Automating Video Editing with Verified Planning

# Illustrative sketch (not from the paper)
import torch
from torch import nn

# placeholder model that emits a JSON‑Patch (RefinePatch)
class Planner(nn.Module):
    def forward(self, brief, clip_pool, music_meta, timeline, ledger):
        # returns a dummy patch dict
        return {"op": "add", "path": "/clips/-", "value": {"id": "clip42"}}

# deterministic verifier that applies a patch and updates ledger
def verifier(patch, timeline, ledger):
    # apply patch (simplified) and recompute constraints
    timeline["clips"].append(patch["value"])  # structural update
    # dummy constraint check – returns True if constraints satisfied
    ledger["violations"] = []
    return True, timeline, ledger

# Stage 2 self‑improvement (RefineCut‑Evo) sketch
K = 4
lam = 0.65
planner = Planner()
for state in training_states:  # assumed iterable of (brief, pool, music, timeline, ledger)
    candidates = []
    for _ in range(K):
        patch = planner(*state)
        ok, new_tl, new_ledger = verifier(patch, state[3].copy(), state[4].copy())
        # rubric score R(c) – placeholder constant
        R = 0.5
        V = 1.0 if ok else 0.0
        S = lam * V + (1 - lam) * R
        candidates.append((patch, S))
    # select high‑margin preferred/rejected pair (simplified)
    best = max(candidates, key=lambda x: x[1])
    worst = min(candidates, key=lambda x: x[1])
    # DPO update would use (best, worst) – omitted for brevity
    pass

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder import

// dummy planner that emits a RefinePatch JSON‑Patch
class Planner {
  forward(brief, clipPool, musicMeta, timeline, ledger) {
    return { op: 'add', path: '/clips/-', value: { id: 'clip42' } };
  }
}

// deterministic verifier applying a patch and recomputing ledger
function verifier(patch, timeline, ledger) {
  timeline.clips.push(patch.value); // structural update
  ledger.violations = []; // assume constraints now satisfied
  return { ok: true, timeline, ledger };
}

// Stage 2 self‑improvement (RefineCut‑Evo) sketch
const K = 4;
const lam = 0.65;
const planner = new Planner();
for (const state of trainingStates) { // state = [brief, pool, music, timeline, ledger]
  const candidates = [];
  for (let i = 0; i < K; i++) {
    const patch = planner.forward(...state);
    const { ok, timeline: newTl, ledger: newLd } = verifier(patch, { ...state[3] }, { ...state[4] });
    const R = 0.5; // placeholder rubric score
    const V = ok ? 1.0 : 0.0;
    const S = lam * V + (1 - lam) * R;
    candidates.push({ patch, S });
  }
  const best = candidates.reduce((a, b) => (a.S > b.S ? a : b));
  const worst = candidates.reduce((a, b) => (a.S < b.S ? a : b));
  // DPO update would use (best, worst) – omitted for brevity
}