# Category Co-occurrence Report
*Generated 2026-03-03 20:15 UTC — 361 drafts, 318 (88.1%) multi-category*

## Category Counts

| Category | Count | % of Drafts |
|:---------|------:|------------:|
| Data formats/interop | 148 | 41.0% |
| A2A protocols | 136 | 37.7% |
| Agent identity/auth | 121 | 33.5% |
| Autonomous netops | 98 | 27.1% |
| Policy/governance | 93 | 25.8% |
| Agent discovery/reg | 79 | 21.9% |
| ML traffic mgmt | 74 | 20.5% |
| AI safety/alignment | 45 | 12.5% |
| Model serving/inference | 42 | 11.6% |
| Human-agent interaction | 30 | 8.3% |
| Other AI/agent | 26 | 7.2% |

## Co-occurrence Matrix

Number of drafts assigned to both categories simultaneously.

| | Data | A2A | IdAuth | Auto | Policy | Disc | ML | Safe | Model | Human | Other |
|:---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| **Data** | **148** | 58 | 33 | 24 | 26 | 35 | 19 | 7 | 14 | 14 | 3 |
| **A2A** | 58 | **136** | 43 | 39 | 23 | 51 | 15 | 12 | 9 | 8 | 3 |
| **IdAuth** | 33 | 43 | **121** | 7 | 40 | 27 | 2 | 26 | 4 | 6 | 1 |
| **Auto** | 24 | 39 | 7 | **98** | 19 | 21 | 29 | 4 | 11 | 8 | 9 |
| **Policy** | 26 | 23 | 40 | 19 | **93** | 4 | 10 | 27 | 4 | 10 | 2 |
| **Disc** | 35 | 51 | 27 | 21 | 4 | **79** | 6 | - | 4 | 2 | 2 |
| **ML** | 19 | 15 | 2 | 29 | 10 | 6 | **74** | 3 | 23 | 4 | 4 |
| **Safe** | 7 | 12 | 26 | 4 | 27 | - | 3 | **45** | - | 5 | 1 |
| **Model** | 14 | 9 | 4 | 11 | 4 | 4 | 23 | - | **42** | - | 3 |
| **Human** | 14 | 8 | 6 | 8 | 10 | 2 | 4 | 5 | - | **30** | 1 |
| **Other** | 3 | 3 | 1 | 9 | 2 | 2 | 4 | 1 | 3 | 1 | **26** |

## Top 20 Co-occurrences

| # | Category A | Category B | Count | Jaccard Index |
|--:|:-----------|:-----------|------:|--------------:|
| 1 | A2A protocols | Data formats/interop | 58 | 0.257 |
| 2 | A2A protocols | Agent discovery/reg | 51 | 0.311 |
| 3 | A2A protocols | Agent identity/auth | 43 | 0.201 |
| 4 | Agent identity/auth | Policy/governance | 40 | 0.230 |
| 5 | A2A protocols | Autonomous netops | 39 | 0.200 |
| 6 | Agent discovery/reg | Data formats/interop | 35 | 0.182 |
| 7 | Agent identity/auth | Data formats/interop | 33 | 0.140 |
| 8 | Autonomous netops | ML traffic mgmt | 29 | 0.203 |
| 9 | AI safety/alignment | Policy/governance | 27 | 0.243 |
| 10 | Agent discovery/reg | Agent identity/auth | 27 | 0.156 |
| 11 | AI safety/alignment | Agent identity/auth | 26 | 0.186 |
| 12 | Data formats/interop | Policy/governance | 26 | 0.121 |
| 13 | Autonomous netops | Data formats/interop | 24 | 0.108 |
| 14 | ML traffic mgmt | Model serving/inference | 23 | 0.247 |
| 15 | A2A protocols | Policy/governance | 23 | 0.112 |
| 16 | Agent discovery/reg | Autonomous netops | 21 | 0.135 |
| 17 | Autonomous netops | Policy/governance | 19 | 0.110 |
| 18 | Data formats/interop | ML traffic mgmt | 19 | 0.094 |
| 19 | A2A protocols | ML traffic mgmt | 15 | 0.077 |
| 20 | Data formats/interop | Model serving/inference | 14 | 0.080 |

## AI Safety/Alignment Coupling Analysis (45 drafts)

Is AI safety structurally isolated from other categories?

| Co-occurring Category | Count | % of Safety Drafts | Jaccard |
|:----------------------|------:|-------------------:|--------:|
| Policy/governance | 27 | 60.0% | 0.243 |
| Agent identity/auth | 26 | 57.8% | 0.186 |
| A2A protocols | 12 | 26.7% | 0.071 |
| Data formats/interop | 7 | 15.6% | 0.038 |
| Human-agent interaction | 5 | 11.1% | 0.071 |
| Autonomous netops | 4 | 8.9% | 0.029 |
| ML traffic mgmt | 3 | 6.7% | 0.026 |
| Other AI/agent | 1 | 2.2% | 0.014 |

**Verdict**: AI safety drafts have 85 co-occurrence links across 8 categories (1.9 avg links per safety draft). 
Safety is **well-coupled** with other categories, particularly policy/governance and identity/auth.

## Category Clusters

Groups of categories that form natural clusters based on co-occurrence.

- **A2A protocols** clusters with: Agent discovery/reg (lift 1.7x)
- **Agent identity/auth** clusters with: AI safety/alignment (lift 1.7x)
- **Policy/governance** clusters with: AI safety/alignment (lift 2.3x)
- **Agent discovery/reg** clusters with: A2A protocols (lift 1.7x)
- **ML traffic mgmt** clusters with: Model serving/inference (lift 2.7x)
- **AI safety/alignment** clusters with: Policy/governance (lift 2.3x), Agent identity/auth (lift 1.7x)
- **Model serving/inference** clusters with: ML traffic mgmt (lift 2.7x)