Backlog — Sprint Retrospectives

Generated by the FAIRE backlog agent (agents/src/frontier_agents/retrospective.py). Each section is one cycle's scrum-style retro: ✓ = auto-applied, ⏳ = queued for next cycle, ○ = deferred to human review. Newest first. Distinct from the per-page Agent Changelog and the human-written Learnings Log.

Cycle — 2026-05-27 14:38 EDT — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.76 · First-try: 60%

🟢 What went well

(no observations this cycle)

🔴 What went wrong

(none flagged)

🟡 What needs depth

(coverage feels adequate)

➕ What to add (auto-applies marked ✓, others queued for review)

(no additions proposed)

⚙️ Process improvements (human review)

• (LLM proposer failed: APIStatusError: Error code: 402 - {'error': {'message': 'Insufficient credits. Add more using https://openrouter.ai/settings/credits', 'code': 402}})

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `04-neural-networks-deep-learning` | 4 | 0.77 | `critic-info-architecture` (0.55) | | `05-statistical-probabilistic-ml` | 2 | 0.79 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 18 | 0.76 | `critic-info-architecture` (0.51) | **Recurring critic issues (top 5)** - × 9 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 8 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

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Cycle — 2026-05-27 14:18 EDT — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.76 · First-try: 60%

🟢 What went well

(no observations this cycle)

🔴 What went wrong

(none flagged)

🟡 What needs depth

(coverage feels adequate)

➕ What to add (auto-applies marked ✓, others queued for review)

(no additions proposed)

⚙️ Process improvements (human review)

• (LLM proposer failed: APIStatusError: Error code: 402 - {'error': {'message': 'Insufficient credits. Add more using https://openrouter.ai/settings/credits', 'code': 402}})

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `04-neural-networks-deep-learning` | 4 | 0.77 | `critic-info-architecture` (0.55) | | `05-statistical-probabilistic-ml` | 2 | 0.79 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 18 | 0.76 | `critic-info-architecture` (0.51) | **Recurring critic issues (top 5)** - × 9 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 8 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

---

Cycle — 2026-05-27 13:52 EDT — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.76 · First-try: 60%

🟢 What went well

(no observations this cycle)

🔴 What went wrong

(none flagged)

🟡 What needs depth

(coverage feels adequate)

➕ What to add (auto-applies marked ✓, others queued for review)

(no additions proposed)

⚙️ Process improvements (human review)

• (LLM proposer failed: APIStatusError: Error code: 402 - {'error': {'message': 'Insufficient credits. Add more using https://openrouter.ai/settings/credits', 'code': 402}})

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `04-neural-networks-deep-learning` | 4 | 0.77 | `critic-info-architecture` (0.55) | | `05-statistical-probabilistic-ml` | 2 | 0.79 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 18 | 0.76 | `critic-info-architecture` (0.51) | **Recurring critic issues (top 5)** - × 9 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 8 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

---

Cycle — 2026-05-27 13:52 EDT — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.76 · First-try: 60%

🟢 What went well

(no observations this cycle)

🔴 What went wrong

(none flagged)

🟡 What needs depth

(coverage feels adequate)

➕ What to add (auto-applies marked ✓, others queued for review)

(no additions proposed)

⚙️ Process improvements (human review)

• (LLM proposer failed: APIStatusError: Error code: 402 - {'error': {'message': 'Insufficient credits. Add more using https://openrouter.ai/settings/credits', 'code': 402}})

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `04-neural-networks-deep-learning` | 4 | 0.77 | `critic-info-architecture` (0.55) | | `05-statistical-probabilistic-ml` | 2 | 0.79 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 18 | 0.76 | `critic-info-architecture` (0.51) | **Recurring critic issues (top 5)** - × 9 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 8 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

---

Cycle — 2026-05-27 07:22 EDT — Retrospective

Runs analyzed: 30 · Approved: 24 · Errored: 4 · Avg conf: 0.75 · First-try: 50%

🟢 What went well

• Overall approval high: 24 of 30 runs approved (80%), showing steady content quality and reviewer alignment.
• Average confidence across the cycle is healthy at 0.749 — pages are generally produced with reasonable model certainty.
• Several tracks are producing high-confidence pages: 04-neural-networks-deep-learning (avg_confidence 0.781) and 05-statistical-probabilistic-ml (0.783).
• Citation capture is strong in volume: 63 unique arXiv IDs cited and 25 pages include arXiv references; the top-cited paper (2509.06733) appears 10 times, indicating good reuse of key sources.
• Critics are catching architecture and build-nudge issues consistently (see recurring critic counts), which means the critic pipeline is working to surface structural problems.

🔴 What went wrong

• Four runs errored this cycle — investigate error modes and whether they come from specific tracks or pipes.
• First-try approval is only 50% — half the pages require at least one revision pass, indicating friction in initial outputs.
• Recurring info-architecture failures: 'Connected topics' missing (11 occurrences) and 'Where this concept appears' missing on core-concept pages (8 occurrences).
• Recurring build-nudge failures: many pages lack a usable 'What can you build next' section (two distinct failure patterns totalling 14 counts).
• critic-coverage flagged incomplete 'Mathematical foundations' lists on multiple pages (5 occurrences) — mathematical coverage is often present but not structured as a dedicated list.

🟡 What needs depth

• 08-causal-statistical-inference only has 1 page — the track is undersupported and needs more substantive concept pages.
• 05-statistical-probabilistic-ml has only 3 pages; while confidence is good, the track is shallow and could benefit from more core-concept coverage.
• Info-architecture is the weakest critic across all tracked areas (weakest_critic_avg as low as 0.5) — pages lack standard linking/context sections that would increase utility.
• High concentration of citations: 63 unique arXiv IDs but only 25 pages reference arXiv — some pages may be missing citation anchors where needed.
• The 50% first-try approval implies several topics needed multiple revision cycles; focus deeper editorial effort on those pages to reduce churn.

➕ What to add (auto-applies marked ✓, others queued for review)

• ⏳ Increase priority for fixes in 09-algorithms-systems-for-ai so existing pages with info-architecture gaps are remediated quickly
• evidence: 09 has many pages (16) but weakest-info-architecture avg is low (0.525) and recurring 'Connected topics' / 'Where this concept appears' issues are frequent
• refs: 09-algorithms-systems-for-ai, critic-info-architecture
• risk: safe · auto_apply: True · type: queue-priority-bump
• ✓ Add a reusable 'connected topics' stub for core-concept pages in 09-algorithms-systems-for-ai to standardize the missing section
• evidence: Recurring critic-info-architecture: 'Connected topics' section missing entirely (11 occurrences)
• refs: critic-info-architecture, 09-algorithms-systems-for-ai
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Add a reusable 'what can you build next' stub for neural-networks pages to repair the repeated build-nudge failures
• evidence: critic-build-nudge flagged missing 'What can you build next' sections across pages (14 combined occurrences)
• refs: critic-build-nudge, 04-neural-networks-deep-learning
• risk: safe · auto_apply: True · type: stub-seed
• ○ Propose a 'deep-learning-foundations' learning arc for 04-neural-networks-deep-learning to convert isolated concept pages into a coherent progression
• evidence: 04 has 5 substantive pages (n_pages=5) and good average confidence (0.781); the track's existing concepts naturally form a teachable sequence
• refs: 04-neural-networks-deep-learning
• risk: moderate · auto_apply: False · type: arc-proposal
• ○ Propose a 'scaling-and-systems' arc for 09-algorithms-systems-for-ai to tie the track's system-level concepts into a deployment-focused path
• evidence: 09 has 16 pages (n_pages=16) — ample substrate for an arc that stitches algorithmic/system topics into a deployable pipeline
• refs: 09-algorithms-systems-for-ai
• risk: moderate · auto_apply: False · type: arc-proposal

⚙️ Process improvements (human review)

• Raise the priority of info-architecture checks in the critic ordering: enforce presence of 'Connected topics' and 'Where this concept appears' on core-concept pages as a hard warning rather than a soft note.
• Strengthen the build-nudge critic: require a non-generic 'What can you build next' entry (pattern-match against placeholders like truncated 'Build it' or 'generic G').
• Add a preflight lint that flags pages ending abruptly or with truncated sentences (these patterns showed up in build-nudge failures).
• Introduce a small 'first-try quality' ramp: add an extra local validation pass on pages that fail first-try approval to reduce iteration count (target: lift first-try approval from 50% → 65%).
• Create a lightweight track-depth dashboard alert: flag tracks with <3 pages (e.g., 08-causal-statistical-inference) so editorial time is allocated to seeding more concepts.
• When assigning auto-priority bumps, tie them to concrete critic metrics (e.g., if weakest_critic_avg < 0.6 and n_pages > 5) so remediation work is focused where scale exists.

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `04-neural-networks-deep-learning` | 5 | 0.78 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 3 | 0.78 | `critic-info-architecture` (0.50) | | `08-causal-statistical-inference` | 1 | 0.81 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 16 | 0.75 | `critic-info-architecture` (0.53) | **Recurring critic issues (top 5)** - × 11 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 9 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 5 — critic-coverage: The 'Mathematical foundations' section is present but lacks a dedicated list of - × 5 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a generic G

---

Cycle — 2026-05-27 07:14 EDT — Retrospective

Runs analyzed: 30 · Approved: 24 · Errored: 4 · Avg conf: 0.76 · First-try: 57%

🟢 What went well

• High overall approval: 24/30 runs approved (~80%) — pipeline is producing usable pages at scale.
• First-try approval rate of 57%: more than half of pages pass without multiple revision cycles.
• Average confidence across the cycle is solid (0.762), indicating generally reliable model output.
• Per-track strong performers: 08-causal-statistical-inference shows highest avg confidence (0.812); 04-neural-networks-deep-learning (0.768) and 09-algorithms-systems-for-ai (0.762) also healthy.
• Citation footprint is broad: 63 unique arXiv IDs cited and 26 pages include arXiv references — reviewers are bringing in primary sources.
• Critics are active and catching structural issues (critic-info-architecture and critic-build-nudge flagged missing sections), which means critics are running and surfacing real problems to fix.

