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# Library

A typed, evidence-backed atlas compiled from your vault. `my-vault` · 412 evergreen · 88 deep dives · 14 topics.

What is this page?

The Library is the reader-facing entrypoint. It surfaces compiled Topics, recently absorbed Concepts, and any Open Questions the pipeline has flagged.

Compiled by
  `ovp-build-crystals` · `ovp-moc --scan`
Reads from
  `10-Knowledge/` · `20-Areas/` · `knowledge.db`
Refresh
  Daily on `ovp --full`, or live when AutoPilot runs
Vocabulary
  Topic = community crystal · Open Question = contradiction crystal (BL-051)

Evergreen concepts
412
+8 this week
Deep dives
88
avg quality 3.4 / 5
Topics
14
last compiled 02:14 today
Open questions
3
2 in AI Research, 1 in Tools

## Featured Topics

→ See all 14 featured topics

### RAG vs. Agent Architectures

cluster ai-research 12 evidence

A topic crystal compiled from 4 deep-dives and 11 atomic concepts. The two architectures are not interchangeable: RAG trades agency for verifiability, AI Agent trades verifiability for agency.

2 open questions

### Local-first Knowledge Tooling

cluster tools 9 evidence

Why Obsidian, SQLite and Markdown outlast SaaS knowledge bases. Compiled from 3 deep-dives.

### Six-term Architecture Vocabulary

cluster programming 7 evidence

Sources, Candidates, Canonical State, Projection, Access Surface, Governance — and the rules that hold them apart.

## Recently Absorbed

- Karpathy LLM Wiki Pattern — 2026-04-04 · evergreen · 3 backlinks
- Deterministic Section Embeddings — 2026-04-04 · evergreen · 2 backlinks
- Six-dimension Quality Rubric — 2026-04-03 · evergreen · 5 backlinks
- Truth Projection — 2026-04-03 · evergreen · 4 backlinks
- Authority Identity Discipline — missing target, search fallback

3 Open Questions

Contradiction crystals the runtime couldn't resolve. Open them to read the conflicting evidence side-by-side.

- Does RAG benefit from re-ranking, or hurt latency more than recall?
- Should evergreen pages cap at one concept, or allow tight clusters?
- Is QMD an Authority or a Projection?

### Polaris

Top of mind, this week.

- Stabilize `ovp-build-crystals` Stage B
- Pack API v2 ergonomics
- Reader vs. Maintainer split (BL-051)

Source: `00-Polaris/README.md`

### Search

FTS5 + deterministic embeddings. Advanced ↗

Topic · ai-research · compiled 2026-04-04 02:14

# RAG vs. Agent Architectures

A topic crystal compiled from 4 deep-dives and 11 atomic concepts. The two architectures trade agency against verifiability — they are not interchangeable.

cluster 12 evidence pages 2 open questions last refresh 02:14
What is a Topic?

A Topic is a compiled cluster of related concepts (internal name: `community_crystal`). It is rebuildable from the vault — never edit it by hand; edit the underlying evergreen pages instead.

Compiled by
  `ovp-build-crystals --pack research-tech`
Sources
  4 deep dives · 11 evergreen concepts
Open questions
  Surfaced from `contradiction_crystal` rows

## Compiled summary

RAG trades agency for verifiability: every answer is grounded in a retrieved span you can audit. Agent architectures invert the trade — they will act on partial information, replan, and reach conclusions no single retrieval would surface, but the audit trail is a transcript, not a citation.

The interesting systems are hybrids. Toolformer and ReAct embed retrieval as a tool the agent calls; Self-RAG lets the model decide when retrieval is needed at all. The unsolved question — see Open Questions below — is whether re-ranking helps or hurts.

### Atomic concepts

- RAG — retrieval-augmented generation
- AI Agent — autonomous task-decomposing system
- ReAct — reasoning + acting interleaved
- Toolformer — self-supervised tool-use training
- Self-RAG — model-controlled retrieval
- Re-ranking — cross-encoder rescoring
- Hybrid Retrieval — missing target, search fallback

### Architecture sketch

      RAG                                AGENT
      ───                                ─────
      query ─→ retrieve ─→ rerank ─→     query ─→ plan ─→ act ──┐
                           ↓                          ↑         │
                         LLM ─→ answer                └────── observe
                           ↑                                    │
                        citations                            transcript

### Open questions

1. Does re-ranking help? Three deep-dives say yes (recall@10 improves 12–18%); two say latency cost dominates above 50ms p95. Read the conflict ↗
2. Is the agent's transcript an audit trail? The architecture doc says no — transcripts aren't citations. Two papers in the cluster argue otherwise. Read the conflict ↗

### Evidence

- Lewis et al., 2020 — RAG
- Yao et al., 2022 — ReAct
- Schick et al., 2023 — Toolformer
- Asai et al., 2023 — Self-RAG
- Karpathy gist — LLM Wiki Pattern
- + 7 more

### Linked Topics

- Local-first Knowledge Tooling
- Six-term Architecture Vocabulary
- Evaluation Practices

### Provenance

Compiled | 2026-04-04 02:14
Pack | `research-tech`
Profile | `full`
Run | run-2426
Hash | 7c4f…d12a

Evergreen · 10-Knowledge/Evergreen/AI Agent.md · absorbed 2026-04-03 14:02

# AI Agent

> One-line definition: An AI Agent is a goal-driven system that interleaves reasoning, tool use, and observation to act autonomously on tasks the operator did not pre-decompose.

