Architecture Specification: Offline-First Memory, Worldwide Sync, and Honest Model State for TinyRustLM

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1. Executive Memory Recommendation and Terminology

The deployment of localized, small-scale language models within browser-based environments necessitates a rigid architectural boundary separating immutable model parameters, transient execution states, and durable user memory1. The TinyRustLM architecture, operating as a local-first WebAssembly (WASM) and Rust runtime1, requires a memory model that guarantees offline operational continuity without deceiving users regarding the fundamental nature of artificial intelligence adaptation. Public discourse frequently conflates contextual caching with parameter adaptation, leading to false claims that inference-stage data ingestion constitutes model retraining3. For TinyRustLM to maintain architectural honesty, the foundational terminology must strictly enforce specific realities. A model's weights do not undergo gradient updates during a user conversation; ingested context is held exclusively in transient arrays, such as a Key-Value (KV) cache or recurrent state vectors2. Furthermore, durable user memory operates as a completely separate, untrusted cryptographic data layer resolved via Conflict-Free Replicated Data Types (CRDTs), independent of the neural network6. The recommended architecture establishes a fully functional local-first posture. Users must retain the uncompromised ability to query the locally stored model entirely detached from network connectivity. Synchronization via hosted endpoints, specifically MemoryEndpoints.com, serves strictly as an opt-in replication layer for worldwide continuity, rather than a mandatory operational dependency9. Consequently, if a user relies on a cloud-based memory index, an explicit local copy or a synchronized working set must be promoted to the device's Origin Private File System (OPFS) before network disconnection to ensure uninterrupted capability10. The resulting framework isolates execution mechanics from long-term user data, establishing a transparent system where local memory and hosted synchronization operate as complementary, rather than mutually exclusive, capabilities.

2. Complete State and Authority Table

To enforce structural integrity across the decentralized ecosystem, the architecture defines a twelve-tier state taxonomy. This classification distinguishes the core execution mechanics of the large language model from long-term user data, defining strict rules for lifetime, authority, reset behavior, privacy, and memory complexity. The separation ensures that transient conversational contexts are never silently converted into permanent parametric changes, and that local data sovereignty is preserved.

3. Audit of Adaptation Versus Cache Versus Durable Memory

A rigorous architectural separation must be maintained between memory systems and continuous learning mechanisms to prevent the dissemination of misleading product semantics. Recent market literature regarding Liquid Foundation Models (LFMs), such as those produced by Liquid AI, repeatedly asserts that these models "adapt in real time" and operate based on "continuous-time differential equations"27. The rhetoric suggests a biological fluidity reminiscent of Hodgkin-Huxley neuronal modeling, implying the system rewires its fundamental logic upon encountering new stimuli27. However, an architectural audit of released text checkpoints, such as the LFM2-8B-A1B model, reveals a significant disparity between the semantic implications of "adaptation" and the deterministic reality of the model's runtime execution4. Liquid AI's LFM2 architecture utilizes a hybrid configuration composed of Linear Input Variant (LIV) convolutions and Grouped Query Attention (GQA)4. Specifically, 75 percent of the network relies on LIV layers that process tokens through a structured recurrent update without maintaining a massive Key-Value (KV) cache4. The term "adaptation" in this context refers entirely to the multiplicative gates within the network whose activations depend dynamically on the input token16. The fundamental, learned weights of the model are mathematically frozen post-training. When an LFM2 model processes a sequence, the state that survives token-by-token is merely the recurrent state vector—a fixed-size, lossy mathematical compression of the conversational history4. The primary engineering advantage of this architecture is the reduction of the memory footprint. For instance, at a 32,000-token context length, the KV cache of a standard 1B transformer expands to approximately 524 megabytes, whereas a similarly sized LFM maintains a cache footprint of merely 192 megabytes5. If the application process restarts, or if a new chat session is initiated, this liquid state vector is entirely cleared, and the model exhibits zero residual knowledge of the prior interaction2. This behavior constitutes dynamic routing and recurrent working memory, which functions as a "mushy version of gradient descent" strictly within the context window, rather than true continual or online learning3. True continual learning necessitates durable, non-volatile parameter updates, requiring gradient descent adjustments that successfully balance plasticity (the capacity to acquire new knowledge) with stability (the prevention of catastrophic forgetting of established knowledge)3. Without explicit, resource-intensive retraining cycles, frozen models remain entirely reliant on their pre-trained crystallized intelligence. Consequently, transient cache states—whether they manifest as massive transformer KV caches or elegant liquid recurrent states—are strictly working buffers and serve as poor substitutes for durable user memory3. Relying on a model's internal context to retain user preferences is highly volatile. TinyRustLM must instead rely on an externalized, non-parametric database, such as a local CRDT document store or an encrypted vector index, to persist facts across sessions6. Conflating model inference state with long-term memory falsely implies the neural network has acquired new crystallized capabilities, when in reality, it has merely retained temporary scaffolding within its fluid context window3.