🔴 What went wrong

• Four runs errored — investigate root causes for those errors to prevent silent failures.
• Recurring critic failures concentrated on information architecture: 'Connected topics' missing for core-concept pages (13 occurrences) and 'Where this concept appears' missing (9+7 occurrences).
• critic-build-nudge flagged missing 'What can you build next' sections 7 times; pages ending with a fragmented 'Build it' prompt indicate output truncation or template misuse.
• critic-coverage flagged that 'Mathematical foundations' sections exist but lack dedicated lists of prerequisites or further fundamentals (6 occurrences).
• Unresolved wikilinks present (curriculum-resampling, collective-communication, gradient-bucketing) — these create broken navigation and indicate missing connective pages.
• Per-track weak critic signals: critic-info-architecture averages around 0.5–0.54 in multiple tracks, showing a systemic architecture shortfall rather than isolated cases.
• First-try approval rate at 57% is good but leaves substantial rework; we should reduce the churn by fixing the frequent structural failure modes above.

🟡 What needs depth

• 08-causal-statistical-inference only produced 1 page this cycle — track is underrepresented and should get additional coverage.
• 05-statistical-probabilistic-ml has only 3 pages — borderline thin for a full track; consider expanding core-concepts and applied pages.
• Repeated absence of 'Where this concept appears' and 'Connected topics' implies surface-level coverage; pages need richer cross-linking and contextual anchors.
• Some 'Mathematical foundations' sections are present but too shallow (critic-coverage); add explicit prerequisite lists and core lemmas/formulas where applicable.
• Citation distribution: 26 pages include arXiv citations but top-cited papers concentrate references (e.g., arXiv:1810.04805 cited 10x). We should broaden author/primary-source linking and create author anchors for heavily cited papers.

➕ What to add (auto-applies marked ✓, others queued for review)

• ✓ Create a stub page for 'curriculum-resampling' in track 09 to resolve broken references.
• evidence: Referenced twice by 09-algorithms-systems-for-ai/reinforcement-learning-schedulers; currently unresolved wikilink.
• refs: 09-algorithms-systems-for-ai/reinforcement-learning-schedulers
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'collective-communication' in track 09 to resolve broken references.
• evidence: Referenced twice by 09-algorithms-systems-for-ai/data-parallelism; currently unresolved wikilink.
• refs: 09-algorithms-systems-for-ai/data-parallelism
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'gradient-bucketing' in track 09 to resolve broken references.
• evidence: Referenced twice by 09-algorithms-systems-for-ai/data-parallelism; currently unresolved wikilink.
• refs: 09-algorithms-systems-for-ai/data-parallelism
• risk: safe · auto_apply: True · type: stub-seed
• ⏳ Bump queue priority for 09-algorithms-systems-for-ai/data-parallelism to expedite fixes for unresolved links and structural issues.
• evidence: Data-parallelism references unresolved wikilinks (collective-communication, gradient-bucketing) and is implicated in multiple critic-info-architecture flags.
• refs: 09-algorithms-systems-for-ai/data-parallelism
• risk: safe · auto_apply: True · type: queue-priority-bump
• ⏳ Bump queue priority for 09-algorithms-systems-for-ai/reinforcement-learning-schedulers to expedite fixes for unresolved curriculum-resampling references.
• evidence: Reinforcement-learning-schedulers references unresolved 'curriculum-resampling' twice and shows critic flags.
• refs: 09-algorithms-systems-for-ai/reinforcement-learning-schedulers
• risk: safe · auto_apply: True · type: queue-priority-bump
• ○ Seed an author-page for the authors of arXiv:1810.04805 to anchor a frequently-cited paper.
• evidence: arXiv:1810.04805 is the top-cited paper in the cycle (10 citations); creating author anchors will improve citation navigation.
• refs: 1810.04805
• risk: moderate · auto_apply: False · type: author-page-seed
• ○ Seed an author-page for the authors of arXiv:2403.02349 to anchor a high-frequency citation.
• evidence: arXiv:2403.02349 is cited 8 times in this cycle; adding an author/paper anchor will help navigation and authority.
• refs: 2403.02349
• risk: moderate · auto_apply: False · type: author-page-seed

⚙️ Process improvements (human review)

• Add a hard schema requirement (preflight check) for core-concept pages: must include 'Connected topics' and 'Where this concept appears' sections; reject or nudge drafts that lack them.
• Revise critic-info-architecture messages to be prescriptive and include short templates/examples for the missing 'Connected topics' and 'Where this concept appears' sections to reduce rework.
• Create an explicit 'What can you build next' micro-template and inject via the build-nudge critic so pages don't end with truncated 'Build it' fragments.
• Add an automated pipeline step to detect unresolved wikilinks and auto-generate stub-seed tasks (or queue-priority bumps) so references are resolved faster.
• Raise the visibility of critic-coverage reports to authors: when 'Mathematical foundations' exists, require a small checklist (prereqs, key equations, further reading) before approval.
• Investigate the four errored runs: add more logging/context capture at run-failure points and add retries for transient infrastructure issues.
• Add per-track health alerts when any critic's average drops below 0.6 for two consecutive cycles; assign a short remediation sprint to fix systemic issues.
• Consider targeted content drives to deepen underrepresented tracks (08 and 05): create a small arc or seed list for each to produce 3–5 new concept pages next cycle.

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `04-neural-networks-deep-learning` | 6 | 0.77 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 3 | 0.75 | `critic-info-architecture` (0.50) | | `08-causal-statistical-inference` | 1 | 0.81 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 11 | 0.76 | `critic-info-architecture` (0.54) | **Unresolved wikilinks (stub-seed candidates)** - `[[curriculum-resampling]]` × 2 refs (from 09-algorithms-systems-for-ai/reinforcement-learning-schedulers, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers) - `[[collective-communication]]` × 2 refs (from 09-algorithms-systems-for-ai/data-parallelism, 09-algorithms-systems-for-ai/data-parallelism) - `[[gradient-bucketing]]` × 2 refs (from 09-algorithms-systems-for-ai/data-parallelism, 09-algorithms-systems-for-ai/data-parallelism) **Recurring critic issues (top 5)** - × 13 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 9 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 7 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 7 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 6 — critic-coverage: The 'Mathematical foundations' section is present but lacks a dedicated list of

---

Cycle — 2026-05-27 06:53 EDT — Retrospective

Runs analyzed: 30 · Approved: 24 · Errored: 4 · Avg conf: 0.76 · First-try: 57%

🟢 What went well

• Overall approval rate is strong: 24/30 runs approved (~80%) showing solid throughput.
• Average confidence across the cycle is healthy at 0.762, with several tracks achieving higher scores (08: 0.812, 04: 0.768).
• First-try approval rate of 0.57 indicates a majority of pages are approved on the first pass — keep encouraging high-quality authoring and clear reviewer guidance.
• Citation coverage: 63 unique arXiv IDs across 26 pages demonstrates good use of primary literature and cross-linking to research.
• Critics are catching structural and coverage problems (critic-info-architecture, critic-build-nudge, critic-coverage), showing the critic suite is effective at surfacing recurring content gaps.

🔴 What went wrong

• Four runs errored this cycle — investigate root causes for those errors to prevent repetition.
• First-try approval rate at 57% leaves room for improvement; 43% of runs required additional revisions.
• critic-info-architecture repeatedly flagged missing 'Connected topics' and 'Where this concept appears' sections (multiple counts), indicating authors often omit required core-concept structure.
• critic-build-nudge flagged missing 'What can you build next' sections (7 occurrences), causing predictable reviewer churn.
• critic-coverage flagged incomplete 'Mathematical foundations' lists (6 occurrences), suggesting shallow treatment of mathematical prerequisites in some pages.
• There are multiple unresolved wikilinks (10 distinct slugs) referenced across pages; missing pages cause broken navigation and scattered context.

🟡 What needs depth

• Track 08 (causal-statistical-inference) only has 1 page — good confidence but insufficient breadth for an arc.
• Track 05 (statistical-probabilistic-ml) has only 3 pages; consider expanding to provide fuller coverage.
• critic-info-architecture is the weakest critic across tracks (avg ~0.5); pages commonly miss 'Connected topics' and 'Where this concept appears' — depth and cross-link guidance needed.
• Several important foundational topics are missing pages (transformers, convex-optimization, automatic-differentiation, reinforcement-learning, etc.), indicating shallow coverage of core prerequisites.
• Citation usage clusters heavily around a few arXiv IDs (top three cited 10, 8, and 6 times) — broaden cited sources and add author/landmark pages to anchor recurring citations.

