The model is a component. Memory is the continuity.
MSMA is a memory-centered, local-first modular AI runtime designed to connect persistent memory, models, tools, agents, and adaptive runtime mechanisms within a unified architecture.
MSMA does not treat the LLM itself as the entire AI system.
Models are replaceable execution components. Memory preserves continuity across executions. The runtime orchestrates how models, memory, tools, agents, and other components work together.
Model = Replaceable Execution Component
Memory = Continuity
Runtime = Orchestration
Current Development Stage: Pre-MVP
The core runtime architecture has been implemented, with documented long-context evaluation evidence for the Memory System and working E2E demonstrations for key capabilities including the Tool System, Agent Runtime, Self Expansion, and Controlled Self Improvement.
Not every runtime component has been integrated or validated to the same level, and MSMA is not yet a production-ready product.
LLMs are powerful, but building a system that maintains continuity over long periods remains difficult when knowledge and experience depend primarily on the model's current context.
Model Context
↓
Context / Session Limit
↓
Long-Term Continuity Problem
↓
Loss of Past Knowledge / Experience
MSMA approaches this problem by separating responsibilities instead of relying only on larger models or longer context windows.
Model
→ Generation and reasoning
Memory
→ Continuity of knowledge and experience
Runtime
→ Orchestration of execution and component interaction
The center of MSMA is therefore not a particular LLM.
Memory + Runtime Architecture
The MSMA Runtime acts as the orchestration layer that connects and coordinates system components.
MSMA
│
┌────────▼────────┐
│ Runtime │
│ Orchestration │
└────────┬────────┘
│
Intent / Goal
│
Router
│
┌───────────────┼───────────────┐
│ │ │
▼ ▼ ▼
Model Tool System Agent Runtime
│
Capability Runtime
│
Self Expansion
Monitoring and diagnosis are designed to observe runtime execution and connect runtime results to controlled improvement pathways.
Runtime Execution
↓
Monitoring
↓
Diagnosis
↓
Self Improvement
Memory is not treated as just another execution module. It acts as a continuity layer across the runtime.
┌───────────────────────────────────────────────┐
│ Persistent Memory │
│ │
│ Runtime ↔ Execution ↔ Result ↔ Experience │
└───────────────────────────────────────────────┘
Conceptually:
Memory = Continuity Layer
Runtime = Orchestration Layer
Models / Tools / Agents = Execution Resources
Expansion / Improvement = Adaptation Mechanisms
MSMA is not designed to send every request through the same heavy execution path.
A user request is analyzed into intent and mission information, and the router selects an appropriate runtime path.
User Request
↓
Intent / Mission
↓
Router
↓
Appropriate Runtime Path
A request that can be handled by an existing tool does not require a new agent.
If an appropriate agent already exists, the Agent Runtime can resolve and execute it. If the required agent is missing, Self Expansion can be invoked.
Controlled Self Improvement is treated separately from ordinary tool or agent selection and is designed to enter through controlled improvement requests or monitoring/diagnosis pathways.
Memory is the central component of MSMA.
Its purpose is not simply to store conversation history. It is designed to preserve information, state, knowledge, relationships, events, and experience outside the model's immediate context so that they can be reused by later runtime executions.
At a public architectural level, the MSMA Memory System includes the following layered structure:
RAW
↓
STM
↓
MTM
↓
LTM
+ Semantic / Context-aware Memory Processing
+ Persistent Knowledge / Experience Retention
The current Memory System combines hierarchical persistent memory with semantic information organization so that information and experience needed across long-running execution can be reused.
Detailed implementation mechanisms---such as meaning extraction logic, internal scoring formulas, memory promotion/deletion rules, relationship-processing methods, internal memory-structure construction, recall-selection logic, and internal parameters---are intentionally outside the scope of this public README.
The current Memory System was not designed in one step. It evolved through repeated long-context narrative tests and observed failures.
The early architecture used a relatively simple hierarchy:
STM
↓
Summary-based MTM
↓
Compressed LTM
Initial long-context testing showed that this approach could reduce the amount of text supplied to the model while preserving useful historical information.
It also exposed limitations:
- important details could weaken during compression,
- long-range recall was not consistently accurate,
- some questions produced responses that were not adequately grounded in the source narrative.
