content-refinement-agent

name	content-refinement-agent
description	Step 5 of the PaperOrchestra pipeline (arXiv:2604.05018). Iteratively refine drafts/paper.tex by simulating peer review and applying targeted revisions, with strict accept/revert halt rules, deterministic 0-100 decision bands (Accept/Minor/Major/Reject) that drive a target-met early stop, and a Devil's Advocate concession-threshold guard that blocks acceptance on unresolved critical findings. Maintains a worklog and snapshots each iteration so revert is real, not symbolic. TRIGGER when the orchestrator delegates Step 5 or when the user asks to "refine the draft", "iterate on the paper", or "run peer review on this paper".
data_access_level	verified_only

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 Refinement Agent prompt at `references/prompt.md`)

    3. snapshot iter<N>/ with new_paper.tex, review.json
       latexmk -pdf new_paper.tex → iter<N>/paper.pdf

    4. score(new_paper.tex) → curr_score

    5. decide via score_delta.py:
       - if curr.overall > prev.overall:                       ACCEPT
       - elif curr.overall == prev.overall and net_subaxis ≥0: ACCEPT
       - else:                                                 REVERT

    6. apply_worklog.py to append the decision

    7. if REVERT or no actionable weaknesses or iter == ITER_CAP: HALT

    paper.tex ← new_paper.tex   (only on ACCEPT)
    prev_score ← curr_score

cp <best iter>/paper.tex → workspace/final/paper.tex

The "best" snapshot at HALT is the one with the highest accepted overall score. On a REVERT halt, the best is the iteration immediately before the revert.

Step-by-step

0. Pre-refinement integrity gate

Before snapshotting or scoring the initial draft, run two gates in order:

Gate A — AI failure modes (load references/ai-failure-modes.md, runs once):

Load references/ai-failure-modes.md (which points to skills/shared/ai_failure_modes.md). Run all 7 checks against the draft and the inputs. This gate runs once only, at the start of iteration 1.

• CONFIRMED failure → write HALT entry to worklog.json, report to user, stop.
• SUSPECTED failure → add WARNING comment to paper.tex, log in worklog.json, continue.
• No failures → proceed.

Gate B — Claim-evidence provenance (runs once, WARN gate):

python skills/paper-orchestra/scripts/claim_evidence_gate.py \
    --paper workspace/drafts/paper.tex \
    --log   workspace/inputs/experimental_log.md \
    --out   workspace/claim_evidence_report.json

Exit 0 → PASS, proceed normally. Exit 1 → WARN: unsupported numeric claims found. Log in worklog.json as: {gate: "claim_evidence", status: "WARN", unsupported_count: N, report: "workspace/claim_evidence_report.json"} Pass the unsupported list from the report to the revision agent in Step 3 as an additional instruction: "The following numeric values appear in the paper but cannot be corroborated in experimental_log.md — verify or remove them: ..." Do NOT halt on Gate B warnings; the revision agent will address them.

Gate C — Read research brief (every run, no exit code):

If workspace/research_brief.md exists, read it before all reviewer calls. Pass the "Sections where evidence was thin" list from §4 as additional context to the Devil's Advocate reviewer. This surfaces the highest-risk sections for CRITICAL scrutiny.

0b. Snapshot the initial draft

python skills/content-refinement-agent/scripts/snapshot.py \
    --src workspace/drafts/paper.tex \
    --dst workspace/refinement/iter0/

This creates iter0/paper.tex. Then compile to iter0/paper.pdf:

cd workspace/refinement/iter0/ && latexmk -pdf -interaction=nonstopmode paper.tex

Score it (see Step 1 below) → iter0/score.json.

1. Simulate peer review

For each iteration N starting from 1:

Writing quality pre-check (start of every iteration): Load references/writing-quality-check.md and run the 5-category checklist (Categories A–E) against the current draft. Note violations and add them to the revision agenda.

Update critique memory before the reviewer call (iter N ≥ 2 only — skip for iter 1):

python skills/content-refinement-agent/scripts/update_critique_memory.py \
    --worklog workspace/refinement/worklog.json \
    --review  workspace/refinement/iter<N-1>/review.json \
    --iter    <N> \
    --out     workspace/refinement/critique_memory.json

This produces critique_memory.json with focus_on (persistent unresolved issues) and do_not_reflag (already-resolved issues). Inject both lists into the reviewer system prompt verbatim:

CRITIQUE MEMORY — you must honour this before reviewing:

FOCUS ON (flagged in prior iterations, not yet resolved — prioritise these):
<critique_memory.focus_on items, one per line>

DO NOT RE-FLAG (already addressed in prior iterations):
<critique_memory.do_not_reflag items, one per line>

This prevents the reviewer from re-discovering already-fixed issues and from missing genuinely stuck problems.

Load references/reviewer-rubric.md as the system prompt for the simulated reviewer call. The reviewer reads iter<N-1>/paper.pdf (or paper.tex if your host LLM lacks PDF input) and produces a JSON of strengths, weaknesses, questions, and per-axis scores.