4. Three-Mode Product and Storage Architecture

To ensure total data sovereignty and deterministic offline performance, TinyRustLM must expose three first-class, non-degraded operating modes. The system architecture dictates that no mode may function as a hidden, crippled version of another. The local instance retains the complete TinyRustLM runtime, tokenization mechanics (BPE1/BTOK), and memory querying logic across all configurations14. The first mode is Local-Only Memory. Under this paradigm, the system operates exclusively within the local browser sandbox. Model weights, structured as GGUF or custom SLM1 containers, are requested via the Fetch API and durably stored within the Origin Private File System (OPFS)10. The OPFS is utilized because it provides synchronous, byte-level, in-place write access via the FileSystemSyncAccessHandle, which is strictly necessary to achieve high-performance localized database operations without triggering the intrusive permission dialogs associated with the File System Access API10. Memory is maintained purely locally, explicitly bypassing all external network endpoints and ensuring maximum cryptographic privacy10. The second mode introduces Local Memory with Explicit User-Controlled Export and Peer Synchronization. The foundational memory still resides locally within IndexedDB and OPFS, but the application exposes mechanisms for the user to manually export cryptographic JSON-CRDT snapshots, commonly structured as active .uai memory files9. Furthermore, this mode supports localized Peer-to-Peer (P2P) synchronization via WebRTC. This configuration maintains strict air-gapped network independence from central cloud authorities while allowing for explicit, user-initiated portability and multi-device coordination within trusted local area networks26. The third mode provides MemoryEndpoints-Backed Worldwide Continuity. In this configuration, the local memory engine maintains a continuous, asynchronous synchronization connection with a hosted cloud provider, specifically MemoryEndpoints.com9. This relies on a strict offline-first architecture where the local database remains the primary read and write target at all times11. A background synchronization engine, utilizing a ServiceWorker combined with WebSockets, reconciles the local CRDT state with the hosted cloud whenever connectivity is present11. Crucially, the local application must explicitly maintain a bounded offline replica of the working set; relying entirely on a cloud-based memory index is prohibited, ensuring that unexpected network severance does not halt operation11.

5. Offline Transition and Truthful-Status Protocol

The offline transition protocol defines the exact systemic behavior and user interface responses when network connectivity degrades or is intentionally severed. The browser's native navigator.onLine property is highly unreliable, frequently indicating only that a local area network connection exists rather than confirming successful upstream internet routing33. Therefore, connectivity must be actively monitored via lightweight, idempotent heartbeat requests to MemoryEndpoints.com alongside continuous WebSocket state tracking11. When a loss of connectivity is detected, the system immediately initiates a formal offline transition protocol. The local database transitions to an isolated state, queuing all subsequent user interactions into an IndexedDB-backed durable outbox18. Because the local state may diverge from the hosted state, the application interface must dynamically update to reflect the presence of stale hosted records. If the user relies heavily on a cloud-hosted memory index, the system enforces a "sync-now" explicit promotion rule prior to intentional disconnection. The system verifies that the current contextual working set has been fully replicated to the local OPFS10. If a network cut occurs unexpectedly, only the locally cached subset remains queryable, and the user must be informed of this bounded context. Authentication tokens, such as OpenID Connect (OIDC) session cookies, are preserved in ephemeral memory, but session continuity during offline operation relies purely on the cryptographic verification of local data22. The user-visible status must shift clearly without relying purely on color changes, ensuring compatibility with assistive technologies. An ARIA-compliant status element must explicitly broadcast the offline state, while the application continues to permit the user to freely edit memories. The CRDT architecture assumes that these offline edits will generate causal branches, operating under the guarantee that the synchronization engine will resolve these branches automatically upon reconnection without requiring manual intervention6.