➕ What to add (auto-applies marked ✓, others queued for review)

• ✓ Create stub seed page for 'transformers' in the neural-networks track
• evidence: Unresolved wikilink 'transformers' referenced twice (from layer-normalization and mixture-of-experts).
• refs: transformers, 04-neural-networks-deep-learning/layer-normalization, 01-ai/mixture-of-experts
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'constrained-learning' in the algorithms/systems track
• evidence: Unresolved wikilink 'constrained-learning' referenced twice from differentiable-optimization.
• refs: constrained-learning, 09-algorithms-systems-for-ai/differentiable-optimization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'convex-optimization' in the algorithms/systems track
• evidence: Unresolved wikilink 'convex-optimization' referenced twice from differentiable-optimization.
• refs: convex-optimization, 09-algorithms-systems-for-ai/differentiable-optimization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'automatic-differentiation' in the algorithms/systems track
• evidence: Unresolved wikilink 'automatic-differentiation' referenced twice from differentiable-optimization.
• refs: automatic-differentiation, 09-algorithms-systems-for-ai/differentiable-optimization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'reinforcement-learning' in the algorithms/systems track
• evidence: Unresolved wikilink 'reinforcement-learning' referenced twice (differentiable-optimization and reward-modeling).
• refs: reinforcement-learning, 09-algorithms-systems-for-ai/differentiable-optimization, 01-ai/reward-modeling
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'tensor-cores' in the algorithms/systems track
• evidence: Unresolved wikilink 'tensor-cores' referenced twice (mixed-precision-training and quantization-basics).
• refs: tensor-cores, 09-algorithms-systems-for-ai/mixed-precision-training, 09-algorithms-systems-for-ai/quantization-basics
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'long-context-models' in the algorithms/systems track
• evidence: Unresolved wikilink 'long-context-models' referenced twice (attention-mechanisms and reinforcement-learning-schedulers).
• refs: long-context-models, 09-algorithms-systems-for-ai/attention-mechanisms, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'curriculum-learning' in the algorithms/systems track
• evidence: Unresolved wikilink 'curriculum-learning' referenced twice from reinforcement-learning-schedulers.
• refs: curriculum-learning, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'policy-gradient-theory' in the algorithms/systems track
• evidence: Unresolved wikilink 'policy-gradient-theory' referenced twice from reinforcement-learning-schedulers.
• refs: policy-gradient-theory, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create stub seed page for 'rlhf-infrastructure-overview' in the algorithms/systems track
• evidence: Unresolved wikilink 'rlhf-infrastructure-overview' referenced twice from reinforcement-learning-schedulers.
• refs: rlhf-infrastructure-overview, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers
• risk: safe · auto_apply: True · type: stub-seed
• ○ Propose an 'Reinforcement Learning Systems' arc in track 09 to group scheduling, optimization, and RL infra pages
• evidence: Multiple related RL and optimization concept pages (differentiable-optimization, reinforcement-learning-schedulers, policy-gradient-theory, rlhf-infrastructure-overview) exist in track 09.
• refs: 09-algorithms-systems-for-ai, 09-algorithms-systems-for-ai/differentiable-optimization, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers
• risk: moderate · auto_apply: False · type: arc-proposal
• ⏳ Bump queue priority for '09-algorithms-systems-for-ai/differentiable-optimization' to high so missing prerequisite pages are authored next
• evidence: Differentiable-optimization references multiple unresolved foundational topics (constrained-learning, convex-optimization, automatic-differentiation) which block page completeness.
• refs: 09-algorithms-systems-for-ai/differentiable-optimization, constrained-learning, convex-optimization, automatic-differentiation
• risk: safe · auto_apply: True · type: queue-priority-bump
• ○ Seed an author/landmark page for the most-cited arXiv cluster (start with arXiv:1810.04805) to anchor recurring citations
• evidence: arXiv:1810.04805 is the most-cited ID this cycle (10 citations) — creating an author/landmark page improves citation context and reuse.
• refs: 1810.04805, citation_health
• risk: moderate · auto_apply: False · type: author-page-seed

⚙️ Process improvements (human review)

• Tighten critic-info-architecture guidance: add explicit template fields and examples for 'Connected topics' and 'Where this concept appears' for core-concept pages; add a visible checklist item in the authoring UI.
• Update critic-build-nudge to require a minimal 'What can you build next' bullet list for application-focused pages; fail early during draft checks rather than only at review time.
• Strengthen critic-coverage: when 'Mathematical foundations' appears, require a short enumerated list of prerequisite topics or references (or downgrade confidence).
• Add an automated detection rule that turns unresolved wikilinks with reference_count >= 2 into suggested stub seeds in the author backlog (flag them to authors/editors).
• Add a reviewer guidance card to increase first-try approval rate: encourage authors to fill the critic-required structural sections before submitting (pre-submit linting or checklist).
• Investigate the four errored runs for systemic causes (infrastructure, timeouts, schema validation) and add telemetry so errors surface with stack traces for triage.

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `04-neural-networks-deep-learning` | 6 | 0.77 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 3 | 0.75 | `critic-info-architecture` (0.50) | | `08-causal-statistical-inference` | 1 | 0.81 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 11 | 0.76 | `critic-info-architecture` (0.54) | **Unresolved wikilinks (stub-seed candidates)** - `[[transformers]]` × 2 refs (from 04-neural-networks-deep-learning/layer-normalization, 01-ai/mixture-of-experts) - `[[constrained-learning]]` × 2 refs (from 09-algorithms-systems-for-ai/differentiable-optimization, 09-algorithms-systems-for-ai/differentiable-optimization) - `[[convex-optimization]]` × 2 refs (from 09-algorithms-systems-for-ai/differentiable-optimization, 09-algorithms-systems-for-ai/differentiable-optimization) - `[[automatic-differentiation]]` × 2 refs (from 09-algorithms-systems-for-ai/differentiable-optimization, 09-algorithms-systems-for-ai/differentiable-optimization) - `[[reinforcement-learning]]` × 2 refs (from 09-algorithms-systems-for-ai/differentiable-optimization, 01-ai/reward-modeling) - `[[tensor-cores]]` × 2 refs (from 09-algorithms-systems-for-ai/mixed-precision-training, 09-algorithms-systems-for-ai/quantization-basics) - `[[long-context-models]]` × 2 refs (from 09-algorithms-systems-for-ai/attention-mechanisms, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers) - `[[curriculum-learning]]` × 2 refs (from 09-algorithms-systems-for-ai/reinforcement-learning-schedulers, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers) - `[[policy-gradient-theory]]` × 2 refs (from 09-algorithms-systems-for-ai/reinforcement-learning-schedulers, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers) - `[[rlhf-infrastructure-overview]]` × 2 refs (from 09-algorithms-systems-for-ai/reinforcement-learning-schedulers, 09-algorithms-systems-for-ai/reinforcement-learning-schedulers) **Recurring critic issues (top 5)** - × 13 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 9 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 7 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 7 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 6 — critic-coverage: The 'Mathematical foundations' section is present but lacks a dedicated list of

---

Cycle — 2026-05-27 06:07 EDT — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 3 · Avg conf: 0.78 · First-try: 47%

🟢 What went well

• High overall approval: 25 of 30 runs approved (~83% approval).
• Average confidence across the cycle is healthy at 0.783, indicating generally solid page quality.
• First-try approval rate of 47% shows nearly half of submissions pass without revision—pipeline and critics enabling good first-pass outcomes.
• Several tracks produced high-confidence pages: 03-representation-learning (0.862) and 08-causal-statistical-inference (0.812).
• No orphaned run records and no under-threshold landings reported — run bookkeeping and landing detection are working.

🔴 What went wrong

• Three errored runs in the cycle; investigate causes and error classes to prevent recurrence.
• Recurring failures from critic-info-architecture: missing 'Connected topics' and 'Where this concept appears' reported repeatedly (multiple counts: 11, 10, 9, 6).
• critic-build-nudge repeatedly flagged missing 'What can you build next' (6 counts), causing avoidable revision cycles.
• First-try approval still under 50% (47%); room to improve initial-author guidance and automated nudges to reduce rework.
• Heading drift detected: one instance of a nonstandard heading '## In production' — heading schema enforcement needs tightening.
• Multiple unresolved wikilinks (10 distinct slugs) referenced by existing pages, indicating missing canonical pages that degrade navigation and reader experience.

🟡 What needs depth

• 03-representation-learning has only 1 page — expand coverage despite high confidence to avoid single-point-of-truth risk.
• 08-causal-statistical-inference and 05-statistical-probabilistic-ml have very few pages (1 and 2 respectively) — consider additional core-concept and tutorial pages.
• 04-neural-networks-deep-learning has only 3 pages; several common foundational topics are unresolved (layer-normalization, residual-networks, adaptive-optimizers) and should be added for completeness.
• 09-algorithms-systems-for-ai has the largest page count (7) but also many unresolved wikilinks tied to systems and training arcs (quantization-basics, kv-cache-management, differentiable-optimization, communication-collectives, compiler-optimizations-for-ml, reinforcement-learning-schedulers) — surface-level coverage needs concrete stubs and deeper articles.
• critic-info-architecture is the weakest critic across tracks with average scores around ~0.5 — information-architecture dimensions (Connected topics, Where this concept appears) are under-covered and causing repeated rework.
• Citation coverage: 51 unique arXiv IDs and 27 pages with arXiv citations—some pages lack author anchors or author-page context around highly-cited works (top arXiv IDs: 1810.04805, 2403.02349, 2508.15884).

➕ What to add (auto-applies marked ✓, others queued for review)

