AbstractIntegratedModule

# IntegratedPipeline
Flow Architecture

Complete input-to-output data flow with geometric weight shaping, hybrid inference, memory gating, and distributed peer consensus.

RAW INPUT → ENCODING → GEOMETRIC SHAPING → PARALLEL INFERENCE → MEMORY GATE → CALIBRATION → OUTPUT
GEOMETRIC CORE
TRANSFORMER
MLP
MEMORY / STORAGE
DECISION / GATE
DISTRIBUTED PEERS
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### 1 — Raw Input Reception

Text, structured data, or feature vectors enter the pipeline

ENTRY
data_preparation()Validates and structures raw input data
auto_generate_labels_from_texts()Optionally generates labels automatically
validate_writable_path()Ensures storage paths are ready
initialize_fitting()Prepares pipeline state for training
ACCEPTS → raw text structured data feature arrays labels (optional)
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### 2 — Encoding & Feature Generation

Raw input converted to dense numeric representations

ENCODE
text_encoder()TF-IDF or Count vectorization of text
encode()General-purpose encoding dispatcher
input_encoding()Prepares input tensor for models
sequence_encoding()Preserves order for transformer
feature_generation()Derives geometric features
shape_adaptation()Reshapes tensors to model spec
initialize_model_encoding()Sets up encoder infrastructure
automatic_parameterization()Auto-selects encoding params
OUTPUTS → TF-IDF vectors token sequences feature matrices
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### 3 — Geometric Weight Shaping

The novel core — weights shaped by spectral manifold geometry before inference

NOVEL CORE
eigenvalue_encoder()Decomposes weight matrices into eigenspaces
spectral_signature()Generates spectral fingerprint of weights
AME_Encoder()Anisotropy Manifold Encoding of weights
anisotropy_measurement()Measures directional variance in weight space
abstract_weight_shaping()Applies geometric constraints abstractly
weight_shaping()Reshapes weights along geometric manifold
spectral_similarity()Compares weight configs geometrically
EFFECT → geometrically constrained weights spectral manifold anisotropy map
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encoded features → both models
⟵ PARALLEL INFERENCE — BOTH RUN SIMULTANEOUSLY ⟶
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### 4a — Transformer

Sequential pattern + attention quality

TRANS
transformer_input_encoding()Prepares sequence for transformer
transformer_pooled_features()Extracts pooled representation
multi_head_attention()Mixed + fixed attention heads
attention_quality_computing()Scores attention pattern quality
transformer_utilities()Layer norm, softmax, forward pass
OUT → class probs attention weights confidence score
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### 4b — MLP

Focused sparse inference + fast generalization

MLP
mlp_training_features()Prepares features for MLP
focused_forward()Attention-guided sparse activation
mlp_predict()Full MLP inference pass
utility_MLP_set()Configures MLP utility settings
automatic_dense_layer()Auto-configures dense architecture
OUT → class probs entropy score confidence score
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both outputs → hybrid fusion
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### 5 — Hybrid Fusion & Agreement Gate

MLP and Transformer outputs compared — confidence decides the path forward

GATE
hybrid_prediction()Combines MLP + Transformer outputs
cosine_robust_similarity()Measures agreement between models
cosine_similarity()Standard similarity comparison
anisotropy_measurement()Geometric agreement analysis
predict_proba()Full probability distribution output
_softmax()Internal probability normalization
AGREEMENT OUTCOMES
✓ MODELS AGREE
Confidence boosted → direct to memory gate
✗ MODELS DISAGREE
Confidence reduced → calibration or peer query
OUTPUTS → fused probabilities agreement score confidence level
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fused output → memory lookup
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### 6 — Memory Gate & Cross-Session Retrieval

SQLite memory checked for geometrically similar past predictions

MEMORY
model_memory_gate()Gates prediction through stored memory
model_probability_gate()Gates probabilities through memory
_cross_session_availability()Checks cross-session memory state
modular_prediction_saving()Saves current prediction to memory
modular_probability_saving()Saves probability distribution
AME_Encoder()Encodes current input geometrically for matching
MEMORY DECISION PATHS
✓ MEMORY HIT
Similar past prediction found → calibrate with stored attention weights
✗ MEMORY MISS
No match → proceed with current prediction, save for future
SQLite TABLES → model_storage model_attn_storage agent_attn_storage
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if uncertain → peer query
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### 7 — Distributed Peer Consensus [CONDITIONAL]

Triggered when local confidence is below threshold — peer agents consulted

OPTIONAL
_handle_distributed_connections()Manages peer connection during prediction
request_prediction()Requests inference from peer agent
request_ensemble_vote()Requests vote from multiple peers
handle_peer_uncertainty()Handles uncertain peer responses
_calibrate_peer_probs()Calibrates peer probabilities
sync_memory_with_agent()Syncs learned memory to peer
PEER PROTOCOL → HMAC signed TLS encrypted trust scored ensemble voted
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calibrated probabilities → final output
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### 8 — Probability Calibration & Confidence Assembly

Final probabilities assembled from all sources and calibrated

CALIBRATE
_calibrate_probs()Calibrates using memory + attention data
predict_single()Single-sample full pipeline prediction
predict_async()Non-blocking async prediction
prediction_batch()Batch processing of multiple inputs
_batch_hybrid_prediction()Batch hybrid inference
safe_pickle_save_with_feedback()Saves calibrated model state
ASSEMBLES → MLP probs Transformer probs Memory calibration Peer votes
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★

### 9 — Final Output + Explainability

Predicted class, confidence scores, attention focus, and geometric explanation

OUTPUT
explain_decision()Full human-readable explanation
_get_attention_focus()What the model attended to most
_get_geometric_features()Geometric feature explanation
explain_confidence()Why confidence is high or low
get_decision_history()Full audit trail of past decisions
analyze_with_feedback()Accepts human correction → retrains
CLASS
PREDICTED LABEL
PROB[]
FULL DISTRIBUTION
CONF
CONFIDENCE SCORE
ATTN
ATTENTION FOCUS
WHY
EXPLANATION TEXT
↺ CONTINUOUS LEARNING FEEDBACK LOOP
_learn_from_feedback()Updates model from single correction
_batch_train_from_feedback()Batch update from corrections
consolidate_supervised_memories()Merges corrections into main memory
training_necessary_condition()Evaluates if retraining is needed
train()Full pipeline retraining with geometric shaping
save_complete_pipeline()Persists updated model to SQLite
Human correction → focused_backward() on relevant neurons → geometric weight re-shaping → updated spectral signatures → saved to memory → future predictions immediately improved
◈ Click any stage to highlight it · All stages run on pure numpy — no framework dependency · Peer stage only activates when local confidence < threshold