# Paper Tables

This file contains draft tables for the manuscript.

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## Table 1 — Research dataset splits

| Split | Rows | Purpose |
|---|---:|---|
| `train_base` | 26,357 | Unaugmented training after OOD holdouts |
| `train_sota` | 32,357 | Training split with lifecycle/adversarial upsampling and multi-turn wrappers |
| `validation` | 1,547 | Validation during training |
| `test_in_distribution` | 1,455 | In-distribution test |
| `test_template_ood` | 3,503 | Held-out prompt-template family |
| `test_use_case_ood` | 4,341 | Held-out use cases |
| `test_sector_ood` | 4,579 | Held-out sectors |
| `test_adversarial` | 33 | Held-out adversarial rejection examples |

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## Table 2 — Qwen3 token-length audit

| Statistic | Tokens |
|---|---:|
| Mean | 754.1 |
| p50 | 705 |
| p95 | 1293 |
| p99 | 1300 |
| Max | 1316 |
| Fit under 2048 | 100% |

Interpretation: `max_length=2048` is safe for Qwen3-8B on this dataset.

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## Table 3 — Stage-1 training configuration

| Item | Value |
|---|---|
| Base model | `Qwen/Qwen3-8B` |
| Training method | QLoRA SFT |
| Quantization | 4-bit NF4 + double quantization |
| LoRA rank | 64 |
| LoRA alpha | 16 |
| LoRA dropout | 0.05 |
| Target modules | `all-linear` |
| Max length | 2048 |
| Loss | Assistant-only SFT loss |
| Learning rate | 2e-4 |
| Scheduler | constant |
| Optimizer | paged AdamW 32-bit |
| Gradient checkpointing | enabled |
| Hardware | RTX 6000 Ada 48/50GB |
| Train split | `train_sota` |

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## Table 4 — Stage-1 raw metrics

| Split | JSON parse | Exact match | Field F1 | KPI presence |
|---|---:|---:|---:|---:|
| `test_in_distribution` | 1.0000 | 0.0227 | 0.6868 | 0.7973 |
| `test_template_ood` | 1.0000 | 0.0014 | 0.6790 | 0.8062 |
| `test_use_case_ood` | 0.9998 | 0.0122 | 0.6825 | 0.7883 |
| `test_sector_ood` | 1.0000 | 0.0166 | 0.6610 | 0.7733 |
| `test_adversarial` | 1.0000 | 0.9697 | 0.9697 | 1.0000 |

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## Table 5 — Stage-1 normalized metrics

| Split | JSON parse | Normalized field F1 | Normalized key F1 | Normalized exact |
|---|---:|---:|---:|---:|
| `test_in_distribution` | 1.0000 | 0.7956 | 0.9811 | 0.0351 |
| `test_template_ood` | 1.0000 | 0.7865 | 0.9801 | 0.0177 |
| `test_use_case_ood` | 0.9998 | 0.7907 | 0.9805 | 0.0253 |
| `test_sector_ood` | 1.0000 | 0.7697 | 0.9818 | 0.0293 |
| `test_adversarial` | 1.0000 | 0.9697 | 1.0000 | 0.9697 |

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## Table 6 — Stage-1 strong and weak target layers

| Target layer | Normalized field F1 range | Interpretation |
|---|---:|---|
| `tmf921` | 0.93–0.94 | Strong high-level intent object generation |
| `camara` | 0.81–0.87 | Strong after volatile-field normalization |
| `intent_3gpp` | 0.80–0.82 | Strong/moderate |
| `etsi_zsm` | 0.75–0.79 | Moderate/strong |
| `a1_policy` | 0.67–0.68 | Moderate, value fidelity remains limited |
| `o1_nrm` | 0.39–0.40 | Weak value fidelity despite correct structure |
| `tmf921_lifecycle_report` | 0.15–0.18 | Weak, likely measurement/simulation mismatch |
| `tmf921_lifecycle_monitor` | 0.39–0.52 | Weak/mixed |

