rose-ash/plans/artdag-on-sx.md

# artdag-on-sx: Content-addressed dataflow DAG engine

art-dag is rose-ash's media-processing engine: a content-addressed DAG of effects,
executed in three phases — **Analyze → Plan → Execute**. Today it's a separate
Python stack (FastAPI + Celery + JAX + IPFS). Its *engine logic* — dependency
analysis, scheduling, content-addressed memoization, incremental recompute,
composable s-expression effects — is exactly the kind of declarative, substrate-shaped
work SX excels at, and art-dag already speaks s-expressions (its `sexp_effects`).

This subsystem rebuilds the **engine** on SX (not the pixel-pushing): the DAG model,
the three-phase pipeline, and the incremental/memoized executor. Media ops
themselves (JAX kernels, IPFS pins) stay opaque — modelled as abstract node
functions in tests, delegated to injected adapters in production. The win is that
the same SX substrates already serve the phases:

- **Analyze** (deps, reachability, dirtiness) → **Datalog** (recursive reachability —
  the acl/relations shape).
- **Plan** (schedule under constraints) → topological batching now; **miniKanren**
  for constraint-based scheduling later (optional).
- **Execute** (composable effects + content-addressed memo) → SX's own
  `perform`/`cek-resume` + a **persist**-backed content-addressed result cache;
  incremental recompute drops the cost of re-rendering to the dirty subgraph.
- **Optimize** (fuse/dedup effect pipelines) → term rewriting (a later, optional
  consumer of `maude-on-sx`'s engine — see `plans/maude-on-sx.md`).

End-state: a content-addressed dataflow engine in `lib/artdag/` with analyze, plan,
incremental execute, effect-pipeline optimization, and a shared-cache federation
extension — the SX heart of art-dag, with media kernels and storage injected at the
edges.

## Status (rolling)

`bash lib/artdag/conformance.sh` → **0/0** (not yet started)

## Ground rules

- **Scope:** only `lib/artdag/**` and `plans/artdag-on-sx.md`. Do **not** edit
  `spec/`, `hosts/`, `shared/`, `lib/datalog/**`, `lib/persist/**`, or other
  `lib/<lang>/`. You may **import** the public APIs of `lib/datalog/` (analyze) and
  `lib/persist/` (memo cache / result store).
- **Design lineage, not code reuse.** The existing Python engine lives in the
  repo's top-level `artdag/` (core/ engine, `sexp_effects/`, l1/ tasks). **Read it
  for design lineage** (the 3-phase model, the effect language, content addressing)
  — do **not** import or port its code; this is a fresh SX implementation.
- **Media ops are opaque.** A node's op is an abstract SX function over its inputs
  in tests (e.g. `(fn (a b) …)`); real JAX/IPFS kernels are injected adapters
  behind an interface. The engine is about *scheduling/memo/incremental*, never
  pixels. Determinism: content ids and tests use only the node spec, never a clock.
- **Content addressing is structural.** A node's id is a deterministic digest of
  `(op, sorted input-ids, params)` so identical subgraphs share an id and a cache
  slot — the core property. Use a structural digest helper; if a real SHA-256/CID
  is needed it's an injected host primitive (Blockers if absent), not hand-rolled.
- **Shared-file issues** → "Blockers" with a minimal repro; do not fix here.
- **SX files:** `sx-tree` MCP tools only; `sx_validate` after every edit.
- **Commits:** one feature per commit. Keep Progress log updated and tick boxes.

## Architecture sketch

```
DAG spec (nodes + edges)                 rendered results
        │                                       ▲
        ▼                                       │
lib/artdag/dag.sx                       lib/artdag/execute.sx
  — node = {op, inputs, params}           — effect interp (perform per node)
  — content-id = digest(spec)             — content-addressed memo (persist)
  — topo order, validate                  — incremental: only dirty nodes
        │                                       ▲
        ▼                                       │
lib/artdag/analyze.sx                   lib/artdag/plan.sx
  — Datalog: deps/dependents/reach        — schedule: topo batches, parallelism
  — dirty propagation (dirty closure)     — (miniKanren constraints, later/opt)
        │                                       ▲
        ▼                                       │
lib/artdag/optimize.sx                  lib/artdag/federation.sx
  — fuse adjacent ops, dead-node elim,     — shared cache by content-id (L2-style)
    CSE (free from content-addressing)       result import/export + provenance/trust
```

## Phase 1 — DAG model + content addressing

- [ ] `lib/artdag/dag.sx` — node `{:op :inputs :params}`; structural content-id =
  digest of `(op, sorted input-ids, params)`; build/validate a DAG (no dangling
  inputs, no accidental cycles); topological order
- [ ] identical-subgraph sharing: two structurally-equal nodes get the same id
- [ ] `lib/artdag/tests/dag.sx` — id determinism, subgraph sharing, cycle/dangling
  rejection, topo order
- [ ] `lib/artdag/conformance.sh` + scoreboard

## Phase 2 — Analyze (Datalog)

- [ ] `lib/artdag/analyze.sx` — project edges to Datalog; `deps-of`, `dependents-of`,
  transitive `reachable` (the recursive-reachability shape)
- [ ] **dirty propagation:** given a set of changed nodes, compute the transitive
  set of dependents that must recompute (`dirty-closure`)
- [ ] `lib/artdag/tests/analyze.sx` — deep chains, diamonds, dirty closure
  correctness, unaffected nodes stay clean

## Phase 3 — Plan

- [ ] `lib/artdag/plan.sx` — schedule into topological **batches** (each batch's
  nodes have all deps satisfied → run in parallel); respect a max-parallelism limit
- [ ] plan over the *dirty* subset only (incremental plan)
- [ ] `lib/artdag/tests/plan.sx` — batch correctness, parallelism cap, dirty-only plan
- [ ] (optional/later) miniKanren constraint scheduling — flag, don't block on it

## Phase 4 — Execute (incremental + memoized)

- [ ] `lib/artdag/execute.sx` — interpret a plan: each node op runs via `perform`
  (mocked op in tests); results keyed by content-id
- [ ] **content-addressed memo cache** backed by `lib/persist/`: a node whose
  content-id already has a stored result is skipped (cache hit)
- [ ] **incremental execute:** re-running after a leaf change recomputes only the
  dirty closure; everything else is a cache hit
- [ ] `lib/artdag/tests/execute.sx` — full run, cache-hit on re-run, incremental
  recompute touches only dirty nodes (assert recompute count)

## Phase 5 — Effect-pipeline optimization

- [ ] `lib/artdag/optimize.sx` — rewrite the DAG before execution: dead-node
  elimination (unreachable from outputs), common-subexpression sharing (free from
  content ids), adjacent-op fusion
- [ ] optimizations are content-id-preserving where semantically identical; assert
  the optimized DAG produces identical results
- [ ] `lib/artdag/tests/optimize.sx` — DCE, CSE dedup, fusion equivalence
- [ ] (optional/later) rule-based optimization via `maude-on-sx`'s rewriting engine —
  flag the integration point, don't block on it

## Phase 6 — Federation (shared content-addressed cache)

- [ ] a result computed on one instance is reusable on another by content-id (the
  L2-registry analog): export/import `{content-id → result}` with provenance
- [ ] trust gating — accept a remote result only from a trusted peer (mirror the
  fed trust shape; mock the transport in tests)
- [ ] revocation/invalidation — drop a remote result if its provenance is withdrawn
- [ ] `lib/artdag/tests/fed.sx` — remote cache hit, trust gating, invalidation

## Progress log

(loop fills this in)

## Blockers

(loop fills this in)