content-refinement-agentlisted

# Content Refinement Agent (Step 5) Faithful implementation of the Content Refinement Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 5, App. F.1 pp. 49–51). **Cost: ~5–7 LLM calls** (App. B), typically ~3 refinement iterations, each consisting of one reviewer call and one revision call. The paper highlights this step as one of the largest contributors to overall quality: refinement alone accounts for +19% (CVPR) and +22% (ICLR) absolute acceptance-rate improvement (Fig. 4). Get this step right. ## Inputs - `workspace/drafts/paper.tex` — output of Step 4 - `workspace/inputs/conference_guidelines.md` - `workspace/inputs/experimental_log.md` — used as ground truth for the hallucination check - `workspace/citation_pool.json` / `workspace/refs.bib` — the allowed bibliography ## Outputs - `workspace/refinement/iter1/`, `iter2/`, `iter3/` — per-iteration snapshots containing `paper.tex`, `paper.pdf`, `review.json`, `score.json` - `workspace/refinement/worklog.json` — append-only history of decisions - `workspace/final/paper.tex` and `workspace/final/paper.pdf` — copy of the best accepted snapshot ## The refinement loop ``` prev_score = score(paper.tex) # baseline from initial draft snapshot iter0/ for iter in 1..ITER_CAP (default 3): 1. simulate_review(paper.tex) → review.json (uses `references/reviewer-rubric.md` rubric) 2. apply_revision(paper.tex, review.json) → new_paper.tex (uses verbatim Refinem