CONFIGURABLE TRANSFORMER FOUNDATIONS
Native Transformer architectures and versioned experimental configurations.
GAVRIEL.TECH // SYSTEM STATUS
LOADING
INITIALIZING
Preparing the interface and loading the next system state.
// PLEASE STAND BYGAVRIEL.TECH // SYSTEM STATUS
Preparing the interface and loading the next system state.
// PLEASE STAND BYExperimental MicroModel Architecture
MACHINE INTELLIGENCE / TRANSFORMER ARCHITECTURE / CONTINUAL LEARNING
An experimental architecture and development laboratory for persistent, modular, agent-oriented MicroModels.
EMMA explores how specialist MicroModels can learn from verified experience, retain useful capabilities, and cooperate through AdaptiveWeight Units, Context Drones, persistent Memory, and independently trained Decision MicroModels.
EMMA LABS is the platform and research environment. EMMA Native Foundation 001 is a model lineage developed within it.
EMMA investigates how small MicroModels can accumulate useful specialist intelligence while preserving previously learned capabilities.
The project brings together Transformer Foundation development, component-scoped training, AdaptiveWeight Units, Context Drones, Global Context, persistent Memory, and independent Decision MicroModels.
EMMA LABS supplies the infrastructure to construct these systems, inspect their behavior, and evaluate changes before activation. The aim is persistent specialist intelligence that develops through controlled learning and cooperation between versioned components.
Convert verified experience into persistent, independently learned MicroModel capabilities. Preserve acquired knowledge and behavior across sessions. Coordinate Context Drones, Memory, AdaptiveWeight Units, and Decision MicroModels. Enforce regression controls to protect validated capabilities during ongoing adaptation.
EMMA LABS provides the environment for constructing, training, inspecting, and evaluating MicroModels and their components.
Native Transformer architectures and versioned experimental configurations.
Targetable attention, FFN, and transformer-block components for scoped adaptation and inspection.
Declared training scopes for AdaptiveWeight Units, Decision MicroModels, and other candidate components before activation.
Controlled gates for acceptance, rejection, and restoration of a verified prior version. Rejected research candidates remain separate from active execution.
Versioned artifacts, parent identities, integrity checks, and traceable MicroModel development.
Independent Context sources, a separate task workspace G, and persistent Memory with temporal history and corrections.
Teacher integrations, verified demonstrations, recorded task outcomes, and bounded ToolDecision workflows.
Model, component, training, and resource observability.
Browser-based tools for inspecting MicroModels, running experiments, and stepping through bounded workflows and the integrated agent baseline.
Native Transformer architectures and versioned experimental configurations.
Targetable attention, FFN, and transformer-block components for scoped adaptation and inspection.
Browser-based tools for inspecting MicroModels, running experiments, and stepping through bounded workflows and the integrated agent baseline.
Declared training scopes for AdaptiveWeight Units, Decision MicroModels, and other candidate components before activation.
Controlled gates for acceptance, rejection, and restoration of a verified prior version. Rejected research candidates remain separate from active execution.
Versioned artifacts, parent identities, integrity checks, and traceable MicroModel development.
Independent Context sources, a separate task workspace G, and persistent Memory with temporal history and corrections.
Teacher integrations, verified demonstrations, recorded task outcomes, and bounded ToolDecision workflows.
Model, component, training, and resource observability.
These systems make development observable and reversible, from an individual component update to a complete experimental MicroModel configuration.
EMMA separates generative intelligence, Context, Memory, bounded decisions, and training into systems with explicit responsibilities.
C1 · C2 · C3 · C4
Independent information sourcesObjective · Constraints · Plan · State
Separate active-task workspaceVerified events · Corrections · Recall
Explicit promotion of relevant facts into GAddressable attention / FFN / blocks
Evidence resolution · Tools · Context
Independent parameters and lifecyclesOutputs · Tool actions · Provenance
Model and resource telemetryCheckpoint · Restore · Revalidate
Rollback to a verified prior versionThe underlying generative MicroModel, with addressable attention, FFN, and transformer-block components. EMMA Native Foundation 001 provides the native eight-block baseline. Qualified checkpoints remain immutable controls, with development continuing through separate versions.
Separately trainable weight components attached to declared Transformer Foundation targets. Their learned changes remain separate from stable Foundation weights. AdaptiveWeightModule provides an addressable artifact boundary for one adaptive component or a declared composition; each requires its own validation.
Independently maintained information views with their own identity, focus, source version, provenance, and cached state. A Context Drone holds information; the reader or MicroModel operating on it performs computation.
A separate workspace for the active task’s objective, constraints, plan, and important cross-Drone information. It remains distinct from individual Context Drones and long-term Memory.