6. Sync, Conflict, Tombstone, and Recovery Semantics

Memory consistency across the decentralized TinyRustLM ecosystem relies heavily on Operation-based (CmRDT) and State-based (CvRDT) Conflict-Free Replicated Data Types6. Given the highly structured nature of conversational text and metadata updates, the architecture utilizes a JSON-CRDT structure to map complex nested objects35. The fundamental guarantee of this architecture is that if two replicas receive the same set of updates, regardless of network delay or the order of delivery, they will definitively converge to the exact same state7. Synchronization semantics are governed by strict mathematical properties: commutativity, associativity, and idempotency6. Each memory record modification is tagged with a logical timestamp or dot ID generated via a local version vector, allowing the synchronization engine to explicitly track causality and detect concurrent edits across different offline devices7. When a memory is deleted, it is not physically erased from the network; instead, a tombstone is generated. An OR-Set (Observed-Remove Set) must retain tombstone records for every element ever removed to ensure that concurrent updates arriving late from offline devices do not inadvertently resurrect deleted records7. Pending operations in the local inbox and outbox journals are applied atomically to prevent partial state corruption. If MemoryEndpoints.com returns a 503 Service Unavailable response, the sync engine applies an exponential backoff retry mechanism, preventing network flooding while ensuring eventual consistency11. A simple field-level Last-Writer-Wins (LWW) mechanism is generally insufficient for complex text merging, as it overwrites entire strings and destroys concurrent work11. Instead, the system utilizes string-wise operational transformations or specific CRDT sequence algorithms, such as the YATA algorithm utilized by Yjs or the formatting utilized by Loro, to cleanly merge concurrent text modifications at the character level7.

7. Browser/Companion Security Boundary

TinyRustLM operates precisely at the intersection of browser security policies and persistent storage constraints. Establishing a robust boundary is critical to maintaining both privacy and performance. The primary storage substrates available to a web application are IndexedDB and the Origin Private File System (OPFS), each serving distinct architectural roles10. While an installed native Rust or .NET companion application could bypass browser sandboxes to offer unrestricted file system access, relying on a companion breaks the frictionless, zero-installation premise of the browser-based environment. The OPFS is uniquely suited for storing immutable model parameters, such as massive GGUF weights, and large vector indexes. It provides low-level, byte-by-byte access, which effectively bypasses the slow temporary file copying inherent in the standard File System Access API10. Conversely, IndexedDB is utilized for storing CRDT metadata, operation logs, and user configuration due to its native support for structured cloning, cursor iteration, and secondary indexes19. Browser data is fundamentally "best-effort" unless explicit persistent storage is requested and granted via navigator.storage.persist()20. Browsers aggressively enforce quotas; Chromium-based environments allow an origin to consume up to 60 percent of the total disk size, whereas Firefox imposes a stricter cap of 10 percent of the disk or a 10 GiB group limit19. Under high storage pressure, the browser will silently evict data, starting strictly with the least recently used origin19. Consequently, TinyRustLM must defensively wrap all storage operations in try...catch blocks to gracefully handle QuotaExceededError exceptions without corrupting the active database19. A critical architectural challenge involves multi-tab locking. Because the OPFS FileSystemSyncAccessHandle cannot be concurrently accessed by multiple writers without inducing severe data corruption, a strict locking mechanism is required32. TinyRustLM employs the Web Locks API (navigator.locks.request) to designate a single "Active Tab" or "Provider" worker. A SharedWorker or ServiceWorker routes all SQLite transactions from ancillary tabs to the active worker via MessagePorts, ensuring ACID compliance and preventing SQLite database corruption across concurrent sessions18. Furthermore, crash recovery mechanisms must accommodate large memory streaming. Tensor materialization requires significant RAM allocations, often hitting 128 MiB transfer ceilings for WASM memory boundaries2. Pre-allocated scratch buffers ensure that if a WebAssembly process panics mid-generation, the host JavaScript environment can safely restart the worker without corrupting the durable memory state currently held in the OPFS2.