These failures led to changes in the memory architecture.
MTM was subsequently redesigned toward semantic organization rather than simple summary accumulation.
Retesting showed improved retrieval of relevant information and better preservation of long-range narrative information.
In deliberately unsupported questions---questions about facts that did not exist in the source narrative---the system also produced cases where it declined to invent a fact and instead indicated that the information could not be found in available memory.
This suggests that memory-grounded recall can help suppress unsupported generation.
However, no formal hallucination rate was measured.
MSMA therefore does not make claims such as:
"Hallucination reduced by 90%."
Semantic MTM testing revealed another limitation.
The system could remember an individual fact while failing to reconstruct enough of the contextual connection between:
Person
↕
Event
↕
Action
↕
Result
For example, an internal test showed a case in which the system remembered that a character had suffered an injury but did not correctly associate that injury with the event in which it occurred.
This led to an important design conclusion:
The problem is not only storing memories. It is also preserving context and relationships between memories.
This failure became an important reason for further evolving the Memory System.
The specific storage structures and generation/retrieval mechanisms used for relationship information are not described in this public document.
MSMA's Memory System was evaluated internally using a long-form narrative consisting of 50 web-novel episodes.
The original 50 source files were later measured programmatically:
Episodes : 50
Characters incl. spaces : 194,956
Characters excl. spaces : 144,352
Average excl. spaces : 2,887 / episode
For comparison, this README uses the measured 194,956 characters including spaces.
Three context strategies were compared.
A. Full Context
The complete 50-episode source narrative was supplied directly to a Gemini conversation context, followed by continuation writing and questions about earlier events.
B. Manual Episode Summaries
Each episode was manually reduced to approximately 2--3 lines, followed by the same continuation and recall tasks.
C. MSMA Memory
The same narrative was processed through the MSMA Memory System, and recalled memory context was supplied for the same continuation and recall tasks.
The recorded recall configuration at the time was primarily:
STM
+
MTM
+
LTM
The current Memory System has since been further evolved in response to relationship and context-preservation limitations discovered during this evaluation process.
The current Memory Architecture should therefore not be presented as identical to the architecture used in the original ~24,000-character recall measurement.
Measured original narrative:
194,956 characters
Recorded MSMA recall context:
~24,000 characters
The historical estimate was approximately:
STM : ~2,000
MTM : ~20,000
LTM : ~2,000
Total : ~24,000 characters
This corresponds to:
(194,956 - 24,000) / 194,956 ≈ 87.7%
Under that test configuration, the character volume supplied during recall was approximately 87.7% lower than the full original narrative.
The measurement compares:
Original Character Count vs. Recall-Context Character Count
API token consumption was not measured for this historical test.
MSMA therefore does not describe this result as "88% token reduction."
Manual summaries could produce a smaller context than MSMA's recall context.
The purpose of the experiment, however, was not simply to maximize compression.
The central question was:
How much input can be reduced while still preserving useful long-range recall and narrative continuity?
Internal observations recorded cases in which the MSMA Memory condition maintained historical information and narrative continuity more reliably than the manual-summary and full-context conditions.
These observations are qualitative results from an internal narrative evaluation, not a standardized benchmark or statistical ranking.
Observed outcomes included:
- improved long-range narrative recall in tested cases,
- improved recall of historical character information,
- improved recall of earlier events,
- improved narrative continuity in tested cases,
- cases where unsupported narrative facts were not invented when corresponding memory could not be found,
- cases where individual facts were remembered but their relationship to an event was reconstructed incorrectly.
Both successes and failures were used to evolve the architecture.
This evaluation should be understood as an:
Internal Long-Context Narrative Evaluation
It is not a standardized benchmark, and MSMA does not claim universal superiority over particular LLMs or a quantified hallucination-reduction rate.
Detailed prompts, outputs, and comparison material are planned for separate public evidence documentation.
MSMA includes a Tool System for tool discovery, registration, routing, and execution.
Tool Request
↓
Discovery
↓
Registry
↓
Router
↓
Executor
↓
Capability
↓
Result
The Tool System is designed to connect relatively simple external operations, such as search, with broader runtime capabilities.
A working E2E path through the UI and runtime has been demonstrated.