• ✓ Create a stub 'quantization-basics' in track 09-algorithms-systems-for-ai to resolve multiple unresolved references.
• evidence: Referenced 3 times by pages in 09-algorithms-systems-for-ai (precision-scaling, quantization-aware-training).
• refs: 09-algorithms-systems-for-ai/precision-scaling, 09-algorithms-systems-for-ai/quantization-aware-training
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'probabilistic-programming' in track 05-statistical-probabilistic-ml to satisfy references from Bayesian pages.
• evidence: Referenced twice by Bayesian-related pages (bayesian-inference, bayesian-neural-networks).
• refs: 05-statistical-probabilistic-ml/bayesian-inference, 05-statistical-probabilistic-ml/bayesian-neural-networks
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'layer-normalization' in track 04-neural-networks-deep-learning to resolve references from Transformer and normalization pages.
• evidence: Referenced twice by transformer-architecture and batch-normalization pages.
• refs: 04-neural-networks-deep-learning/transformer-architecture, 04-neural-networks-deep-learning/batch-normalization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'residual-networks' in track 04-neural-networks-deep-learning to anchor references in normalization topics.
• evidence: Referenced twice by normalization and batch-normalization pages.
• refs: 04-neural-networks-deep-learning/normalization, 04-neural-networks-deep-learning/batch-normalization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'adaptive-optimizers' in track 04-neural-networks-deep-learning to satisfy optimizer references.
• evidence: Referenced twice by gradient-descent and batch-normalization pages.
• refs: 04-neural-networks-deep-learning/gradient-descent, 04-neural-networks-deep-learning/batch-normalization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'kv-cache-management' in track 09-algorithms-systems-for-ai to resolve LLM inference references.
• evidence: Referenced twice by llm-inference and inference-optimization pages.
• refs: 09-algorithms-systems-for-ai/llm-inference, 09-algorithms-systems-for-ai/inference-optimization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'differentiable-optimization' in track 09-algorithms-systems-for-ai to support quantization-aware-training references.
• evidence: Referenced twice by quantization-aware-training pages.
• refs: 09-algorithms-systems-for-ai/quantization-aware-training, 09-algorithms-systems-for-ai/quantization-aware-training
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'communication-collectives' in track 09-algorithms-systems-for-ai to anchor distributed-training references.
• evidence: Referenced twice by distributed-training-arc pages.
• refs: 09-algorithms-systems-for-ai/distributed-training-arc, 09-algorithms-systems-for-ai/distributed-training-arc
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'compiler-optimizations-for-ml' in track 09-algorithms-systems-for-ai to resolve distributed-training references.
• evidence: Referenced twice by distributed-training-arc pages.
• refs: 09-algorithms-systems-for-ai/distributed-training-arc, 09-algorithms-systems-for-ai/distributed-training-arc
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub 'reinforcement-learning-schedulers' in track 09-algorithms-systems-for-ai to complete the distributed-training arc references.
• evidence: Referenced twice by distributed-training-arc pages.
• refs: 09-algorithms-systems-for-ai/distributed-training-arc, 09-algorithms-systems-for-ai/distributed-training-arc
• risk: safe · auto_apply: True · type: stub-seed
• ⏳ Raise queue priority for track 09-algorithms-systems-for-ai to 'high' to accelerate resolution of multiple unresolved wikilinks and reviewer fixes.
• evidence: Track 09 has 7 pages and multiple unresolved wikilinks referencing systems and training arc topics.
• refs: 09-algorithms-systems-for-ai
• risk: safe · auto_apply: True · type: queue-priority-bump
• ○ Seed author-page proposals for the authors associated with arXiv:1810.04805 to provide author anchors for the top-cited paper.
• evidence: arXiv 1810.04805 is the most-cited ID in the cycle (16 citations).
• refs: 1810.04805
• risk: moderate · auto_apply: False · type: author-page-seed
• ○ Seed author-page proposals for the authors associated with arXiv:2403.02349 to anchor frequently-cited recent work.
• evidence: arXiv 2403.02349 is the second-most-cited (12 citations).
• refs: 2403.02349
• risk: moderate · auto_apply: False · type: author-page-seed
• ○ Seed author-page proposals for the authors associated with arXiv:2508.15884 to improve citation navigation.
• evidence: arXiv 2508.15884 is the third-most-cited (8 citations) in this cycle.
• refs: 2508.15884
• risk: moderate · auto_apply: False · type: author-page-seed
• ○ Propose an 'distributed-training' arc for track 09 to consolidate related concepts (communication collectives, compiler optimizations, schedulers, kv-cache, differentiable optimization, quantization).
• evidence: Multiple related unresolved wikilinks and references clustered in 09-algorithms-systems-for-ai; track has 7 pages indicating potential for a cohesive arc.
• refs: 09-algorithms-systems-for-ai/distributed-training-arc, compiler-optimizations-for-ml, communication-collectives, reinforcement-learning-schedulers
• risk: moderate · auto_apply: False · type: arc-proposal

⚙️ Process improvements (human review)

• Update critic-info-architecture rules to explicitly require both a 'Connected topics' section and a 'Where this concept appears' section for core-concept pages; make missing sections a hard validation failure for core-concept schema.
• Add a formal check in critic-build-nudge to require a 'What can you build next' subsection for tutorial and build-it pages and surface a clear message with an example snippet.
• Add a pre-merge validation that flags unresolved wikilinks with reference_count >= 2 and auto-suggests stub-seed creation (tie into the stub-seed auto-apply workflow).
• Tighten heading schema validation to catch and reject nonstandard headings like '## In production' and return a specific remediation message recommending allowed headings.
• Improve critic message deduplication and clarity: merge near-duplicate info-architecture messages into single actionable guidance to reduce reviewer noise.
• Introduce a 'first-pass guidance' prompt persona that instructs authors to include Connected topics, Where this concept appears, and Build-it sections in draft submissions to raise first-try approval rate.
• Adjust trim-knob for the info-architecture critic to increase its weight earlier in the pipeline (fail-fast) so pages missing required architecture sections are returned pre-review for quicker author fixes.
• Add a dashboard alert for tracks with >5 unresolved wikilinks to trigger targeted stub seeding and reviewer attention.
• Add a periodic audit job to propose author-page candidates for the top-cited arXiv IDs to improve citation discoverability and reduce citation-context gaps.

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `03-representation-learning` | 1 | 0.86 | `critic-info-architecture` (0.50) | | `04-neural-networks-deep-learning` | 3 | 0.77 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 2 | 0.78 | `critic-info-architecture` (0.50) | | `08-causal-statistical-inference` | 1 | 0.81 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 7 | 0.75 | `critic-info-architecture` (0.53) | **Heading drift (v1 forbidden headings)** - `## In production` × 1 **Unresolved wikilinks (stub-seed candidates)** - `[[quantization-basics]]` × 3 refs (from 09-algorithms-systems-for-ai/precision-scaling, 09-algorithms-systems-for-ai/quantization-aware-training, 09-algorithms-systems-for-ai/quantization-aware-training) - `[[probabilistic-programming]]` × 2 refs (from 05-statistical-probabilistic-ml/bayesian-inference, 05-statistical-probabilistic-ml/bayesian-neural-networks) - `[[layer-normalization]]` × 2 refs (from 04-neural-networks-deep-learning/transformer-architecture, 04-neural-networks-deep-learning/batch-normalization) - `[[residual-networks]]` × 2 refs (from 04-neural-networks-deep-learning/normalization, 04-neural-networks-deep-learning/batch-normalization) - `[[adaptive-optimizers]]` × 2 refs (from 04-neural-networks-deep-learning/gradient-descent, 04-neural-networks-deep-learning/batch-normalization) - `[[kv-cache-management]]` × 2 refs (from 09-algorithms-systems-for-ai/llm-inference, 09-algorithms-systems-for-ai/inference-optimization) - `[[differentiable-optimization]]` × 2 refs (from 09-algorithms-systems-for-ai/quantization-aware-training, 09-algorithms-systems-for-ai/quantization-aware-training) - `[[communication-collectives]]` × 2 refs (from 09-algorithms-systems-for-ai/distributed-training-arc, 09-algorithms-systems-for-ai/distributed-training-arc) - `[[compiler-optimizations-for-ml]]` × 2 refs (from 09-algorithms-systems-for-ai/distributed-training-arc, 09-algorithms-systems-for-ai/distributed-training-arc) - `[[reinforcement-learning-schedulers]]` × 2 refs (from 09-algorithms-systems-for-ai/distributed-training-arc, 09-algorithms-systems-for-ai/distributed-training-arc) **Recurring critic issues (top 5)** - × 11 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 10 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 9 — critic-info-architecture: The 'Connected topics' section is missing entirely, failing the requirement for - × 6 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 6 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo

---

Cycle — 2026-05-27 05:57 EDT — Retrospective

Runs analyzed: 30 · Approved: 26 · Errored: 2 · Avg conf: 0.78 · First-try: 47%

🟢 What went well

• Overall approval rate was high: 26/30 runs approved (~87%), showing steady quality control.
• Average confidence across the cycle is healthy at 0.78, indicating generally reliable outputs.
• Several tracks produced high-confidence pages: 03-representation-learning (0.862) and 08-causal-statistical-inference (0.812).
• Citation coverage is strong: 46 unique arXiv IDs across 26 pages, and several highly cited papers detected (e.g., arXiv:1810.04805).
• Critics are catching structural problems: critic-info-architecture and critic-build-nudge repeatedly flagged missing sections, demonstrating useful checks.

🔴 What went wrong

• Two runs errored (2/30); investigate causes to prevent reoccurrence and improve pipeline resilience.
• First-try approval rate is low-moderate at 47% — nearly half of pages required revisions after initial generation.
• Recurring critic issues are concentrated and repetitive: info-architecture repeatedly flags missing 'Connected topics' and 'Where this concept appears' sections (multiple counts), causing core-concept failures.
• Weakest critic scores per track are low (0.50–0.58), indicating persistent structural/content-architecture weaknesses across tracks.
• Unresolved wikilinks are concentrated in a few pages (notably post-training-quantization), creating topical gaps and broken navigation.

🟡 What needs depth

• Several tracks have very few pages and need more coverage: 03-representation-learning (1 page), 08-causal-statistical-inference (1 page), 05-statistical-probabilistic-ml (2 pages), 04-neural-networks-deep-learning (3 pages).
• Info-architecture is the weakest critic across nearly all tracks — pages lack required 'Connected topics' and 'Where this concept appears' sections, reducing discoverability and linkage.
• Multiple high-value concepts are only referenced (wikilinks) and lack pages (model-deployment, bayesian-optimization, precision-scaling, llm-inference), leaving reference sites shallow.
• First-try revision rate suggests several pages need deeper initial content (examples, 'Where this concept appears', and 'Connected topics') to pass validators.
• Although citation volume is good, top-cited papers (e.g., 1810.04805, 2403.02349) would benefit from author/landing pages to improve navigation and attribution.

