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0b7ee79 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # GENERATION VALIDATION REPORT: Autoregressive Decoding Audit
**Date**: September 17, 2026
**Auditor**: Senior ML Systems Engineer & Research Software Auditor
**Final Status**: **`GENERATION_PASS`**
---
## 1. Generation Interface Verification
The repository provides generation interfaces at two levels:
1. **Model-Level Method**: `QTensorFormer.generate()` in `src/models.py` (with `AdaptiveKVCache`).
2. **Hugging Face Integration**: `QTensorFormerForCausalLM` in `src/hf_model.py` (inherits `transformers.GenerationMixin`).
3. **Pure PyTorch Greedy Loop**: Evaluated via standard causal next-token prediction `argmax(logits[:, -1, :])` directly on the model's `forward()` output contract.
---
## 2. Deterministic Reproducibility Audit
Deterministic greedy decoding was executed twice from identical initial input prompt `[10, 25, 30]` with `seed=42`:
### Test Run Comparison
- **`src.models.QTensorFormer`**:
- Run 1 Tokens: `[10, 25, 30, 5278, 5278, 5278, 5278, 5278]`
- Run 2 Tokens: `[10, 25, 30, 5278, 5278, 5278, 5278, 5278]`
- Match: **100% Identical (Zero divergence)**
- **`q_tensor_former.QTensorFormer`**:
- Run 1 Tokens: `[10, 25, 30, 2856, 1538, 2498, 2498, 2498]`
- Run 2 Tokens: `[10, 25, 30, 2856, 1538, 2498, 2498, 2498]`
- Match: **100% Identical (Zero divergence)**
---
## 3. Per-Token Sequence Growth & Dimension Trace
| Step | Current Sequence Length ($T$) | Logits Shape | Selected Next Token ID | Next Sequence Length ($T+1$) | Invariant Check |
| :---: | :---: | :---: | :---: | :---: | :---: |
| Prompt | 3 | N/A | N/A | 3 | Initial prompt |
| 1 | 3 | `[1, 3, 10000]` | $5278$ | 4 | **VALID** |
| 2 | 4 | `[1, 4, 10000]` | $5278$ | 5 | **VALID** |
| 3 | 5 | `[1, 5, 10000]` | $5278$ | 6 | **VALID** |
| 4 | 6 | `[1, 6, 10000]` | $5278$ | 7 | **VALID** |
| 5 | 7 | `[1, 7, 10000]` | $5278$ | 8 | **VALID** |
---
## 4. Verification Checklist
- [x] Input IDs semantics: Int64 tensor indexing within $[0, V-1]$.
- [x] Logits semantics: Real-valued unnormalized log-probabilities over vocabulary.
- [x] Next-token selection: Greedy argmax along vocabulary dimension.
- [x] Sequence growth: Monotonic single-token concatenation.
- [x] Determinism: Repeated execution with identical prompt and seed yields identical sequences.
- [x] Numerical stability: No NaN or Inf encountered during unrolling.
**FINAL STATUS**: **`GENERATION_PASS`**
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