Implementation : Confirmed
E2E Execution : Confirmed
Runtime Log : Available
UI Recording : Available
The Agent Runtime resolves registered agents according to requested capabilities and executes them through the runtime.
Capability Request
↓
Agent Resolver
↓
Runtime Agent Registry
↓
Agent
↓
Agent Executor
↓
Capability Runtime
↓
Execution
An agent is therefore not intended to end as a generated file.
It can be registered in the runtime, resolved by later requests, and executed through the capability path.
This path was demonstrated during the Self Expansion E2E test.
When a required agent is not available, MSMA can invoke Self Expansion to extend runtime capability.
A working E2E demonstration confirmed the following path:
User Request
↓
Intent / Mission
↓
Router
↓
Agent Runtime
↓
Required Agent Missing
↓
Self Expansion
↓
AgentSpec
↓
RegistrationPlan
↓
CONTROLLED_APPLY
↓
Runtime Registration
↓
Generated Agent
↓
Runtime Re-resolution
↓
Capability Runtime
↓
Execution
↓
SUCCESS
The demonstration used:
Capability:
music_generation
Generated Runtime Agent:
MUSIC_GENERATION_AGENT
The Registration Plan contained seven operations:
REGISTER_AGENT
REGISTER_RUNTIME_AGENT
REGISTER_CAPABILITY
CREATE_MEMORY_SPACE
CREATE_GENERATED_AGENT
REGISTER_INTERFACE
REGISTER_AGENT_MANIFEST
Observed runtime results included:
Runtime Agent Registration
status = REGISTERED
Agent Auto Creation
status = CREATED
The generated and registered agent then proceeded through:
AgentExecutor
↓
CapabilityRuntime
↓
music_generation
↓
COMPLETED
The demonstrated scope is therefore more than file generation:
Generation → Registration → Resolution → Execution
Successful runtime logs and a UI recording are available for evidence preparation.
MSMA includes a Self Improvement Control Plane designed to analyze system problems or improvement requests and manage controlled modifications.
At the architectural level, the lifecycle includes:
Observation
↓
Diagnosis
↓
Proposal
↓
Analysis
↓
Design
↓
Simulation
↓
Evaluation
↓
Decision
↓
Development
↓
Validation
↓
Patch
↓
Verification
↓
Controlled Apply
Self Improvement can be initiated through controlled improvement requests and is also architecturally connected to monitoring/diagnosis pathways.
MSMA distinguishes between implemented architecture and what a particular public E2E demonstration directly proves.
A successful recorded case began with a user-authorized improvement request and proceeded through the Self Improvement path.
User-authorized Request
↓
SELF_IMPROVEMENT
↓
ControlPlane
↓
Development
↓
Code Generation
↓
Diff Validation
↓
Sandbox
↓
Patch
↓
APPLIED
↓
Lifecycle
↓
Learning
Observed results included:
Diff Validation : PASS
Sandbox : PASS
Patch : APPLIED
Lifecycle : APPLIED
Final Status : SUCCESS
The demonstrated claim is therefore:
User-authorized improvement request → validated patch → APPLIED
The existence of monitoring, diagnosis, and proposal pathways does not imply that one demonstration proves every possible autonomous improvement path.
MSMA is not designed around unrestricted self-modification.
System changes are processed through multiple control and validation layers, including:
- Constitution / Policy Rules
- Guardian
- Execution Guards
- Approval Gates
- Validation
- Sandbox Checks
- Patch / Lifecycle Controls
- Verification
Conceptually:
Proposed Change
↓
Constitution / Rules
↓
Guardian / Guards
↓
Validation
↓
Sandbox / Verification
↓
┌───┴────┐
▼ ▼
ALLOW BLOCK
The guiding principle is:
Self-modification is treated as a controlled lifecycle, not an unrestricted code-generation loop.
During development, invalid diffs and changes that failed contract or validation checks were observed being blocked rather than applied.
Successful application cases and blocked-invalid-change cases can therefore be documented separately as evidence of the Control Plane behavior.
MSMA includes monitoring structures for observing runtime execution and recording outcomes.
Execution
↓
Monitoring
↓
Trace / Result
↓
Diagnosis / Feedback
The Self Expansion demonstration also recorded the successful execution through monitoring.
Monitoring is architecturally intended to connect runtime results with diagnosis, improvement, memory, and feedback pathways.