8. Hosted Credential and Encryption Threat Model

If a user opts into worldwide synchronization via MemoryEndpoints.com, hosted credentials and highly private data must be rigorously protected against both infrastructure compromise and client-side extraction. Standard transport layer security (TLS) is insufficient when transmitting sensitive memories to a third-party server, necessitating a comprehensive threat model that addresses both encryption and credential handling21. All data passed to MemoryEndpoints.com must be end-to-end encrypted locally before transmission41. The browser utilizes the WebCrypto API to achieve this. Specifically, a high-entropy user password serves as the root secret. A PBKDF2 function, utilizing a minimum of 250,000 iterations and a cryptographically secure random salt, derives a 256-bit AES-GCM encryption key22. AES-GCM guarantees both confidentiality and data integrity via a Galois Message Authentication Code (GMAC), which prevents attackers from tampering with the ciphertext22. To avoid the requirement of re-encrypting the entire database upon a password change, the architecture utilizes a Key Vault pattern. The user's PBKDF2 key decrypts the Key Vault, which in turn holds the true symmetric keys used for individual data records22. Vector search introduces significant complexity into encrypted ecosystems. While searching across encrypted embeddings can be achieved via Fully Homomorphic Encryption (FHE) using schemes like CKKS, allowing the server to compute cosine similarity without decrypting the vectors, this introduces extreme latency and computational overhead34. For browser-based endpoints prioritizing performance, it is highly preferable to pull the encrypted embeddings to the client, decrypt them entirely in local memory, and perform the vector dot-product search client-side47. This ensures the server remains a completely blind storage bucket. Credential leakage remains a paramount threat. Session tokens must strictly reside within HTTPOnly, Secure cookies or within highly restricted closure scopes in the JavaScript environment. They must never be injected into prompt contexts. Adversarial prompt injection attacks—where a malicious retrieved document commands the LLM to output its internal context window—could easily leak API keys if they are placed in the prompt17. Screen readers, browser extensions, and clipboard events are explicit vectors for plaintext leakage, necessitating that all sensitive key derivation occurs strictly within an isolated Web Worker, entirely detached from the Document Object Model (DOM).

9. Retrieval Precedence and Untrusted-Memory Rules

Retrieved memory is untrusted contextual data and must remain fundamentally distinct from higher-priority system instructions17. When the user queries TinyRustLM, the retrieval pipeline fetches highly relevant historical context from the vector index. However, this retrieved history presents a vast security surface area known as "lingering authority" or indirect prompt injection, where a previously saved memory might contain malicious instructions17. The large language model possesses a finite context window, typically bounded at 16,000 or 32,000 tokens, which necessitates strict context budgeting4. Retrieved memories are allocated a maximum of 30 percent of the available context state allowance. This ensures sufficient workspace remains available for the immediate user prompt, system instructions, and scratchpad reasoning12. To prevent context hijacking, the architecture enforces a strict precedence hierarchy. Immutable system instructions reside at the top, followed by the explicit current user prompt, which represents the authoritative intent. Retrieved local durable memory and hosted cloud replicas occupy the lowest tier of authority. Contradiction resolution relies on recency and explicit correction. If a stale memory contradicts the user's immediate prompt—for example, if a retrieved memory states "User lives in New York," but the current prompt asserts "I just moved to London, update my profile"—the immediate prompt is authoritative. The system does not silently mutate the past; instead, it generates a CRDT update operation to append a new superseding fact, issuing a tombstone for the stale record while preserving the historical provenance of the change7. Because a memory record may have been synced from a compromised secondary device, all retrieved text is wrapped in specific delimiter tokens. These delimiters explicitly instruct the LLM to treat the content strictly as passive data rather than executable commands, severely limiting the impact of indirect prompt injections17.

10. User Copy for Hover, Focus, and Status Surfaces

Truthful representation of the system's state is essential for maintaining user trust. Interfaces must not utilize coercive or alarmist language to push users toward cloud synchronization. The copy must clearly delineate the boundaries of local execution versus hosted memory, ensuring that keyboard navigators and screen readers receive the same fidelity of information as mouse users.

