Christian Nennemann
d6beb9c0a0
Gap-to-Draft Pipeline (ietf pipeline): - Context builder assembles ideas, RFC foundations, similar drafts, ecosystem vision - Generator produces outlines + sections using rich context with Claude - Quality gates: novelty (embedding similarity), references, format, self-rating - Family coordinator generates 5-draft ecosystem (AEM/ATD/HITL/AEPB/APAE) - I-D formatter with proper headers, references, 72-char wrapping Living Standards Observatory (ietf observatory): - Source abstraction with IETF + W3C fetchers - 7-step update pipeline: snapshot, fetch, analyze, embed, ideas, gaps, record - Static GitHub Pages dashboard (explorer, gap tracker, timeline) - Weekly CI/CD automation via GitHub Actions Also includes: - 361 drafts (expanded from 260 with 6 new keywords), 403 authors, 1,262 ideas, 12 gaps - Blog series (8 posts planned), reports, arXiv paper figures - Agent team infrastructure (CLAUDE.md, scripts, dev journal) - 5 new DB tables, schema migration, ~15 new query methods Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
91 lines
4.2 KiB
Markdown
91 lines
4.2 KiB
Markdown
# 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) |