Encode the thought - neural network training for understanding semantic instead of predicting tokens. Version 2.
“Thoughts die the moment they are embodied by words.” A. Schopenhauer
This is a continuation of the first version of the Encode the thought
A neural architecture for extracting the invariant semantic core of text into a compact, order-invariant matrix of learnable slots. Instead of predicting the next token autoregressively, Encode_thoughtV2 compresses sequences of base encoder embeddings into a fixed semantic representation and reconstructs them via a parallel transformer decoder. The pipeline is model-agnostic and operates on top of frozen base encoders.
KEY HIGHLIGHTS
- Model-Agnostic Pipeline: Compatible with any transformer encoder. Currently optimized and validated with prajjwal1/bert-mini (256d).
- Ultra-Minimal Configuration: 1 slot, internal dimension 64, 1 encoder/decoder layer, 4 induced points. Total trainable parameters: approximately 0.8 to 0.9 million (less than 2 percent of the base LLM).
- Hybrid Loss and Regularization: CrossEntropy (alpha=0.2) plus CosineLoss (beta=1.0) with label smoothing (0.1) and Context Dropout (p=0.15) for stable parallel training.
- Phase 1 Complete: Achieves 80 to 90 percent lexical reconstruction and greater than 0.95 cosine similarity in Teacher Forcing (Corrected) mode.
- Transparent Limitations: Autoregressive generation (AR and Raw AR) currently collapses into repetition loops due to Exposure Bias. Stabilizing closed-loop generation is the sole focus of next phase.
CURRENT EXPERIMENTAL RESULTS
| Mode | Context Source | Lexical Accuracy | Status |
| Corrected (Teacher Forcing) | Ground Truth | 80 to 90 % | Preserves plot, entities, and semantics. Minor subtoken artifacts and local repetitions at sentence boundaries |
| AR (Quantized Context) | Own Predictions | Approximately 0% | Collapses into high-frequency token loops after 5 to 10 steps |
| Raw AR | Raw Embeddings | Approximately 0% | Similar collapse with semantic drift |
Diagnosis: The architecture successfully compresses and reconstructs semantics when provided with a valid context window. AR failure is strictly due to Exposure Bias (distribution shift between training and inference), not capacity limits or architectural flaws. Scaling parameters does not resolve this; it requires a shift to sequence-level training paradigms.
All code, datasets, and results are provided for full reproducibility.
continue on github