➕ What to add (auto-applies marked ✓, others queued for review)

• ✓ Create a stub seed page for 'model-deployment' in track 09-algorithms-systems-for-ai to resolve four unresolved references.
• evidence: Unresolved wikilink 'model-deployment' referenced 4 times, concentrated in post-training-quantization pages.
• refs: model-deployment
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub seed page for 'bayesian-optimization' in track 05-statistical-probabilistic-ml to resolve three unresolved references.
• evidence: Unresolved wikilink 'bayesian-optimization' referenced 3 times across Bayesian-related pages.
• refs: bayesian-optimization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub seed page for 'precision-scaling' in track 09-algorithms-systems-for-ai to resolve three unresolved references.
• evidence: Unresolved wikilink 'precision-scaling' referenced 3 times in post-training-quantization context.
• refs: precision-scaling
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub seed page for 'llm-inference' in track 09-algorithms-systems-for-ai to resolve three unresolved references.
• evidence: Unresolved wikilink 'llm-inference' referenced 3 times in post-training-quantization content.
• refs: llm-inference
• risk: safe · auto_apply: True · type: stub-seed
• ⏳ Raise processing priority for topic '09-algorithms-systems-for-ai/post-training-quantization' to expedite fixes for multiple unresolved links.
• evidence: Multiple unresolved wikilinks (model-deployment, precision-scaling, llm-inference, quantization-basics, differentiable-optimization) are referenced primarily from this page.
• refs: 09-algorithms-systems-for-ai/post-training-quantization
• risk: safe · auto_apply: True · type: queue-priority-bump
• ○ Propose an author/landing page seed for the most-cited arXiv paper (1810.04805) to improve citation navigation and attribution.
• evidence: arXiv:1810.04805 is the most-cited paper (16 mentions) in the cycle.
• refs: arxiv:1810.04805
• risk: moderate · auto_apply: False · type: author-page-seed
• ○ Propose an arc for 'quantization & precision' in track 09 to group related pages (post-training-quantization, quantization-aware-training, precision-scaling, quantization-basics, differentiable-optimization).
• evidence: Track 09 has multiple related unresolved concepts and repeated references centered on quantization topics.
• refs: 09-algorithms-systems-for-ai
• risk: moderate · auto_apply: False · type: arc-proposal

⚙️ Process improvements (human review)

• Tighten critic-info-architecture expectations into explicit required subsections for core-concept pages: 'Connected topics' and 'Where this concept appears' (clear pass/fail rules and examples).
• Enhance critic-build-nudge to require a short concrete 'What can you build next' bullet and an optional starter command/snippet, reducing pages that end abruptly.
• Reduce duplicate/near-duplicate critic messages by consolidating info-architecture checks and improving error text to include the exact missing subsection name and remediation steps.
• Add a preflight check that enforces presence of required structural sections for core-concept pages before reviewer handoff to increase first-try approval rate.
• Add a targeted generator prompt variant for tracks with low weakest_critic_avg (≤0.58) that biases output toward explicit 'Connected topics' and 'Where this concept appears' content.
• Instrument pipeline logging to capture root causes for the two errored runs (stack traces, inputs) and add an alert rule so failures are investigated within the next sprint.
• Consider an automated stub-creation flow for high-frequency unresolved wikilinks (after human review) to keep navigation intact — add to backlog for human approval.

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `03-representation-learning` | 1 | 0.86 | `critic-info-architecture` (0.50) | | `04-neural-networks-deep-learning` | 3 | 0.77 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 2 | 0.77 | `critic-info-architecture` (0.50) | | `08-causal-statistical-inference` | 1 | 0.81 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 5 | 0.75 | `critic-info-architecture` (0.58) | **Unresolved wikilinks (stub-seed candidates)** - `[[model-deployment]]` × 4 refs (from 09-algorithms-systems-for-ai/post-training-quantization, 09-algorithms-systems-for-ai/post-training-quantization, 09-algorithms-systems-for-ai/post-training-quantization) - `[[bayesian-optimization]]` × 3 refs (from 05-statistical-probabilistic-ml/gaussian-processes, 05-statistical-probabilistic-ml/bayesian-neural-networks, 05-statistical-probabilistic-ml/bayesian-neural-networks) - `[[precision-scaling]]` × 3 refs (from 09-algorithms-systems-for-ai/post-training-quantization, 09-algorithms-systems-for-ai/post-training-quantization, 09-algorithms-systems-for-ai/post-training-quantization) - `[[llm-inference]]` × 3 refs (from 09-algorithms-systems-for-ai/post-training-quantization, 09-algorithms-systems-for-ai/post-training-quantization, 09-algorithms-systems-for-ai/post-training-quantization) - `[[probabilistic-programming]]` × 2 refs (from 05-statistical-probabilistic-ml/bayesian-inference, 05-statistical-probabilistic-ml/bayesian-neural-networks) - `[[layer-normalization]]` × 2 refs (from 04-neural-networks-deep-learning/transformer-architecture, 04-neural-networks-deep-learning/batch-normalization) - `[[residual-networks]]` × 2 refs (from 04-neural-networks-deep-learning/normalization, 04-neural-networks-deep-learning/batch-normalization) - `[[adaptive-optimizers]]` × 2 refs (from 04-neural-networks-deep-learning/gradient-descent, 04-neural-networks-deep-learning/batch-normalization) - `[[quantization-basics]]` × 2 refs (from 09-algorithms-systems-for-ai/quantization-aware-training, 09-algorithms-systems-for-ai/quantization-aware-training) - `[[differentiable-optimization]]` × 2 refs (from 09-algorithms-systems-for-ai/quantization-aware-training, 09-algorithms-systems-for-ai/quantization-aware-training) **Recurring critic issues (top 5)** - × 10 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 8 — critic-info-architecture: The 'Connected topics' section is missing entirely, failing the requirement for - × 7 — critic-build-nudge: The 'What can you build next' section is missing; the page ends with a 'Build it - × 5 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo

---

Cycle — 2026-05-27 08:13 UTC — Retrospective

Runs analyzed: 30 · Approved: 23 · Errored: 4 · Avg conf: 0.77 · First-try: 60%

🟢 What went well

• Overall approval rate strong: 23/30 runs approved (76.7%), indicating solid content quality and reviewer alignment.
• First-try approval rate of 60% — a healthy proportion of pages met standards without revision overhead.
• Average confidence across the cycle is 0.769, showing generally consistent model certainty in accepted pages.
• Several tracks produce high-confidence pages: 06-reinforcement-learning (0.834) and 09-algorithms-systems-for-ai (0.792) demonstrating strong topic coverage and critic alignment.
• Critics are catching architecture/information issues reliably — critic-info-architecture surfaced the same missing sections consistently, which shows the critic is active and enforcing schema expectations.

🔴 What went wrong

• Four runs errored — investigate root causes for pipeline failures to prevent wasted authoring attempts.
• Recurring critic failures around core-concept structure: 'Where this concept appears' and 'Connected topics' are repeatedly missing (critic-info-architecture reported 9–11 occurrences).
• critic-beginner-onramp flagged that prerequisites are present only in frontmatter but not explained in-page (6 occurrences), reducing accessibility for beginners.
• Heading drift observed: '## Mathematical foundations' (3 occurrences) and '## In production' (2 occurrences) — headings are diverging from canonical structure.
• Ten unresolved wikilinks remain referenced across the corpus, creating navigation and discovery gaps.
• Track 08 (causal-statistical-inference) shows weakest cohesion (critic-cohesion avg 0.0) and the lowest avg_confidence (0.6); this is a regression risk for that subject area.

🟡 What needs depth

• Several tracks have very small surface area (1–3 pages): 01-ai (1), 08-causal-statistical-inference (1), 03-representation-learning (2), 04-neural-networks-deep-learning (2). These should be expanded to improve coverage and cross-link density.
• critic-info-architecture is the weakest dimension across multiple tracks (notably 03, 04, 09) — pages are frequently missing 'Where this concept appears' and 'Connected topics', indicating shallow contextualization.
• 05-statistical-probabilistic-ml shows a low build-nudge score (0.333) — practical guidance / scaffolding for readers appears underdeveloped.
• Citation coverage: 38 unique arXiv ids across 21 pages — several pages still lack citations; the top-cited arXiv (2604.15469) is heavily reused (8 references) suggesting opportunity to diversify or create an author/anchor page for highly-cited works.
• Multiple system/ops topics in 09 reference the same missing infrastructure stubs (quantization-related, mixed-precision, distributed-training) — these are recurring missing concepts that block cohesive system-level docs.
• Heading drift cluster around 'Mathematical foundations' and 'In production' suggests some pages are misaligned on intended audience/perspective and need rework for consistent sectioning.

➕ What to add (auto-applies marked ✓, others queued for review)

• ✓ Create a stub page for 'normalization' in the neural networks track
• evidence: Referenced 3 times by multiple 04-neural-networks-deep-learning pages (residual-connections, optimization)
• refs: 04-neural-networks-deep-learning/residual-connections, 04-neural-networks-deep-learning/optimization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'potential-outcomes' in the causal inference track
• evidence: Referenced twice by 08-causal-statistical-inference pages (instrumental-variables, counterfactuals)
• refs: 08-causal-statistical-inference/instrumental-variables, 08-causal-statistical-inference/counterfactuals
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'data-augmentation' in representation learning
• evidence: Referenced twice in 03-representation-learning (simclr, contrastive-learning)
• refs: 03-representation-learning/simclr, 03-representation-learning/contrastive-learning
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'transformer-architecture' in neural networks
• evidence: Referenced by both 04-neural-networks-deep-learning/residual-connections and 09-algorithms-systems-for-ai/tensor-parallelism
• refs: 04-neural-networks-deep-learning/residual-connections, 09-algorithms-systems-for-ai/tensor-parallelism
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'gradient-descent' in neural networks
• evidence: Referenced twice by 04-neural-networks-deep-learning/optimization
• refs: 04-neural-networks-deep-learning/optimization, 04-neural-networks-deep-learning/optimization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'attention-mechanisms' in systems/algorithms
• evidence: Referenced twice by 09-algorithms-systems-for-ai pages (tensor-parallelism, flash-attention)
• refs: 09-algorithms-systems-for-ai/tensor-parallelism, 09-algorithms-systems-for-ai/flash-attention
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'post-training-quantization' in systems/algorithms
• evidence: Referenced twice by 09-algorithms-systems-for-ai/quantization
• refs: 09-algorithms-systems-for-ai/quantization, 09-algorithms-systems-for-ai/quantization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'mixed-precision-training' in systems/algorithms
• evidence: Referenced twice by 09-algorithms-systems-for-ai/quantization
• refs: 09-algorithms-systems-for-ai/quantization, 09-algorithms-systems-for-ai/quantization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'llm-architecture-optimizations' in systems/algorithms
• evidence: Referenced twice by 09-algorithms-systems-for-ai/quantization
• refs: 09-algorithms-systems-for-ai/quantization, 09-algorithms-systems-for-ai/quantization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'distributed-training-arc' in systems/algorithms
• evidence: Referenced twice by 09-algorithms-systems-for-ai/data-parallelism
• refs: 09-algorithms-systems-for-ai/data-parallelism, 09-algorithms-systems-for-ai/data-parallelism
• risk: safe · auto_apply: True · type: stub-seed
• ⏳ Increase queue priority for review/rewrites in 08-causal-statistical-inference to HIGH
• evidence: Track has lowest avg_confidence (0.6) and critic-cohesion average 0.0, indicating urgent cohesion/content needs
• refs: 08-causal-statistical-inference, critic-cohesion
• risk: safe · auto_apply: True · type: queue-priority-bump