The rubric is structured to mimic AgentReview (Jin et al., 2024) — the paper's chosen evaluator. We ship a faithful rubric in the references directory; the host agent's LLM does the actual reviewing.

Devil's Advocate reviewer: One simulated reviewer must be designated the DA following references/da-reviewer.md. The DA challenges core claims from first principles (causal overclaiming, ablation coverage, baseline fairness, generalization claims, novelty inflation) rather than surface polish. If the DA issues a CRITICAL finding that remains unaddressed after all reviewers weigh in, that finding blocks the "refinement accepted" decision regardless of rubric scores. Log DA CRITICAL findings in worklog.json: {da_critical: true, finding: "..."}.

Record the DA's per-round findings and concession decisions in workspace/refinement/da_concessions.json (schema in references/da-reviewer.md) and enforce the concession-threshold protocol deterministically — this stops the simulated DA from sycophantically caving:

python skills/content-refinement-agent/scripts/concession_guard.py \
    --log workspace/refinement/da_concessions.json \
    --out workspace/refinement/iter<N>/da_guard.json
# exit 0 = clear; exit 1 = standing CRITICAL → force REVERT this iteration;
# exit 2 = a concession was rejected (caving/consecutive) → DA must restate;
# exit 3 = schema error.

The guard rejects any concession made at rebuttal_score < 4 or in a round immediately following another concession, and restores the affected finding to "standing". A standing CRITICAL (exit 1) overrides an ACCEPT into a REVERT.

Save to workspace/refinement/iter<N>/review.json.

2. Score the draft

The reviewer call produces both qualitative feedback and a per-axis score:

{
  "axis_scores": {
    "scientific_depth":     {"score": 65, "justification": "..."},
    "technical_execution":  {"score": 70, "justification": "..."},
    "logical_flow":         {"score": 60, "justification": "..."},
    "writing_clarity":      {"score": 55, "justification": "..."},
    "evidence_presentation":{"score": 72,  
              
  
   
   
   ...
   ...
   ...

Save to iter<N>/score.json. (Combined with review.json if your host emits one document; the schemas overlap.)

decision_band is derived deterministically from overall_score — Accept (≥80) / Minor Revision (65–79) / Major Revision (50–64) / Reject (<50). Fill it in with python skills/content-refinement-agent/scripts/decision_band.py --score-json iter<N>/score.json rather than by hand, so it can never disagree with the number. The bands drive the target-met halt in Step 5.

3. Apply revision

Load the verbatim Content Refinement Agent prompt at references/prompt.md. Prepend the Anti-Leakage Prompt. Inputs:

• paper.tex — current draft
• paper.pdf — compiled PDF (multimodal context if available)
• conference_guidelines.md
• experimental_log.md — ground truth for numeric claims
• worklog.json — history of previous changes
• citation_pool.json — the allowed bibliography
• reviewer_feedback — the JSON from Step 1

The prompt instructs the model to address weaknesses, integrate question answers, and emit two output blocks:

1. A worklog JSON {addressed_weaknesses[], integrated_answers[], actions_taken[]}
2. The full revised LaTeX code

Save the revised LaTeX as iter<N>/paper.tex. Append the worklog JSON to workspace/refinement/worklog.json via apply_worklog.py.

4. Compile and re-score

cd workspace/refinement/iter<N>/ && latexmk -pdf -interaction=nonstopmode paper.tex

Then re-run the simulated review on the new draft → updated score.json for the new iteration. (This is the "re-score after revision" call.)

5. Apply the accept/revert decision

The calling loop must track CONSECUTIVE_SMALL (starts at 0) and pass it on each call so score_delta.py can detect the plateau:

python skills/content-refinement-agent/scripts/score_delta.py \
    --prev workspace/refinement/iter<N-1>/score.json \
    --curr workspace/refinement/iter<N>/score.json \
    --plateau-threshold 1.0 \
    --plateau-streak 3 \
    --accept-threshold 80 \
    --consecutive-small $CONSECUTIVE_SMALL \
    > workspace/refinement/iter<N>/delta.json

EXIT=$?
# Update streak for next iteration:
CONSECUTIVE_SMALL=$(python3 -c "
import json
d = json.load(open('workspace/refinement/iter<N>/delta.json'))
print(d['consecutive_small'])
")

Exit codes:

• 0 — ACCEPT (overall improved or tied with non-negative net sub-axis, below the Accept band, no plateau)
• 1 — REVERT (overall decreased)
• 2 — REVERT (tied overall, but net sub-axis change negative)
• 4 — HALT_PLATEAU (accepted but N consecutive iterations below threshold — stop early)
• 5 — HALT_TARGET_MET (accepted AND reached the Accept band, overall ≥ 80 — stop)

Behavior:

• ACCEPT (exit 0): keep iter<N>/paper.tex as the new best. Continue to iter N+1.
• REVERT (exit 1 or 2): copy iter<N-1>/paper.tex back as canonical, halt.
• HALT_PLATEAU (exit 4): keep current (it was accepted), but stop — further iterations are unlikely to yield meaningful gains. In practice ~85% of refinement gain comes in iteration 1; the plateau fires when subsequent iterations improve by less than 1 point for 3 consecutive rounds.
• HALT_TARGET_MET (exit 5): keep current (it was accepted), but stop — the paper has reached the Accept band (overall ≥ 80), so there is no reason to keep iterating and risk a regression. The delta.json carries decision_band_prev / decision_band_curr for the run report.