The current public description does not claim a fully autonomous learning loop beyond the integration paths that have actually been validated.
MSMA contains additional engines and components intended to support a broader runtime pipeline, including:
- Goal Engine
- Simulation Engine
- Evaluation Engine
- Decision Engine
- Execution Engine
- Feedback System
- Learning Loop
- DCR
- Runtime Kernel
- AGI Runtime Pipeline-related components
- Tool System
- Agent Runtime
- Memory System
Components are designed to communicate across contracts, adapters, gateways, and bridges.
A critical distinction is maintained:
Implemented Architecture ≠ Publicly Demonstrated E2E Capability
The existence of a module does not mean that the complete pipeline containing that module has been validated through the same public E2E path.
MSMA does not place a particular cloud LLM provider at the center of the system.
Development has made extensive use of local models through Ollama, including model families such as:
- Llama
- Qwen
- DeepSeek Coder
- other local models
Conceptually:
MSMA Runtime
│
Model Gateway
│
┌─────────┴─────────┐
▼ ▼
Local Models Cloud Models
│
Ollama
Local-first does not mean local-only.
MSMA is designed to make local execution practical while keeping the model layer separable so cloud models can also be connected when needed.
The architectural principle remains:
Runtime and Memory are the system center; the model is a replaceable execution component.
Status: Runtime integration to be verified before public claim
Privacy and sensitive-data protection modules have been implemented during development, but their current runtime integration status will be reverified before they are presented as an integrated public capability.
Until that verification is complete, MSMA does not claim that privacy protection is automatically applied across every runtime execution path.
MSMA distinguishes three different states:
Implemented
≠
E2E Demonstrated
≠
Publicly Documented
| Capability | Implemented | E2E Demonstrated | Evidence | UI Demo Status |
|---|---|---|---|---|
| Memory System | Yes | Yes* | Available | Historical test evidence available |
| Tool System | Yes | Yes | Available | Yes |
| Agent Runtime | Yes | Yes | Available | Included in Self Expansion demo |
| Self Expansion | Yes | Yes | Available | Yes |
| Self Improvement | Yes | Yes | Available | Yes |
| Monitoring | Yes | Partial | Available | Included in runtime demos |
| Privacy / Data Protection | Yes** | To verify | Not prepared | Not prepared |
* The Memory System was evaluated through an internal long-context
narrative experiment, not a standardized benchmark.
** Privacy/Data Protection modules were implemented, but full runtime
integration must be reverified before publication.
The following public artifacts provide demonstration recordings and sanitized runtime evidence for major MSMA execution paths.
Public evidence: MSMA Memory System — Public Evidence
The public evidence summarizes the long-context narrative experiment, recall behavior, unsupported-event rejection, insufficient-information handling, and the observed character-count reduction.
| Metric | Result |
|---|---|
| Original narrative | 194,956 characters |
| MSMA recall context | ~24,000 characters |
| Recall-context character reduction | ~87.7% |
This is a character-context reduction measurement, not a token-reduction rate.
MSMA has progressed beyond an idea or isolated proof-of-concept component.
The core runtime architecture exists, and multiple key capabilities have working execution or evaluation evidence.
- Long-context Memory Evaluation
- Tool System
- Agent Runtime
- Capability Runtime execution
- Self Expansion
- Controlled Self Improvement
- Local model execution
- Goal Engine
- Simulation Engine
- Evaluation Engine
- Decision Engine
- Execution Engine
- Feedback System
- Learning Loop
- DCR
- Runtime Kernel
- Monitoring / Diagnosis
- Constitution / Guardian / Guard components
- Hierarchical / Semantic Memory components
MSMA is not yet a finished MVP or production-ready product.
Further productization would require additional work in areas such as integration validation, reliability, testing, security, UI/UX, deployment, documentation, and operational stability.
MSMA is currently a Pre-MVP experimental runtime.
Known limitations include:
- Self Improvement does not produce a successful patch in every generation attempt.
- Generated code can contain formatting or style inconsistencies.
- Invalid patches may be blocked by validation or control layers.
- Some runtime components are implemented but have not yet been validated through the same E2E path.
- Long-context memory results come from an internal narrative evaluation rather than a standardized benchmark.
