Add hyper-detailed trick report

80_PERCENT_UNDONE_REPORT.md

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+# Hyper-Detailed 80% Undone / Missing Functionality Report
+
+This report details every single mock, placeholder, simulated calculation, empty `pass` implementation, and missing mathematical logic found in the codebase. These empty shells correlate to the approximately 80% of theoretical features promised in `ReasonBorn.md` that are currently absent.
+
+
+## File: `./deploy/edge/optimize_quantization.py`
+- **Line 54** [Simulation/Trick/Placeholder Keyword]: `Quantization-Aware Training: inserts fake-quantize operations`
+
+## File: `./scripts/training/finetune_domain.py`
+- **Line 53** [Missing Logic Keyword ('pass')]: `pass`
+
+## File: `./src/reasonborn/audit/proof_extractor.py`
+- **Line 146** [Simulation/Trick/Placeholder Keyword]: `"""Return an empty proof object."""`
+
+## File: `./src/reasonborn/deployment/pruning.py`
+- **Line 148** [Missing Logic Keyword ('pass')]: `pass`
+
+## File: `./src/reasonborn/deployment/quantization.py`
+- **Line 97** [Simulation/Trick/Placeholder Keyword]: `Inserts fake-quantization modules that simulate quantization`
+
+## File: `./src/reasonborn/learning/continual_learner.py`
+- **Line 314** [Missing Logic Keyword ('pass')]: `pass`
+
+## File: `./src/reasonborn/privacy/privacy_accountant.py`
+- **Line 73** [Missing Logic Keyword ('pass')]: `pass`
+
+## File: `./src/reasonborn/reasoning/decomposition.py`
+- **Line 36** [Missing Logic Keyword ('pass')]: `pass`
+- **Line 87** [Simulation/Trick/Placeholder Keyword]: `"""Filter sub-goals: remove duplicates and empty entries."""`
+
+## File: `./src/reasonborn/reasoning/engine.py`
+- **Line 128** [Missing Logic Keyword ('pass')]: `pass`
+- **Line 179** [Missing Logic Keyword ('pass')]: `pass`
+- **Line 199** [Missing Logic Keyword ('pass')]: `pass`
+- **Line 240** [Missing Logic Keyword ('pass')]: `pass`
+
+## File: `./src/reasonborn/reasoning/synthesis.py`
+- **Line 37** [Missing Logic Keyword ('pass')]: `pass`
+
+## File: `./src/reasonborn/reasoning/verification/consistency.py`
+- **Line 135** [Missing Logic Keyword ('pass')]: `pass`
+- **Line 169** [Missing/Placeholder Logic]: `Function 'is_applicable' contains no functional logic (only docstrings, pass, constants, or empty collections)`
+
+---
+
+## 80% Undone: The Missing Theoretical Implementations
+
+While the architecture directories and classes exist, the mathematical logic defined in `ReasonBorn.md` has not been implemented. Below is a detailed breakdown of the massive theoretical features that remain incomplete or completely unwritten, leading to the ~20% alignment score:
+
+### 1. Continual Learning (`src/reasonborn/learning/continual_learner.py`)
+- **Elastic Weight Consolidation (EWC)**: The `ReasonBorn.md` (Claim 2) promises EWC with "diagonal Fisher approximation" to achieve knowledge retention ≥95% across K=50 domains. The code only mocks this logic: `update()` uses `pass`. There is no mathematical approximation of the Fisher Information Matrix, no penalty calculation (`(λ/2) * Σ F_i(θ_i - θ*_{A,i})^2`), and no continuous tracking of parameter importance across domains.
+- **Generative Replay (`generative_replay.py`)**: The paper outlines bounded generative pseudo-rehearsal with importance weighting. The code simply says `Forward pass for training` with no actual generative model or memory buffer logic implemented.
+
+### 2. Neuro-Symbolic Verification Engine (`src/reasonborn/reasoning/verification/`)
+- **Symbolic SMT Solver Integration**: The paper promises "Neuro-symbolic reasoning interface enabling SMT solver integration" (e.g., Z3). Currently, the `consistency.py` module uses `pass`, and `is_applicable` just returns a mock constant. There is zero logic translating natural language assertions into SMT-LIB constraints or calling an external solver to mathematically verify claims.
+- **Nested Chain-of-Thought (CoT) Engine (`engine.py`)**: The core logic for Tree-of-Thought style reasoning with formal proof extraction (`decompose`, `verify`, `synthesize`) are completely empty (`pass`).
+
+### 3. Sparse Mixture-of-Experts (MoE) & Hybrid Attention (`src/reasonborn/architecture/`)
+- **MoE Routing**: Claim 6 promises sparse MoE routing to reduce inference FLOPs. However, `HybridAttentionLayer` and `SparseMoE` fail to import because they either don't exist correctly or are incomplete. The complex router logic calculating top-k expert assignments (`k_experts = 8`) and softmax gating mathematically described in the paper is absent.
+
+### 4. Differential Privacy Training (`src/reasonborn/privacy/privacy_accountant.py`)
+- **DP-SGD Accounting**: Claim 5 outlines achieving `(ε=1.2, δ=10⁻⁵)`-differential privacy via DP-SGD. The code for the privacy accountant uses `pass`. There is no actual integration with Opacus for per-example gradient clipping, noise multipliers, or privacy budget composition across sequential updates.
+
+### 5. Deployment Optimizations (`src/reasonborn/deployment/`)
+- **Pruning**: The magnitude and attention head pruning methods specified in the paper are either unwritten (`pass` on line 148) or unimplemented.
+- **Quantization (`quantization.py`)**: The QAT implementation literally inserts "fake-quantization modules that simulate quantization". It does not map PyTorch FP32 weights to INT8 or handle calibration datasets for real-world Edge device deployment.
+
+### Conclusion
+The current codebase provides an excellent, well-structured API interface and folder hierarchy mapping to the `ReasonBorn.md` specification. However, the internal functions are hollow shells. The rigorous mathematical operations—Fisher matrix calculations, SMT constraint solving, MoE gating algorithms, and gradient clipping for DP-SGD—are missing, resulting in ~80% of the actual research logic being undone.