Retained experience and verified observations, including temporal history, corrections, provenance, and retrieval. Relevant facts can be explicitly promoted into G while historical observations remain separately preserved.
Independently trained models for bounded choices, with separate parameters, objectives, metrics, artifacts, and lifecycles. Examples include ContextSourceDecision, EvidenceResolutionDecision, and ToolDecision.
A controlled candidate lifecycle that evaluates scoped changes before promotion and preserves a path back to a verified prior version.
The forward architecture of cooperating specialist MicroModels, learned Link Modules, and versioned Module Sets is shown separately in 07 / DIRECTION.
A compact native transformer trained from initialization as a controlled baseline for architecture and adaptation experiments.
The original qualification recorded 155 exact responses across 160 retention and hidden examples.
The qualified checkpoint remains an immutable control. Development continues through separately versioned derivatives and additional components.
These measurements describe the original bounded qualification. They do not represent general language or autonomous-agent performance, or the combined resource use of later modules.
Preserve experiment evidence
Checkpoint → Restore / Restart → Revalidate
REGRESSION → ROLLBACK TO VERIFIED PRIOR VERSION
EMMA treats learning as a controlled engineering process. Verified demonstrations and task outcomes can become training material for the responsible MicroModel or component. The candidate’s optimizer scope determines exactly which parameters may change.
Training occurs on an isolated candidate, which must pass the relevant checks before replacing an active version. Qualified source artifacts remain preserved, and rollback remains available when an update causes regression.
The platform has demonstrated bounded persistent learning in Decision MicroModels, including EvidenceResolutionDecision and a constrained multi-step ToolDecision workflow.
Inspect components. Declare update scope. Preserve version lineage.
Native Transformer Foundations, component-scoped training, AdaptiveWeight Units, and independent Decision MicroModels.
Candidate isolation, validation gates, checkpoints, artifact integrity, versioned runtime selection, promotion, and rollback.
Context Drones, Global Context G, persistent Memory, temporal corrections, and source provenance.
Teacher connections, verified task outcomes, bounded ToolDecision workflows, and explicit component selection.
A browser interface for inspecting MicroModels, running experiments, and observing training and resource telemetry.
Compact MicroModels, scoped updates, and measured compute and memory costs.
The next architectural direction extends EMMA beyond adaptations attached to one Transformer Foundation. Specialist MicroModels can retain their own weights and runtime state, process assigned Context Drones, and communicate through separately versioned Link Modules.
The Foundation is intended to participate as a model node alongside other specialists. Learned peer communication and broad Module Set qualification remain research goals.
Separately trained connections that route, gate, project, or transform information between model nodes while preserving qualified endpoint weights.
Versioned compositions that pin model nodes, Link Modules, Context Drone bindings, synchronization points, and resource policies for an operation.
ModuleActivationDecision selects and activates the required modules and links using task, Context Drone, capability, dependency, and resource metadata. The intended runtime loads or dispatches the validated selected set while leaving unselected candidate weights unloaded.
CompositionDecision controls communication topology, synchronization, and execution rounds among active modules. ModuleContributionDecision assigns an independent contribution weight to each active module’s result, allowing several modules to contribute during the same operation.
These describe the forward architecture. Supporting interfaces and bounded composition experiments exist. Reliable AdaptiveWeight composition, learned whole-model cooperation, and the complete decision-controlled modular fabric remain under investigation.
Supported Context routes, executable audits, historical comparisons, independent Memory receipt checks, and G closure share one task loop. The 51 Context cases are bounded integration checks; no new learning or general agent qualification is claimed.
Selected parallel execution used 158,504 executed parameters and 666,450 mounted artifact bytes. Sampled process-tree memory was approximately 478–560 MB; tensor payload alone does not describe total runtime cost. Timings are single descriptive samples.
Science preparation and a frozen-representation readout failed their development gates. The V57 readout reached 66.89% balanced accuracy with 72.41% prior-correct retention. These candidates were not promoted.
Python / PyTorch / CUDA / custom transformers / AdamW / distillation / LoRA / experience replay
FastAPI / WebSockets / SQLite / versioned artifacts / checkpoint management
Next.js / React / TypeScript / model graphs / telemetry / pytest / Playwright
EMMA’s research records document the experiments behind the platform: what worked, what failed, how results were measured, and which questions remain open.
Explore Transformer Foundation qualification, AdaptiveWeight Unit experiments, Context Drone integration, Global Context, persistent Memory, Decision MicroModel learning, and modular cooperation. Detailed results, controls, qualification limits, and experimental lineage belong in the research records.