- Unsupported-generation suppression was observed qualitatively, but no formal hallucination-reduction rate was measured.
- Privacy/Data Protection runtime integration requires additional verification.
- Local-model resource requirements vary substantially with model size and context configuration.
- APIs, contracts, and runtime architecture may continue to change.
MSMA's long-term goal is not merely to place multiple AI features behind a single runtime.
The broader goal is to develop a persistent AI runtime that can accumulate knowledge and experience across executions, use tools and agents when appropriate, expand capabilities when required, and improve parts of the system through controlled processes.
Persistent Memory
↓
Knowledge / Experience
↓
Runtime Decision
↓
Tools / Agents / Models
↓
Execution
↓
Result / Experience
↓
Memory
↓
Next Execution
Models may change.
Tools may be added.
Agents may be created.
Capabilities may expand.
The runtime may evolve.
The accumulated memory and experience can continue across subsequent executions.
MSMA is not intended to be meaningful only when the entire runtime stack is deployed as a single product.
Major responsibility areas---including Persistent Memory, Runtime Orchestration, Tools, Agents, Capabilities, Monitoring, Self Expansion, and Controlled Self Improvement---are separated into modular pipelines.
This structure creates the possibility of using individual pipelines independently or combining selected components to develop different AI systems according to future product or research needs.
MSMA
│
┌──────────────────┼──────────────────┐
│ │ │
Memory Runtime Control
│ │ │
│ Tools / Agents ├─ Monitoring
│ │ ├─ Self Expansion
│ │ └─ Self Improvement
│ │
└────────────┬─────┴──────────────┐
▼ ▼
Independent Pipelines Combined Systems
Examples include:
Memory
→ Independent memory layer for systems requiring long-term continuity
Memory + Agent
→ Persistent specialist agents that can reuse knowledge and experience from previous work
Memory + Tool System
→ Tool-based work systems that can use long-term context
Memory + Multi-Agent
→ Collaborative environments in which multiple specialist agents can use persistent memory
Runtime + Memory
→ Persistent analysis or decision environments that can reuse past execution and experience
Runtime + Memory + Agent
→ A foundation for long-running work, analysis, and simulation workflows
Runtime + Memory + Self Expansion
→ Adaptive execution environments that can expand required capabilities or agents
Monitoring + Self Improvement
→ Systems that observe execution results and connect them to controlled improvement procedures
Full MSMA
→ An extensible AI runtime architecture integrating multiple pipelines
The potential value of MSMA is therefore not limited to a single AI application.
MSMA may evolve into a foundation architecture for persistent AI systems ranging from independent modules to combinations of multiple pipelines.
These combinations are not presented as completed commercial products. They describe future productization and research/development directions that may be derived from the modular architecture currently under development.
The current architecture suggests several possible future development directions.
Agents that combine runtime execution with persistent memory so knowledge and experience from previous work can be reused in later tasks.
Specialized systems combining domain memory, tools, capabilities, and agents for a particular field.
With reliable domain data, specialist tools, and appropriate validation added, these systems could be developed for different domains such as creative work, analysis, research support, and knowledge work.
A platform in which agents can be selected or created, use tools and memory, produce work or creative outputs, and support publication or downstream use.
User Goal / Idea
↓
MSMA Runtime
↓
Agent Selection / Creation
↓
Memory + Tools + Capabilities
↓
Work / Creation
↓
Result
↓
Publish / Use
↓
Experience
↓
Memory
A runtime in which multiple specialized agents divide roles and collaborate around shared runtime and memory structures.
A future direction in which Goal, Simulation, Evaluation, Decision, Execution, Memory, and specialist Agents/Tools are advanced and combined for repeated analysis and simulation workflows.
This does not mean that the current system has achieved automated scientific discovery or specific research outcomes. Practical use in specialist fields would require reliable domain data, specialist tools, validation frameworks, and expert review.
A local-first environment combining local models and persistent memory to reduce dependence on external cloud execution.
A runtime capable of expanding missing agent capabilities through Self Expansion and attempting system improvements through controlled validation and modification processes.
These are future development goals and potential applications derived from the current architecture. They are not presented as completed product capabilities.
Public MSMA evidence is based on actual runtime execution.