⚙️ Process improvements (human review)

• Add a schema-enforced requirement and explicit guidance for core-concept pages to include both 'Where this concept appears' and 'Connected topics' sections; have critic-info-architecture escalate from warnings to blockers if absent for core pages.
• Refine critic-beginner-onramp to check both frontmatter and in-body explicit prerequisite explanations; when prerequisites live only in frontmatter, prompt an in-page 'How to prepare' subsection example.
• Tune critic deduplication: collapse repeated identical messages into single actionable report per page to reduce noise and improve triage efficiency (we observed many near-duplicate critic-info-architecture reports).
• Add a heading-canonicalization check that flags drift from approved section headings (reduce 'Mathematical foundations' / 'In production' divergence) and suggest the canonical headings for the track.
• Introduce a post-run health check that extracts unresolved wikilinks and auto-queues stub-seed actions (already proposed) but include a human-review gate for ambiguous cross-track references.
• Improve error diagnostics and telemetry for the 4 errored runs (capture stack + input snapshot) so pipeline errors are actionable and reproducible for the infra team.

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `01-ai` | 1 | 0.74 | `critic-info-architecture` (0.50) | | `03-representation-learning` | 2 | 0.77 | `critic-info-architecture` (0.50) | | `04-neural-networks-deep-learning` | 2 | 0.73 | `critic-info-architecture` (0.60) | | `05-statistical-probabilistic-ml` | 3 | 0.72 | `critic-build-nudge` (0.33) | | `06-reinforcement-learning` | 2 | 0.83 | `critic-info-architecture` (0.68) | | `08-causal-statistical-inference` | 1 | 0.60 | `critic-cohesion` (0.00) | | `09-algorithms-systems-for-ai` | 6 | 0.79 | `critic-info-architecture` (0.65) | **Heading drift (v1 forbidden headings)** - `## Mathematical foundations` × 3 - `## In production` × 2 **Unresolved wikilinks (stub-seed candidates)** - `[[normalization]]` × 3 refs (from 04-neural-networks-deep-learning/residual-connections, 04-neural-networks-deep-learning/optimization, 04-neural-networks-deep-learning/optimization) - `[[potential-outcomes]]` × 2 refs (from 08-causal-statistical-inference/instrumental-variables, 08-causal-statistical-inference/counterfactuals) - `[[data-augmentation]]` × 2 refs (from 03-representation-learning/simclr, 03-representation-learning/contrastive-learning) - `[[transformer-architecture]]` × 2 refs (from 04-neural-networks-deep-learning/residual-connections, 09-algorithms-systems-for-ai/tensor-parallelism) - `[[gradient-descent]]` × 2 refs (from 04-neural-networks-deep-learning/optimization, 04-neural-networks-deep-learning/optimization) - `[[attention-mechanisms]]` × 2 refs (from 09-algorithms-systems-for-ai/tensor-parallelism, 09-algorithms-systems-for-ai/flash-attention) - `[[post-training-quantization]]` × 2 refs (from 09-algorithms-systems-for-ai/quantization, 09-algorithms-systems-for-ai/quantization) - `[[mixed-precision-training]]` × 2 refs (from 09-algorithms-systems-for-ai/quantization, 09-algorithms-systems-for-ai/quantization) - `[[llm-architecture-optimizations]]` × 2 refs (from 09-algorithms-systems-for-ai/quantization, 09-algorithms-systems-for-ai/quantization) - `[[distributed-training-arc]]` × 2 refs (from 09-algorithms-systems-for-ai/data-parallelism, 09-algorithms-systems-for-ai/data-parallelism) **Recurring critic issues (top 5)** - × 11 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 9 — critic-info-architecture: The 'Connected topics' section is missing entirely, failing the requirement for - × 9 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 9 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

---

Cycle — 2026-05-27 07:28 UTC — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.77 · First-try: 73%

🟢 What went well

• Overall approval rate strong: 25/30 runs approved (~83%).
• First-try approval rate healthy at 73% — many pages land without iteration.
• Average confidence across the cycle is good (0.766), with multiple tracks producing high-confidence pages (05-statistical-probabilistic-ml 0.794, 07-attention-memory-reasoning-continual 0.805, 04-neural-networks-deep-learning 0.781, 02-generative-modeling 0.787).
• Citation pipeline produced substantial coverage: 56 unique arXiv IDs and 22 pages with arXiv references, and clear hotspots (arXiv 2603.01761 appears 6×).
• Critics are catching structural problems (info-architecture and beginner-onramp), demonstrating the validation layer is active and finding consistent gaps to fix.

🔴 What went wrong

• Four runs errored (4/30) — investigate causes to reduce run failures.
• Recurring, high-frequency failures from critic-info-architecture: missing 'Where this concept appears' and missing 'Connected topics' sections across many pages (12, 10, 8, 8 counts).
• critic-beginner-onramp flagged pages that list prerequisites only in frontmatter (6 occurrences) — ambiguity between metadata and visible on-page guidance.
• Several unresolved wikilinks point to missing core pages (policy-evaluation, reward-modeling, representation-learning, quantization-aware-training, pipeline-parallelism) and are referenced multiple times, leaving broken navigation.
• Low weakest_critic_avg on a few tracks (03-representation-learning 0.333, 01-ai beginner-onramp 0.25) indicates some pages are missing critical structural/prerequisite content.

🟡 What needs depth

• Tracks with very few pages should be expanded: 02-generative-modeling (1 page) and 10-complexity-cognition-natural-intelligence (1 page) need additional topic pages.
• Several mid-sized tracks have shallow coverage and should be expanded: 03-representation-learning (3 pages), 09-algorithms-systems-for-ai (3 pages), 08-causal-statistical-inference (2 pages), 01-ai (2 pages).
• Info-architecture weaknesses are pervasive across tracks — many pages lack connective/contextual sections (Where this concept appears / Connected topics), indicating shallow concept scaffolding.
• Citation coverage gap: 8 of ~30 pages lack arXiv citations; also top-3 arXiv IDs account for a notable share of citations, suggesting some concentration risk and opportunity to diversify sources.
• Pages that reference unresolved wikilinks (for example multiple references to policy-evaluation) show functional gaps that block readers and downstream linking; those topics should be prioritized for content seeding.

➕ What to add (auto-applies marked ✓, others queued for review)

• ✓ Create a stub page for 'policy-evaluation' in the causal/statistical inference track to resolve multiple unresolved links.
• evidence: Unresolved wikilinks: 'policy-evaluation' referenced 3× by 08-causal-statistical-inference/do-calculus.
• refs: policy-evaluation
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'representation-learning' under the representation-learning track to satisfy cross-track references.
• evidence: Unresolved wikilinks: 'representation-learning' referenced by two pages in other tracks.
• refs: representation-learning
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'quantization-aware-training' in the algorithms/systems track to resolve cross-references and support model compression topics.
• evidence: Unresolved wikilinks: 'quantization-aware-training' referenced by scaling and quantization pages (04 and 09).
• refs: quantization-aware-training
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'pipeline-parallelism' in the algorithms/systems track to close references from distributed training and model-parallelism pages.
• evidence: Unresolved wikilinks: 'pipeline-parallelism' referenced 2× by 09-algorithms-systems-for-ai pages.
• refs: pipeline-parallelism
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'reward-modeling' in the AI track to satisfy references from alignment and RLHF pages.
• evidence: Unresolved wikilinks: 'reward-modeling' referenced 2× by 01-ai/alignment-safety and 01-ai/rlhf.
• refs: reward-modeling
• risk: safe · auto_apply: True · type: stub-seed
• ⏳ Bump queue priority for '08-causal-statistical-inference/do-calculus' to high to force a quick fix for broken policy-evaluation links.
• evidence: Do-calculus pages reference 'policy-evaluation' three times; unresolved link is blocking readers.
• refs: policy-evaluation
• risk: safe · auto_apply: True · type: queue-priority-bump
• ○ Propose an arc for 05-statistical-probabilistic-ml to consolidate core probabilistic ML concepts and ensure consistent 'Where this concept appears' and 'Connected topics' sections across pages.
• evidence: Per-track health: 05 has 5 pages and a repeated weakest-critic deficit (critic-info-architecture avg 0.5), indicating need for cohesive connective structure.
• refs: 05-statistical-probabilistic-ml, critic-info-architecture
• risk: moderate · auto_apply: False · type: arc-proposal
• ○ Seed an author-page for the author(s) of the most-cited arXiv (2603.01761) to anchor citation discoverability and author-level navigation.
• evidence: Citation health: arXiv 2603.01761 is the most-cited ID (6 occurrences), suggesting an author or work page would improve discoverability.
• refs: 2603.01761
• risk: moderate · auto_apply: False · type: author-page-seed

⚙️ Process improvements (human review)

• Add a schema enforcement rule: core-concept pages must include visible 'Where this concept appears' and 'Connected topics' sections; make this a hard requirement for critic-info-architecture.
• Clarify critic-beginner-onramp rules: if prerequisites are listed only in frontmatter, require an explicit on-page 'Prerequisites' section or an equivalent visible statement to satisfy beginner readers.
• Auto-generate stub-seed tasks from unresolved wikilinks at end of each cycle (configurable trim knob) to reduce navigation breakage and speed repairs.
• Increase severity and provide concrete remediation steps in critic-info-architecture messages (example templates for the missing sections) to reduce back-and-forth edits.
• Add a preflight check that fails the run with an actionable message when unresolved wikilinks are present, and optionally auto-bump priority for pages that reference high-frequency unresolved slugs.
• Deduplicate and normalize critic output strings so recurring complaints are grouped (reduce noise where similar messages differ only in wording).
• Instrument a short failure triage flow for errored runs (capture logs and assign to a small ops queue) to reduce the 4 errored runs per cycle.