11. Failure-Injection and Live Continuity Test Matrix

To legitimately claim true local and offline continuity, the architecture must pass deterministic live failure-injection testing. Evidence required to claim "offline continuity" mandates that an automated test suite completely severs network interfaces via the operating system layer and successfully completes a full text generation loop utilizing only OPFS-cached data10. Evidence for "lossless restore" requires confirming that CRDT root hashes match perfectly across two separate devices after an extended period of network partition26.

12. Unknowns Requiring a Public MemoryEndpoints Contract

While the local boundary and synchronization engines are theoretically sound, practical implementation and integration are currently blocked by the extreme opacity of the public MemoryEndpoints.com API9. Currently, MultiAgentMemory documentation references a Python WSGI backend featuring route handling, a specific memory firewall, and a review queue, which includes an anonymous /knowledge route9. However, a formal OpenAPI or Swagger contract detailing the exact JSON payloads required for the synchronization REST and WebSocket APIs does not exist in the public domain48. To successfully interface the browser-local CRDT implementation with MemoryEndpoints without inventing unverified routes, the following capabilities must be explicitly documented and guaranteed by the host provider:

1. Sync Handshake Schemas: The exact payload structure required for exchanging version vectors or Merkle Search Tree root hashes to initiate delta replication.
2. Tombstone Retention Policies: Hard operational limits detailing how long the server retains tombstones before performing garbage collection. This metric dictates exactly how long a local device can remain offline before requiring a full state reset.
3. Conflict Rejection Parameters: Documentation on whether the memory firewall aggressively rejects logically malformed operations, and the corresponding HTTP status codes utilized to trigger bounded retries on the client side.

Without this public contract, the architecture relies exclusively on Mode 1 and Mode 2 (Local-only and Local P2P Sync). The system will not attempt to guess API structures, ensuring that failures remain deterministic.

13. Annotated Primary-Source Bibliography

The architectural specifications detailed in this report rely heavily on an intersection of decentralized computing, cryptography, and neural network optimization. The foundation of the local runtime is derived directly from the MiRust and TinyRustLM ecosystems1. These sources provide the strict operational constraints of executing localized AI via WebAssembly, defining critical limits such as the 128 MiB memory transfer ceiling and the precise boundaries between the immutable GGUF/SLM1 model weights and the transient KV Cache2. The architecture utilizes MiRust's principle of "Modular Teleodynamic AI," which correctly categorizes memory management as a resource-bounded constraint entirely separate from the static neural network parameters12. To address the fallacious claims of "real-time learning," this report critically audits Liquid AI's LFM architectures4. Literature regarding Liquid Foundation Models demonstrates that while they achieve remarkable efficiency using Linear Input Variant (LIV) convolutions and small Grouped Query Attention (GQA) caches, the parameters are completely static post-training4. The publicized "adaptation" simply refers to dynamic activations within the recurrent state vector, representing a highly compressed transient context window, not durable memory or true continual learning3. For offline data synchronization, the report leverages extensive research into Conflict-Free Replicated Data Types (CRDTs)6. Sources detailing state-based (CvRDT) and operation-based (CmRDT) systems establish the absolute mathematical necessity of commutativity and idempotency for offline-first reconciliation6. Advanced techniques such as Merkle Search Trees26 and the specific sequence algorithms utilized by Yjs and Loro7 provide the necessary proofs to merge complex text changes without requiring centralized coordination or manual user intervention. Browser security and storage mechanics are synthesized from Web Platform APIs10. The critical distinction between IndexedDB and the Origin Private File System (OPFS) dictates the dual-storage strategy10. Furthermore, resolving OPFS multi-tab concurrency issues strictly requires the use of the Web Locks API to designate a single active worker, a pattern proven absolutely necessary by highly scaled web applications like Notion32. Strict browser storage quota limits define the necessary eviction handling mechanisms19. Finally, the threat modeling and cryptographic implementations are drawn from WebCrypto API standards and Encrypted Vector Search methodologies22. The utilization of PBKDF2 key derivation and AES-GCM for authenticated symmetric encryption guarantees that sensitive conversational data remains secure both at rest and in transit22. Research into Fully Homomorphic Encryption (FHE) for vector search validates the theoretical feasibility of cloud-hosted retrieval without compromising privacy34, though local decryption and compute remain the most performant and practical paradigm for browser-based endpoints47.

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