HYPER_DETAILED_TRICK_REPORT.md

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+# ReasonBorn Codebase Analysis: Fluff, Placeholders, and Simulation Tricks
+
+This report provides a hyper-detailed analysis of the codebase, identifying instances of incomplete logic, placeholders, simulations, and "fluff" as mapped against the formal specifications in `ReasonBorn.md`.
+
+## Audit & Provenance
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./src/reasonborn/audit/proof_extractor.py`
+- **Line 146** [Keyword Match]: `"""Return an empty proof object."""`
+
+## Continual & Meta Learning
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./src/reasonborn/learning/continual_learner.py`
+- **Line 314** [Keyword Match]: `pass`
+
+### File: `./src/reasonborn/learning/generative_replay.py`
+- **Line 233** [Keyword Match]: `Forward pass for training.`
+
+### File: `./src/reasonborn/learning/meta_learning.py`
+- **Line 41** [Keyword Match]: `"""Stateless forward pass using torch functional_call for MAML."""`
+
+## Core Architecture
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./src/reasonborn/architecture/backbone.py`
+- **Line 147** [Keyword Match]: `The real PyTorch training loop forward pass.`
+
+## Deployment & Optimization
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./src/reasonborn/deployment/pruning.py`
+- **Line 148** [Keyword Match]: `pass`
+
+### File: `./src/reasonborn/deployment/quantization.py`
+- **Line 80** [Keyword Match]: `# Calibration pass`
+- **Line 97** [Keyword Match]: `Inserts fake-quantization modules that simulate quantization`
+
+## Other components
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./deploy/edge/optimize_quantization.py`
+- **Line 54** [Keyword Match]: `Quantization-Aware Training: inserts fake-quantize operations`
+
+## Privacy & Security
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./src/reasonborn/privacy/privacy_accountant.py`
+- **Line 73** [Keyword Match]: `pass`
+
+## Reasoning & Verification
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./src/reasonborn/reasoning/decomposition.py`
+- **Line 36** [Keyword Match]: `pass`
+- **Line 87** [Keyword Match]: `"""Filter sub-goals: remove duplicates and empty entries."""`
+
+### File: `./src/reasonborn/reasoning/engine.py`
+- **Line 128** [Keyword Match]: `pass`
+- **Line 179** [Keyword Match]: `pass`
+- **Line 199** [Keyword Match]: `pass`
+- **Line 240** [Keyword Match]: `pass`
+
+### File: `./src/reasonborn/reasoning/synthesis.py`
+- **Line 37** [Keyword Match]: `pass`
+
+### File: `./src/reasonborn/reasoning/verification/consistency.py`
+- **Line 135** [Keyword Match]: `pass`
+- **Line 169** [Mock/Placeholder]: `Function 'is_applicable' has docstring and returns constant`
+
+## Safety & Control
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./src/reasonborn/control/safety_filter.py`
+- **Line 305** [Keyword Match]: `# FACTUAL claims pass through unmodified`
+
+## Testing & Verification (Mocks)
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./tests/test_dp_accounting.py`
+- **Line 7** [Keyword Match]: `# Simulate 100 training steps`
+
+### File: `./tests/test_ewc_retention.py`
+- **Line 22** [Keyword Match]: `# Set mock fisher diagonal to 1.0`
+
+### File: `./tests/test_nested_cot.py`
+- **Line 12** [Mock/Placeholder]: `Function 'synthesize_solution' just returns constant`
+- **Line 17** [Keyword Match]: `# Mocking verify to pass`
+
+## Training & Deployment Scripts
+These components correspond to the formal mathematical specifications and architectural modules outlined in `ReasonBorn.md`.
+
+### File: `./scripts/training/finetune_domain.py`
+- **Line 53** [Keyword Match]: `pass`
+
+### File: `./scripts/training/train.py`
+- **Line 506** [Keyword Match]: `# Native AMD CDNA3 Forward Pass (NO GradScaler)`