Here is a Product Requirements Document (PRD) for the Latent Vector Model (LVM) concept, structured around your goals and incorporating the insights from our previous discussion.
Product Requirements Document: Latent Vector Model (LVM) Core
1. 🌍 Introduction & Vision
This document outlines the requirements for the Latent Vector Model (LVM) Core, the reasoning engine for the Latent Neurolese Semantic Processor (LNSP).
The LNSP's vision is to create a reasoning system that overcomes the limitations of token-based processing. By operating directly in a high-dimensional conceptual (vector) space, the LNSP will perform "thinking" as a series of vector transformations, unburdened by linguistic syntax. This allows for a chain of thought that is purely semantic and logical. The final, synthesized vector-based "thought" is only translated into human-readable text at the final step.
The LVM is the heart of this system: a vector-in, vector-out model responsible for this internal conceptual "thinking" loop.
2. 🎯 The Core Problem: LVM vs. LLM Parity
The primary challenge is achieving performance parity with token-based Large Language Models (LLMs). Our initial attempts to train an LVM on encyclopedic data (like Wikipedia) failed, not due to a model bug, but due to a fundamental Data-Objective Mismatch.
The LVM's failure was a _symptom_ of being trained on data that is optimized for explanation (hub-and-spoke references), not for prediction (causal chains).
This PRD re-aligns the LVM's objective with a new, requisite data structure. The LVM must be trained on data where the next concept is a logical consequence of the previous one.
3. 🏛️ LNSP System Architecture
The LNSP is a three-stage pipeline. The LVM Core (Stage 2) is the primary focus of this document.
Stage 1: Encoder (Text-to-Concept)
[c_1, c_2, ..., c_n].Stage 2: LVM Core (Concept-to-Concept "Thinking")
1. Context_k = [c_1, ..., c_n, e_1, ..., e_k]
2. Emergent_Concept_k+1 = LVM(Context_k)
3. Append e_k+1 to context.
4. Repeat until a stop condition is met.
Stage 3: Decoder (Concept-to-Text)
LNSP Flowchart
Code snippet
[User Prompt (Text)]
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v
[Stage 1: Encoder] (GTR-T5 + LLM-based 16D TMD)
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v
[Initial Context: (c_1, ..., c_n)]
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+----------------------------------+
| [Stage 2: LVM Core (Recursive)] |
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| v |
| LVM(Context_k) -> [Emergent_Concept_e_k+1]
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| +---(Append)-------------------+
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| [Context_k+1: (c_1, ..., c_n, e_1, ..., e_k+1)]
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| +--(Loop until Stop Condition)--+
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v
[Final Thought Vectors: (e_1, ..., e_final)]
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v
[Stage 3: Decoder (V2T)]
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v
[Final Answer (Text)]
4. 📚 Requirement: The Causal Corpus
The LVM's success is entirely dependent on its training data. The optimal training data is not just _sequential_; it must be semantically causal and developmental.
The ideal dataset is a "chain-of-thought" corpus where each vector c_n+1 represents the logical consequence or next step derived from c_n.
Top 20 Ranked Data Sources for LVM Training
The following table ranks potential, downloadable, large-scale datasets based on their "LVM Suitability"—a measure of their causal/procedural/narrative flow. Domains are mapped from the TMD Schema2.
import $\rightarrow$ use; class def $\rightarrow$ instance; function A $\rightarrow$ call from B.Axiom$\rightarrow$ Lemma $\rightarrow$ Theorem.5. 🛠️ Requirement: Training Methodology for Emergent Concepts
To support the LNSP's "thinking" loop, the LVM must be trained to synthesize new concepts. A simple f(c_n) -> c_n+1model is insufficient, even with a causal corpus, as it only learns a single-step transition.
We must train the LVM to synthesize a _conclusion_ from a _context_.
Proposed Training Objective: Causal Synthesis Loss
The model must be trained to predict an "emergent concept" vector y_emergent from a variable-length context [c_1, ..., c_n].
We will create this y_emergent target in two ways:
- Context: [c_1, ..., c_n] (e.g., _Recipe steps 1-3_)
- Target y: c_n+1 (e.g., _Recipe step 4_)
- Loss: Loss_Chain = CosineDistance(LVM(Context), c_n+1)
- Why: This teaches the model to predict the _immediate next logical step_. It's the baseline for causal flow.
- Context: [c_1, ..., c_n] (e.g., _All "Methodology" chunks from an arXiv paper_)
- Target y: c_summary (e.g., The _single vector_ for the "Results" or "Conclusion" section's abstract/summary).
- Loss: Loss_Synth = CosineDistance(LVM(Context), c_summary)
- Why: This explicitly trains the model to _read a block of concepts and synthesize their implication/summary_. This is the core "thinking" task.
The final LVM training will use a combined loss to learn both local progression and holistic synthesis:
$$Loss_{Total} = \alpha \cdot Loss_{Chain} + (1 - \alpha) \cdot Loss_{Synth} $$This dual-objective ensures the LVM can both "take the next step" and "form a conclusion." ----- ## 6\. ⚙️ Requirement: Tunable Parameters The LNSP architecture introduces new hyperparameters for both training the LVM and running the inference loop. ### Training Parameters context_window_size (k): The number of vectors [c_n-k, ..., c_n] used to make a prediction. synthesis_window_size (s): The number of vectors [c_1, ..., c_s] used in the Loss_Synth objective. loss_alpha: The weighting (0.0-1.0) between Loss_Chain and Loss_Synth. model_architecture: (e.g., VMMoE, Transformer), including number of layers, heads, and expert count. [cite\_start]tmd_weight: The degree to which the 16D TMD vector 4 influences the model's attention or gating mechanisms. ### Inference Parameters (The "Thinking Loop") max_depth (int): The maximum number of recursive "thought" steps (e.g., 50) the LVM can take before forcing a stop. Prevents infinite loops. stop_threshold (float): A cosine similarity threshold. The loop stops if an emergent concept e_k+1 is highly similar to e_k (i.e., "conceptual convergence") or a pre-trained "stop" vector. context_management (enum): FIFO: Oldest vectors are dropped as new ones are added. Summarize: The LVM periodically synthesizes its own context into a new, single vector. Full: All vectors are retained (risk of context overflow). [cite\_start]TMD_bias_weights (vector): A 16D vector 5 applied at inference to guide the thinking loop. [cite\_start]A high weight on Domain: 15 (Software) 6 would bias the LVM to "think like a programmer." ## 7\. ❌ Out of Scope for this PRD V2T Decoder: The architecture and training of the Stage 3 (Vector-to-Text) model. Text Encoder: The selection and fine-tuning of the Stage 1 (Text-to-Vector) model. Data Sourcing/ETL: The physical downloading, cleaning, and vectorization of the Causal Corpus. This PRD defines the data, but a separate plan will be required to acquire* it.$$