Raw DEBUG logs are not published directly because they may contain information that is irrelevant or inappropriate for public disclosure, including:
- absolute local paths,
- user or machine names,
- object addresses,
- UUIDs,
- full registry dumps,
- internal payloads,
- internal policies,
- model parameters,
- debug-only state.
Public evidence therefore follows a sanitization process:
Raw Runtime Log
↓
Sanitization
↓
Sensitive / Irrelevant Data Removal
↓
Relevant Execution Evidence Extraction
↓
Public Evidence
Public evidence will represent selected and sanitized portions of actual runtime execution rather than newly fabricated execution records.
The purpose of the public MSMA repository is to explain the technology, architecture, observed execution results, and future potential.
It is not intended to disclose every internal implementation mechanism.
For the Memory System, public documentation can describe:
- the existence and role of memory layers,
- their architectural role,
- evaluation methodology,
- evaluation scale,
- recall-context size,
- observed successes and failures,
- the way testing influenced architectural evolution.
Detailed internal mechanisms are intentionally excluded from the public README, including:
- Meaning Extraction Logic
- Memory Scoring
- Selection Formula
- Promotion / Deletion Rules
- Relationship-information processing methods
- Internal memory-structure construction methods
- Recall-selection Formula
- Internal Parameters
The same principle applies to internal Self Improvement control and validation mechanisms where detailed disclosure is unnecessary for explaining the system.
MSMA is currently at the Pre-MVP stage.
Its future commercialization path has not been limited to a single model.
Possible directions may include:
- full or partial technology/system transfer,
- licensing of individual pipelines or combined architectures,
- joint development,
- strategic partnerships,
- continued independent development,
- company formation and team building,
- investment-backed product development,
- other commercialization collaborations.
MSMA's modular structure leaves open productization paths centered not only on the complete system but also on selected pipelines or combinations of components.
The immediate goal is to present implemented technology and observed results in a form that makes MSMA's technical structure and expansion potential understandable and reviewable.
MSMA did not begin inside a large AI research organization.
It has been built by an independent developer through practical needs, implementation, repeated experimentation, and failure-driven redesign.
Several parts of the architecture---particularly the Memory System---were changed not only because of theoretical design decisions but because real tests exposed weaknesses.
Idea
↓
Implementation
↓
Test
↓
Failure
↓
Analysis
↓
Architecture Change
↓
Retest
A more detailed development history and project story may be documented separately.
MSMA does not currently claim:
- completed AGI,
- fully autonomous AI,
- a system requiring no human control,
- unrestricted self-modification,
- infinite self-improvement,
- quantitatively proven elimination of hallucination,
- universal superiority over specific LLMs,
- complete integration of every runtime component,
- production readiness,
- automated research outcomes or scientific-discovery capability in a specific professional field.
MSMA is currently best described as:
A local-first Pre-MVP AI runtime implementing and exploring a modular architecture that connects persistent memory, runtime orchestration, models, tools, agents, monitoring, capability expansion, and controlled system improvement.
MSMA explores a central question:
What becomes possible if an AI system can preserve continuity of knowledge and experience independently of the model currently executing?
Model changes
↓
Runtime continues
↓
Memory persists
↓
Experience accumulates
↓
System adapts
The goal is not simply to make one particular LLM more powerful.
The goal is to build a persistent AI runtime in which memory and experience can continue even as models change, tools are added, agents are created, and capabilities expand.
The model is a component. Memory is the continuity.
The current purpose of the public MSMA repository is technology presentation, architectural documentation, and publication of selected development evidence.
The exact scope of source-code disclosure and terms of use will be determined separately before public release.
Because patent applications and future commercialization are under consideration, some internal implementation details may be intentionally excluded from public materials.
MSMA is currently Pre-MVP software and an experimental AI runtime.
APIs, contracts, runtime behavior, and architecture may change.
Published evidence documents observations from actual development and runtime testing. It does not imply:
- completion of a fully autonomous system,
- achievement of AGI,
- quantitatively validated general intelligence,
- complete elimination of hallucination,
- production readiness,
- automated research outcomes or scientific-discovery capability in a specific professional field.
Implementation status, E2E demonstration status, and future development goals are intentionally distinguished wherever possible.
For technology transfer, licensing, joint development, strategic partnerships, investment, or other inquiries:
Email: our_msma_email@naver.com