## 📝 Detailed explanation

### What is it?

An AI Agent is built around a loop: plan → act → observe → revise. Unlike a function-call pipeline, the agent's plan is generated at runtime from a goal and a tool catalog, and may be revised after every observation.

### Why does it matter?

Agents trade verifiability for agency. They reach conclusions no single retrieval would surface — at the cost of an audit trail that is a transcript rather than a citation. See the topic RAG vs. Agent Architectures for the full trade-off.

### How does it work?

Three execution patterns dominate: ReAct (interleave thought and action tokens), Toolformer (self-supervised training over tool tokens), and Plan-and-Execute (decouple a long-horizon plan from per-step execution).

### 🏗️ Architecture

      ┌──────────┐    ┌──────────┐    ┌──────────┐
      │  Plan    │ → │   Act     │ → │  Observe  │
      └──────────┘    └──────────┘    └──────────┘
            ↑                              │
            └──────── revise ──────────────┘

### 💡 Action items

- Short term — read Yao et al. 2022 (ReAct) and run the canonical hotpot QA replication.
- Long term — evaluate LangGraph vs. hand-rolled state machines for production agents.

### 🔗 Related concepts

- RAG — the verifiability counterpoint
- Tool Use
- Reflection
- Multi-Agent Coordination

### Frontmatter

type | evergreen
date | 2026-04-03
tags | evergreen, ai-research
aliases | autonomous agent, LLM agent
quality | 4.2 / 5

### Backlinks

5 deep dives, 2 topics

- RAG vs. Agent Architectures (topic)
- ai-agent-architecture_深度解读
- react-paper-replication_深度解读
- karpathy-llm-wiki_深度解读
- MOC-AI-Research (MOC)

### Open in

Open in Obsidian View raw markdown Show in graph

### 6-dim quality

Definition | 5
Explanation | 4
Details | 4
Structure | 5
Actionable | 4
Linking | 3

Total 4.2 · pass threshold 3.0

# Maintenance · Ops

Operator workbench for pipeline runs, projection health, and governance audits. Reader vocabulary does not apply here — internal storage names are exposed.

What is the Ops shell?

The Ops shell is for the operator (you) — not the reader. It exposes raw pipeline state, lets you replay runs, and surfaces governance violations. It is intentionally separate from the Library shell.

Reads from
  `knowledge.db` · `60-Logs/pipeline.jsonl` · `60-Logs/transactions/`
Refresh
  5s meta-refresh on `/ops/queue` and `/ops/runs`
Boundary
  Read-only. Mutations happen via CLI: `ovp-rebuild-registry`, `ovp-knowledge-index`, `ovp-lint --fix`

Active run
running `ovp --full`
step 6 of 9 · absorb · ETA 02:18
Inbox queue
7
3 articles · 2 papers · 2 clippings
Projection drift
2
`knowledge.db` stale vs. registry
Governance
0
no violations in last 24h

### Recent runs

last 10 · all runs ↗

id | cmd | status | duration | started
--- | --- | --- | --- | ---
run-2426 | `ovp --full` | running | — · started 02:11
run-2425 | `ovp-build-crystals` | ready | 8.2s · 02:09
run-2424 | `ovp-knowledge-index` | ready | 12.6s · 02:06
run-2423 | `ovp --step articles` | stale | 2 retries · 01:38
run-2422 | `ovp-absorb --recent 7` | ready | 22.1s · 01:14
run-2421 | `ovp-lint --check` | ready | 1.4s · 00:52

### Pipeline timeline · last 30 minutes

auto-refresh 5s
02:11:04 `articles` · 3 deep dives drafted from `50-Inbox/01-Raw/`+3 candidates
02:13:22 `quality` · 2 of 3 cleared 6-dim threshold avg 3.7
02:14:01 `fix_links` · 1 deep dive deferred — broken link to `[[Hybrid Retrieval]]`deferred
02:14:48 `absorb` · 5 evergreen candidates promoted+5 evergreen
02:15:30 `registry_sync` · in progress…running
2 projection drift events

The derived `knowledge.db` reports a row count for `evergreen_pages` that does not match the registry. Rebuild the projection, never edit it directly.

    $ ovp-knowledge-index --rebuild --confirm

### AutoPilot

running `--watch=inbox --parallel=1`

Uptime | 4d 06h
Files seen | 148
Pack | `research-tech`
Profile | `autopilot`
Last commit | 02:09 · auto

### WIGS lint

L1 Transactions | 0 open
L2 Orphans | 3
L2 Broken links | 11
L3 Ingestion | 7 queued
L4 MOC drift | 0
L5 Archive | 42 stale

ovp-lint --interactive ↗

### Six-term boundaries

1. Sources external, immutable
2. Candidates awaiting review
3. Canonical State evidence-backed
4. Projection rebuildable, derived
5. Access Surface read APIs
6. Governance audit + replay

"If a Projection cannot be rebuilt, it carried truth that should have been in Canonical State — that's an architectural bug."