Override — DA CRITICAL. If concession_guard.py (Step 1) returned exit 1 for this iteration, treat the outcome as REVERT even when score_delta.py says ACCEPT: roll back to iter<N-1>/paper.tex and require the next revision to address the standing CRITICAL finding.

Always log the decision via apply_worklog.py --decision ....

6. Halt rules

Halt the loop when ANY of these is true:

1. Iteration count reaches ITER_CAP (default 3).
2. score_delta.py returned exit code 1 or 2 (REVERT), OR concession_guard.py returned exit 1 (standing DA CRITICAL → forced REVERT).
3. The simulated reviewer's weaknesses list is empty (no actionable feedback to apply).
4. score_delta.py returned exit code 4 (HALT_PLATEAU — plateau early-stop).
5. score_delta.py returned exit code 5 (HALT_TARGET_MET — reached the Accept band, overall ≥ 80; promote the current draft).

7. Promote the best snapshot

Identify the iteration with the highest accepted overall_score (this may be the latest accepted iteration, OR an earlier one if a later iteration was reverted). Copy:

cp workspace/refinement/iter<best>/paper.tex workspace/final/paper.tex
cp workspace/refinement/iter<best>/paper.pdf workspace/final/paper.pdf

Then in the final report, tell the user:

• How many iterations were run
• The final overall score and its decision band (Accept / Minor / Major / Reject)
• The score trajectory with bands (e.g., "iter0 58.0 Major → iter1 67.3 Minor (accept) → iter2 81.0 Accept (halt: target met)")
• Which iteration was promoted, and the halt reason (revert / plateau / target met / iter cap / DA critical)

Critical safety constraints (App. F.1 page 50–51)

The paper explicitly notes that early versions of the Refinement Agent "exploited the automated reviewer's scoring function by superficially listing missing baselines as limitations to artificially inflate acceptance scores." The verbatim prompt forbids this. You must honor it:

• [IRON RULE] Halt on score regression. If score_delta.py returns exit code 1 or 2 (REVERT), immediately revert to the previous snapshot and halt. No further revision attempts are permitted after a regression.
• [IRON RULE] No new experiments in revision. Ignore reviewer requests for new experiments, ablations, or baselines. The Refinement Agent's job is presentation, not new science. If the reviewer asks for missing data, simply skip those points — do NOT add fabricated experiments, do NOT add a "future work" item promising them.
• [IRON RULE] All numeric claims must match experimental_log.md. The agent cannot introduce new numbers, only re-present existing ones. Any number in the revised paper that does not appear in experimental_log.md is a hallucination.
• Never explicitly state a limitation. The phrase "we acknowledge as a limitation that..." is forbidden. The model can address weaknesses through clearer explanation, but must not game the evaluator by listing them defensively.

These rules prevent reward hacking and keep the refinement loop honest.

Resources

• references/prompt.md — verbatim Content Refinement Agent prompt from App. F.1
• references/reviewer-rubric.md — AgentReview-style scoring rubric (6 axes)
• references/halt-rules.md — accept/revert/halt logic in formal pseudocode
• references/safe-revision-rules.md — anti-reward-hack constraints
• references/writing-quality-check.md — 5-category anti-AI-prose checklist (pointer to shared)
• references/ai-failure-modes.md — 7-mode integrity gate run before first iteration (pointer to shared)
• references/da-reviewer.md — Devil's Advocate reviewer protocol and concession rules
• scripts/score_delta.py — accept/revert/halt decision from two score JSONs; emits decision bands + target-met halt (exit 5)
• scripts/decision_band.py — map an overall score to a canonical decision band (Accept/Minor/Major/Reject)
• scripts/concession_guard.py — enforce the DA concession-threshold protocol; blocks accept on a standing CRITICAL
• scripts/score_trajectory.py — per-dimension score history, regression and plateau detection
• scripts/apply_worklog.py — append iteration entries to worklog.json
• scripts/snapshot.py — copy paper.tex/paper.pdf into iter/ for rollback
• scripts/update_critique_memory.py — NEW build/update critique_memory.json from worklog + review (AutoSci-inspired reviewer memory)
• skills/shared/writing_quality_check.md — full anti-AI-prose checklist (5 categories)
• skills/shared/ai_failure_modes.md — full AI research failure modes gate (7 modes)
• skills/shared/handoff_schemas.md — formal data contracts between all pipeline steps
• skills/shared/research_brief_template.md — NEW research brief schema (read §1–§4 before first reviewer call)