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## Table 7 — Stage 1 vs Stage 2 global comparison

| Split | Stage 1 norm field F1 | Stage 2 norm field F1 | Delta | Stage 1 norm key F1 | Stage 2 norm key F1 | Delta |
|---|---:|---:|---:|---:|---:|---:|
| `test_in_distribution` | 0.7956 | 0.7952 | -0.0003 | 0.9811 | 0.9796 | -0.0014 |
| `test_template_ood` | 0.7865 | 0.7855 | -0.0010 | 0.9801 | 0.9786 | -0.0015 |
| `test_use_case_ood` | 0.7907 | 0.7895 | -0.0012 | 0.9805 | 0.9787 | -0.0018 |
| `test_sector_ood` | 0.7697 | 0.7694 | -0.0002 | 0.9818 | 0.9809 | -0.0009 |
| `test_adversarial` | 0.9697 | 0.9596 | -0.0101 | 1.0000 | 0.9697 | -0.0303 |

Decision: Stage 2 is diagnostic and not promoted.

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## Table 8 — Stage 1 vs Stage 2 weak-layer comparison

| Split | Layer | Stage 1 | Stage 2 | Delta |
|---|---|---:|---:|---:|
| ID | `o1_nrm` | 0.3927 | 0.3906 | -0.0021 |
| ID | `a1_policy` | 0.6837 | 0.6787 | -0.0050 |
| ID | `tmf921_lifecycle_report` | 0.1667 | 0.1889 | +0.0222 |
| ID | `tmf921_lifecycle_monitor` | 0.5172 | 0.4926 | -0.0246 |
| ID | `tmf921_lifecycle_scale` | 0.9345 | 0.9453 | +0.0108 |
| Template OOD | `o1_nrm` | 0.3976 | 0.3993 | +0.0017 |
| Template OOD | `a1_policy` | 0.6763 | 0.6758 | -0.0004 |
| Template OOD | `tmf921_lifecycle_report` | 0.1799 | 0.1905 | +0.0106 |
| Template OOD | `tmf921_lifecycle_scale` | 0.5363 | 0.5560 | +0.0197 |
| Use-case OOD | `o1_nrm` | 0.3936 | 0.3895 | -0.0042 |
| Use-case OOD | `a1_policy` | 0.6808 | 0.6786 | -0.0023 |
| Use-case OOD | `tmf921_lifecycle_report` | 0.1531 | 0.1981 | +0.0450 |
| Use-case OOD | `tmf921_lifecycle_monitor` | 0.3875 | 0.4187 | +0.0312 |
| Use-case OOD | `tmf921_lifecycle_scale` | 0.6993 | 0.7411 | +0.0418 |
| Sector OOD | `o1_nrm` | 0.3858 | 0.3888 | +0.0029 |
| Sector OOD | `a1_policy` | 0.6740 | 0.6763 | +0.0023 |
| Sector OOD | `tmf921_lifecycle_report` | 0.1763 | 0.1830 | +0.0067 |
| Sector OOD | `tmf921_lifecycle_monitor` | 0.4310 | 0.4696 | +0.0385 |
| Sector OOD | `tmf921_lifecycle_scale` | 0.7279 | 0.7437 | +0.0158 |

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## Table 9 — Limitations summary

| Limitation | Impact | Mitigation / future work |
|---|---|---|
| Synthetic data | May not reflect real operator language | Add expert/human-authored validation subset |
| No official standard validators | Cannot claim production compliance | Add TMF921/CAMARA/OpenAPI/YANG validators |
| O1 NRM weak value fidelity | Low-level RAN configuration unreliable | Add semantic evaluator and canonical labels |
| A1 policy moderate fidelity | Policy values may be wrong | Add policy-specific extractor/scorer |
| Lifecycle report/monitor weak | Measurement fields may be hard to reproduce | Use tolerance/semantic scoring |
| Exact match low | Raw exact match over-penalizes volatile fields | Report normalized metrics alongside raw |