/8380ac1243d92c62ac5096f33857a7c7b81c85ff.pdf
Safe, Untrusted, “Proof-Carrying” AI Agents: toward the agentic lakehouse Thanks to [1] for coming up with a great title (a long time ago, for a different type of agents). - arXiv, accessed March 27, 2026, https://arxiv.org/html/2510.09567v1
Cybersecurity Under Change: Proof-Carrying Assurance via Frozen Records and a One-Residual, One-Clock Certificate - Preprints.org, accessed March 27, 2026, https://www.preprints.org/manuscript/202601.0050
Vision Paper: Proof-Carrying Code Completions - Computer Science | UC Davis Engineering, accessed March 27, 2026, https://web.cs.ucdavis.edu/~cdstanford/doc/2024/ASEW24b.pdf
Agentic AI-Based Formal Property Generation - Emergent Mind, accessed March 27, 2026, https://www.emergentmind.com/topics/agentic-ai-based-formal-property-generation
How Anthropic Became the Most Disruptive Company in the World - TIME, accessed March 27, 2026, https://time.com/article/2026/03/11/anthropic-claude-disruptive-company-pentagon/
Recursive Self-Improvement - Perspectives - claude - follow the idea - Obsidian Publish, accessed March 27, 2026, https://publish.obsidian.md/followtheidea/Recursive+Self-Improvement+-+Perspectives+-+claude
Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents | OpenReview, accessed March 27, 2026, https://openreview.net/forum?id=pUpzQZTvGY
Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents - arXiv, accessed March 27, 2026, https://arxiv.org/html/2505.22954v2
The Universe's Algorithm: How Information Became Self-Aware | by Major Jackson | Medium, accessed March 27, 2026, https://medium.com/@shaheim/the-universes-algorithm-how-information-became-self-aware-90041cbb1cc4
Racket: Programmable Programming for Constraint Natural Language of AI Agents, accessed March 27, 2026, https://volodymyrpavlyshyn.medium.com/racket-programmable-programming-for-constraint-natural-language-of-ai-agents-5be7f18019af
Darwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents - arXiv.org, accessed March 27, 2026, https://arxiv.org/html/2505.22954v3
May-June 2025 Progress in Guaranteed Safe AI - LessWrong, accessed March 27, 2026, https://www.lesswrong.com/posts/WknCdhjs7ErzQ6AZt/may-june-2025-progress-in-guaranteed-safe-ai
Ultra-Low-Power Edge Intelligence: Green AI Algorithms and Hardware Co-Design, accessed March 27, 2026, https://www.researchgate.net/publication/401599523_Ultra-Low-Power_Edge_Intelligence_Green_AI_Algorithms_and_Hardware_Co-Design
Beyond Moore's Law: Harnessing the Redshift of Generative AI with Effective Hardware-Software Co-Design - arXiv, accessed March 27, 2026, https://arxiv.org/html/2504.06531v1
Formal verification of a realistic compiler - Xavier Leroy, accessed March 27, 2026, https://xavierleroy.org/publi/compcert-CACM.pdf
Defense Software for a Contested Future: Agility, Assurance, and Incentives (2025), accessed March 27, 2026, https://www.nationalacademies.org/read/29129/chapter/5
Write-Once, Prove-Once: A Reusable Framework for Secure Boot Verification in Rocq, accessed March 27, 2026, https://www.computer.org/csdl/proceedings-article/icpads/2025/11323130/2dgOfPLyoCI
TinyML Security: Attacks, Defenses, and Open Challenges in Resource-Constrained Machine Learning Systems | Request PDF - ResearchGate, accessed March 27, 2026, https://www.researchgate.net/publication/401022918_TinyML_Security_Attacks_Defenses_and_Open_Challenges_in_Resource-Constrained_Machine_Learning_Systems
Safety-Centered Design of Self-Improving Superintelligent Systems Using Formal Verification and Control Theory. - Scholar9, accessed March 27, 2026, https://scholar9.com/publication/IACSE-IJGASIAI_04_01_001_1750684962.pdf
An Alternative Trajectory for Generative AI - arXiv, accessed March 27, 2026, https://arxiv.org/html/2603.14147v1
USENIX Security '25 Cycle 1 Accepted Papers, accessed March 27, 2026, https://www.usenix.org/conference/usenixsecurity25/cycle1-accepted-papers
Proof-of-Reasoning Blockchain: A New Paradigm for AI-Powered Trust Systems - Medium, accessed March 27, 2026, https://medium.com/@drfolkan/proof-of-reasoning-blockchain-a-new-paradigm-for-ai-powered-trust-systems-92f06bfb80bf
How Runtime Authorization Became the Missing Layer in AI ... - FERZ, accessed March 27, 2026, https://ferz.ai/articles/how-runtime-authorization-became-the-missing-layer-in-ai-governance
Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials - arXiv.org, accessed March 27, 2026, https://arxiv.org/html/2603.12183v2
Proof-Carrying Materials: Falsifiable Safety Certificates for Machine-Learned Interatomic Potentials - arXiv, accessed March 27, 2026, https://arxiv.org/html/2603.12183v1
Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems - arXiv, accessed March 27, 2026, https://arxiv.org/html/2405.06624v2
Stop Asking AI Why It Decided — Build Decisions That Carry Their, accessed March 27, 2026, https://leverageai.com.au/stop-asking-ai-why-it-decided-build-decisions-that-carry-their-own-proof/
MindStream: An AI-Era Research Framework - AIS eLibrary, accessed March 27, 2026, https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1013&context=neais2025
Domain-Specific Conceptual Modeling | springerprofessional.de, accessed March 27, 2026, https://www.springerprofessional.de/en/domain-specific-conceptual-modeling/20208082
Living Paper: Authoring AR Narratives Across Digital and Tangible Media - ResearchGate, accessed March 27, 2026, https://www.researchgate.net/publication/341721947_Living_Paper_Authoring_AR_Narratives_Across_Digital_and_Tangible_Media
Formal Verification for AI-Assisted Code Changes in Regulated Environments - Computer Fraud and Security, accessed March 27, 2026, https://computerfraudsecurity.com/index.php/journal/article/download/793/544/1528
PRISM: Proof-Carrying Artifact Generation through LLM × MDE Synergy and Stratified Constraints - arXiv, accessed March 27, 2026, https://arxiv.org/html/2510.25890v1
When code isn't law: rethinking regulation for artificial intelligence | Policy and Society, accessed March 27, 2026, https://academic.oup.com/policyandsociety/article/44/1/85/7684910