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `01-ai` | 2 | 0.75 | `critic-beginner-onramp` (0.25) | | `02-generative-modeling` | 1 | 0.79 | `critic-info-architecture` (0.50) | | `03-representation-learning` | 3 | 0.72 | `critic-info-architecture` (0.33) | | `04-neural-networks-deep-learning` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 5 | 0.79 | `critic-info-architecture` (0.50) | | `06-reinforcement-learning` | 5 | 0.75 | `critic-info-architecture` (0.50) | | `07-attention-memory-reasoning-continual` | 4 | 0.81 | `critic-info-architecture` (0.59) | | `08-causal-statistical-inference` | 2 | 0.77 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 3 | 0.71 | `critic-info-architecture` (0.57) | | `10-complexity-cognition-natural-intelligence` | 1 | 0.77 | `critic-info-architecture` (0.50) | **Unresolved wikilinks (stub-seed candidates)** - `[[policy-evaluation]]` × 3 refs (from 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus) - `[[representation-learning]]` × 2 refs (from 10-complexity-cognition-natural-intelligence/compositionality, 01-ai/mechanistic-interpretability) - `[[quantization-aware-training]]` × 2 refs (from 04-neural-networks-deep-learning/scaling-laws, 09-algorithms-systems-for-ai/quantization) - `[[pipeline-parallelism]]` × 2 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/model-parallelism) - `[[reward-modeling]]` × 2 refs (from 01-ai/alignment-safety, 01-ai/rlhf) **Recurring critic issues (top 5)** - × 12 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 10 — critic-info-architecture: The 'Connected topics' section is missing entirely, failing the requirement for - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 8 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

---

Cycle — 2026-05-27 07:04 UTC — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.76 · First-try: 73%

🟢 What went well

• Overall approval rate strong: 25 approved out of 30 runs (83%).
• First-try approval rate solid at 73% — most pages pass initial review without rework.
• Several tracks produced high-confidence pages (07-attention-memory-reasoning-continual avg_confidence=0.805; 02-generative-modeling=0.787; 04-neural-networks-deep-learning=0.781).
• Critics are detecting structural issues consistently (critic-info-architecture and critic-beginner-onramp flagged missing sections), indicating the critic pipeline is functioning and catching common authoring gaps.
• Citation diversity: 63 unique arXiv IDs cited across the cycle showing good breadth of sources.

🔴 What went wrong

• Four errored runs (4/30) — investigate causes to avoid repeat failures.
• Recurring failures from critic-info-architecture: many pages missing a required 'Where this concept appears' section and/or a 'Connected topics' section (multiple counts totaling 13/11/8/7).
• critic-beginner-onramp flagged prerequisites missing from page body (6 occurrences) despite frontmatter entries.
• Multiple unresolved wikilinks remain (10 distinct slugs), creating navigation gaps and blocking concept resolution.
• Track-level weak points: 03-representation-learning shows the lowest avg_confidence (0.723) and weakest info-architecture score (0.375), indicating rework needed for structure and clarity.
• Nearly 27% of pages required at least one revision (1 - first_try_approval_rate = 0.27) — indicates room to improve first-pass completeness.

🟡 What needs depth

• Tracks with very few pages likely under-covered: 02-generative-modeling (1 page) and 10-complexity-cognition-natural-intelligence (1 page) — consider expanding core concept coverage.
• Track 01-ai has low weakest-critic (critic-beginner-onramp avg 0.25) — onramp / prerequisite material is too shallow or missing from page bodies.
• 03-representation-learning requires depth and structure fixes (avg_confidence=0.723; weakest_critic_avg=0.375) — likely multiple pages need rework to meet info-architecture standards.
• Unresolved high-value wikilinks indicate missing topic pages that are referenced repeatedly (e.g., causal-representation-learning referenced 5x; bayesian-neural-networks and expectation-maximization referenced 3x each) — these are creating cross-track friction.
• Citation surface could be improved on pages without arXiv references: only 22 pages contain arXiv links despite 30 runs — surface canonical citations or author pages for frequently-cited works.

➕ What to add (auto-applies marked ✓, others queued for review)

• ✓ Create a stub page for 'causal-representation-learning' under the causal/statistical inference track.
• evidence: Referenced 5 times by multiple pages in 08-causal-statistical-inference (instrumental-variables, mediation-analysis, do-calculus).
• refs: causal-representation-learning
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'bayesian-neural-networks' in statistical/probabilistic ML.
• evidence: Referenced 3 times by variational-inference pages in 05-statistical-probabilistic-ml.
• refs: bayesian-neural-networks
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'expectation-maximization' in statistical/probabilistic ML.
• evidence: Referenced 3 times by variational-inference pages in 05-statistical-probabilistic-ml.
• refs: expectation-maximization
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'tensor-parallelism' in algorithms/systems for AI.
• evidence: Referenced 3 times across distributed-training and model-parallelism pages in 09-algorithms-systems-for-ai.
• refs: tensor-parallelism
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'data-parallelism' in algorithms/systems for AI.
• evidence: Referenced twice by distributed-training pages in 09-algorithms-systems-for-ai.
• refs: data-parallelism
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'model-based-reinforcement-learning' in reinforcement learning.
• evidence: Referenced by world-models and actor-critic pages in 06-reinforcement-learning (2 references).
• refs: model-based-reinforcement-learning
• risk: safe · auto_apply: True · type: stub-seed
• ✓ Create a stub page for 'policy-gradients' in reinforcement learning to resolve cross-track links.
• evidence: Referenced twice (06-reinforcement-learning/ppo and 01-ai/rlhf).
• refs: policy-gradients
• risk: safe · auto_apply: True · type: stub-seed
• ⏳ Bump queue priority for 08-causal-statistical-inference/do-calculus to HIGH to resolve multiple unresolved links (policy-evaluation, causal-representation-learning).
• evidence: do-calculus pages reference unresolved concepts repeatedly and are blocking navigation (multiple references in 08-causal-statistical-inference).
• refs: causal-representation-learning, policy-evaluation, 08-causal-statistical-inference/do-calculus
• risk: safe · auto_apply: True · type: queue-priority-bump
• ○ Seed an author/works page for arXiv:2603.01761 to anchor repeated citations and improve citation discoverability.
• evidence: arXiv 2603.01761 is the most-cited single ID in this cycle (6 citations).
• refs: 2603.01761
• risk: moderate · auto_apply: False · type: author-page-seed
• ○ Propose an arc for 05-statistical-probabilistic-ml to connect variational inference, Bayesian NN, and EM content into a focused learning arc.
• evidence: 05 has multiple related unresolved topics (bayesian-neural-networks, expectation-maximization) and the track has 4 pages—suitable for an arc proposal.
• refs: 05-statistical-probabilistic-ml, bayesian-neural-networks, expectation-maximization
• risk: moderate · auto_apply: False · type: arc-proposal

⚙️ Process improvements (human review)

• Tighten critic-info-architecture guidance: require explicit 'Where this concept appears' and 'Connected topics' for core-concept pages and fail earlier in the pipeline if absent.
• Adjust critic-beginner-onramp checks to require prerequisites be present in the page body (not just frontmatter) and provide template wording snippets for authors.
• Introduce an automated stub-seed creation rule for unresolved wikilinks referenced >=3 times (configurable trim-knob) to reduce cross-track blocking.
• Add a reviewer queue filter for pages with weakest_critic_avg < 0.5 so editors can prioritize structural fixes (start with 03-representation-learning pages).
• Surface top-cited arXiv IDs automatically in the author/citation staging area so maintainers can create author pages or canonical citation shortpages.
• Increase telemetry for errored runs to collect stack/context so we can triage the 4 errored pages (capture error type, stage, and input page slug).
• Refine writer persona prompts to explicitly instruct adding 'Connected topics' and in-body prerequisites for core-concept and onramp pages.
• Consider a queue-priority policy that temporarily elevates pages which reference unresolved high-frequency slugs (e.g., causal-representation-learning) until stubs exist.

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `01-ai` | 2 | 0.75 | `critic-beginner-onramp` (0.25) | | `02-generative-modeling` | 1 | 0.79 | `critic-info-architecture` (0.50) | | `03-representation-learning` | 4 | 0.72 | `critic-info-architecture` (0.38) | | `04-neural-networks-deep-learning` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `06-reinforcement-learning` | 5 | 0.75 | `critic-info-architecture` (0.50) | | `07-attention-memory-reasoning-continual` | 4 | 0.81 | `critic-info-architecture` (0.59) | | `08-causal-statistical-inference` | 2 | 0.78 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 3 | 0.71 | `critic-info-architecture` (0.57) | | `10-complexity-cognition-natural-intelligence` | 1 | 0.77 | `critic-info-architecture` (0.50) | **Unresolved wikilinks (stub-seed candidates)** - `[[causal-representation-learning]]` × 5 refs (from 08-causal-statistical-inference/instrumental-variables, 08-causal-statistical-inference/mediation-analysis, 08-causal-statistical-inference/do-calculus) - `[[bayesian-neural-networks]]` × 3 refs (from 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference) - `[[expectation-maximization]]` × 3 refs (from 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference) - `[[policy-evaluation]]` × 3 refs (from 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus) - `[[tensor-parallelism]]` × 3 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/model-parallelism) - `[[model-based-reinforcement-learning]]` × 2 refs (from 06-reinforcement-learning/world-models, 06-reinforcement-learning/actor-critic) - `[[policy-gradients]]` × 2 refs (from 06-reinforcement-learning/ppo, 01-ai/rlhf) - `[[representation-learning]]` × 2 refs (from 10-complexity-cognition-natural-intelligence/compositionality, 01-ai/mechanistic-interpretability) - `[[quantization-aware-training]]` × 2 refs (from 04-neural-networks-deep-learning/scaling-laws, 09-algorithms-systems-for-ai/quantization) - `[[data-parallelism]]` × 2 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/distributed-training) **Recurring critic issues (top 5)** - × 13 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 11 — critic-info-architecture: The 'Connected topics' section is missing entirely, failing the requirement for - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 7 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

---

Cycle — 2026-05-27 07:03 UTC — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.76 · First-try: 73%

🟢 What went well

(no observations this cycle)

🔴 What went wrong

(none flagged)

🟡 What needs depth

(coverage feels adequate)

➕ What to add (auto-applies marked ✓, others queued for review)

(no additions proposed)

⚙️ Process improvements (human review)

• (LLM proposer failed: LengthFinishReasonError: Could not parse response content as the length limit was reached - CompletionUsage(completion_tokens=4080, prompt_tokens=3284, total_tokens=7364, completion_tok)

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `01-ai` | 2 | 0.75 | `critic-beginner-onramp` (0.25) | | `02-generative-modeling` | 1 | 0.79 | `critic-info-architecture` (0.50) | | `03-representation-learning` | 4 | 0.72 | `critic-info-architecture` (0.38) | | `04-neural-networks-deep-learning` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `06-reinforcement-learning` | 5 | 0.75 | `critic-info-architecture` (0.50) | | `07-attention-memory-reasoning-continual` | 4 | 0.81 | `critic-info-architecture` (0.59) | | `08-causal-statistical-inference` | 2 | 0.78 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 3 | 0.71 | `critic-info-architecture` (0.57) | | `10-complexity-cognition-natural-intelligence` | 1 | 0.77 | `critic-info-architecture` (0.50) | **Unresolved wikilinks (stub-seed candidates)** - `[[causal-representation-learning]]` × 5 refs (from 08-causal-statistical-inference/instrumental-variables, 08-causal-statistical-inference/mediation-analysis, 08-causal-statistical-inference/do-calculus) - `[[bayesian-neural-networks]]` × 3 refs (from 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference) - `[[expectation-maximization]]` × 3 refs (from 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference) - `[[policy-evaluation]]` × 3 refs (from 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus) - `[[tensor-parallelism]]` × 3 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/model-parallelism) - `[[model-based-reinforcement-learning]]` × 2 refs (from 06-reinforcement-learning/world-models, 06-reinforcement-learning/actor-critic) - `[[policy-gradients]]` × 2 refs (from 06-reinforcement-learning/ppo, 01-ai/rlhf) - `[[representation-learning]]` × 2 refs (from 10-complexity-cognition-natural-intelligence/compositionality, 01-ai/mechanistic-interpretability) - `[[quantization-aware-training]]` × 2 refs (from 04-neural-networks-deep-learning/scaling-laws, 09-algorithms-systems-for-ai/quantization) - `[[data-parallelism]]` × 2 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/distributed-training) **Recurring critic issues (top 5)** - × 13 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 11 — critic-info-architecture: The 'Connected topics' section is missing entirely, failing the requirement for - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 7 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

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Cycle — 2026-05-27 07:01 UTC — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.76 · First-try: 73%

🟢 What went well

(no observations this cycle)

🔴 What went wrong

(none flagged)

🟡 What needs depth

(coverage feels adequate)

➕ What to add (auto-applies marked ✓, others queued for review)

(no additions proposed)

⚙️ Process improvements (human review)

(no process changes proposed)

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `01-ai` | 2 | 0.75 | `critic-beginner-onramp` (0.25) | | `02-generative-modeling` | 1 | 0.79 | `critic-info-architecture` (0.50) | | `03-representation-learning` | 4 | 0.72 | `critic-info-architecture` (0.38) | | `04-neural-networks-deep-learning` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `06-reinforcement-learning` | 5 | 0.75 | `critic-info-architecture` (0.50) | | `07-attention-memory-reasoning-continual` | 4 | 0.81 | `critic-info-architecture` (0.59) | | `08-causal-statistical-inference` | 2 | 0.78 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 3 | 0.71 | `critic-info-architecture` (0.57) | | `10-complexity-cognition-natural-intelligence` | 1 | 0.77 | `critic-info-architecture` (0.50) | **Unresolved wikilinks (stub-seed candidates)** - `[[causal-representation-learning]]` × 5 refs (from 08-causal-statistical-inference/instrumental-variables, 08-causal-statistical-inference/mediation-analysis, 08-causal-statistical-inference/do-calculus) - `[[bayesian-neural-networks]]` × 3 refs (from 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference) - `[[expectation-maximization]]` × 3 refs (from 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference) - `[[policy-evaluation]]` × 3 refs (from 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus) - `[[tensor-parallelism]]` × 3 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/model-parallelism) - `[[model-based-reinforcement-learning]]` × 2 refs (from 06-reinforcement-learning/world-models, 06-reinforcement-learning/actor-critic) - `[[policy-gradients]]` × 2 refs (from 06-reinforcement-learning/ppo, 01-ai/rlhf) - `[[representation-learning]]` × 2 refs (from 10-complexity-cognition-natural-intelligence/compositionality, 01-ai/mechanistic-interpretability) - `[[quantization-aware-training]]` × 2 refs (from 04-neural-networks-deep-learning/scaling-laws, 09-algorithms-systems-for-ai/quantization) - `[[data-parallelism]]` × 2 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/distributed-training) **Recurring critic issues (top 5)** - × 13 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 11 — critic-info-architecture: The 'Connected topics' section is missing entirely, failing the requirement for - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 7 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

---

Cycle — 2026-05-27 07:00 UTC — Retrospective

Runs analyzed: 30 · Approved: 25 · Errored: 4 · Avg conf: 0.76 · First-try: 73%

🟢 What went well

(no observations this cycle)

🔴 What went wrong

(none flagged)

🟡 What needs depth

(coverage feels adequate)

➕ What to add (auto-applies marked ✓, others queued for review)

(no additions proposed)

⚙️ Process improvements (human review)

• (Phase B failed: KeyError('ANTHROPIC_API_KEY')) — check OPENAI_API_BASE + key

Data appendix — Phase A signals

**Per-track health** | Track | Pages | Avg conf | Weakest critic | |---|---|---|---| | `01-ai` | 2 | 0.75 | `critic-beginner-onramp` (0.25) | | `02-generative-modeling` | 1 | 0.79 | `critic-info-architecture` (0.50) | | `03-representation-learning` | 4 | 0.72 | `critic-info-architecture` (0.38) | | `04-neural-networks-deep-learning` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `05-statistical-probabilistic-ml` | 4 | 0.78 | `critic-info-architecture` (0.50) | | `06-reinforcement-learning` | 5 | 0.75 | `critic-info-architecture` (0.50) | | `07-attention-memory-reasoning-continual` | 4 | 0.81 | `critic-info-architecture` (0.59) | | `08-causal-statistical-inference` | 2 | 0.78 | `critic-info-architecture` (0.50) | | `09-algorithms-systems-for-ai` | 3 | 0.71 | `critic-info-architecture` (0.57) | | `10-complexity-cognition-natural-intelligence` | 1 | 0.77 | `critic-info-architecture` (0.50) | **Unresolved wikilinks (stub-seed candidates)** - `[[causal-representation-learning]]` × 5 refs (from 08-causal-statistical-inference/instrumental-variables, 08-causal-statistical-inference/mediation-analysis, 08-causal-statistical-inference/do-calculus) - `[[bayesian-neural-networks]]` × 3 refs (from 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference) - `[[expectation-maximization]]` × 3 refs (from 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference, 05-statistical-probabilistic-ml/variational-inference) - `[[policy-evaluation]]` × 3 refs (from 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus, 08-causal-statistical-inference/do-calculus) - `[[tensor-parallelism]]` × 3 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/model-parallelism) - `[[model-based-reinforcement-learning]]` × 2 refs (from 06-reinforcement-learning/world-models, 06-reinforcement-learning/actor-critic) - `[[policy-gradients]]` × 2 refs (from 06-reinforcement-learning/ppo, 01-ai/rlhf) - `[[representation-learning]]` × 2 refs (from 10-complexity-cognition-natural-intelligence/compositionality, 01-ai/mechanistic-interpretability) - `[[quantization-aware-training]]` × 2 refs (from 04-neural-networks-deep-learning/scaling-laws, 09-algorithms-systems-for-ai/quantization) - `[[data-parallelism]]` × 2 refs (from 09-algorithms-systems-for-ai/distributed-training, 09-algorithms-systems-for-ai/distributed-training) **Recurring critic issues (top 5)** - × 13 — critic-info-architecture: The page is missing a 'Where this concept appears' section, which is required fo - × 11 — critic-info-architecture: The 'Connected topics' section is missing entirely, failing the requirement for - × 8 — critic-info-architecture: The page is a core-concept page but lacks a 'Where this concept appears' section - × 7 — critic-info-architecture: The 'Connected topics' section is missing entirely, which is required for core-c - × 6 — critic-beginner-onramp: The prerequisites are listed in the frontmatter, but the page does not explicitl

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