# PaperSpine Article Options Report

Date: 2026-05-23

Folder audited:

`D:/kooopam_rl_low_varience/paper_spine_nonnormal_rl_closed_loops_20260523`

## Executive Decision

The folder is strongest for a **problem-first 6-page paper**:

> **Nonnormality in Reinforcement Learning Closed Loops: From Action Smoothing to Source-Amplifier Diagnostics**

The best paper is not a broad safe-RL benchmark paper and not a full CG-RVC algorithm paper. It should argue that in continuous-control RL, action jitter is a source, while the actual downstream risk depends on how residual actions, exploration noise, execution noise, and disturbances are injected into a nonnormal closed-loop amplifier. The folder already contains enough evidence to write this as a compact diagnostic/control framework with local-surrogate and simulation-level claims.

## What The Folder Currently Contains

| Evidence Class | Current Artifacts | Writing Value |
|---|---|---|
| PaperSpine workflow package | `paper_spine_config.md`, `confirmed_motivation.md`, `paper_plan.md`, `section_blueprints.md`, `writing_rationale_matrix.md` | The writing motivation and six-page structure are already fixed. |
| Literature/reference basis | `references/deep-research-report (49).md`, `references/final_report.md`, `references/REFERENCE_INDEX.md`, `research_dossier.md`, `sota_gap_map.md` | Useful for framing and citation mining; final citations still need DOI/original-paper verification. |
| Evidence bank | `evidence_bank.md`, `project_evidence_index.md`, `project_evidence_dossier.md`, `experiment_claim_matrix.md` | Enough to map claims to internal evidence and caveats. |
| Figures | `evidence_materials/selected_figures/*.png`, `figure_asset_map.md` | Ready for draft figures: source-amplifier diagram, CI-1, CI-2, 2D ablation, Full3D, conditional proxy, ControlGym. |
| Draft paper | `final_paper/main.tex`, `final_paper/main.pdf` | A minimal compiled scaffold already exists, but it is not yet the evidence-rich final paper. |
| Project-wide provenance | `project_*_evidence_candidates.json`, copied project indices under `evidence_materials/project_indices/` | Good audit trail for where code, tables, and figures came from. |

## Article Option A: Short Problem Paper

**Recommended status:** Write first.

**Working title:**

> Nonnormality in Reinforcement Learning Closed Loops: From Action Smoothing to Source-Amplifier Diagnostics

**Article type:**

6-page conference/workshop short paper, technical note, or arXiv-style position-plus-evidence paper.

**Core thesis:**

Action jitter in continuous-control RL is not the final risk. Residual actions and execution noise become risky when propagated through a nonnormal closed-loop amplifier, where they appear as state covariance, safety-margin loss, posterior Bellman-Lyapunov residuals, and projection burden.

**Why this is the strongest fit:**

- The folder already has a confirmed PaperSpine motivation matching this thesis.
- `paper_plan.md` already defines a clean six-page structure.
- CI-1 and CI-2 directly separate amplifier and source.
- The 2D ablation supports the critique of ordinary `Var(u)` and action-diff smoothing.
- Projection burden and residual-feedback diagnostics make the mechanism operational.
- Limitations are well developed, preventing overclaiming.

**Available spine:**

1. Introduction: stable/high-return but jittery or unsafe policies motivate source-amplifier risk.
2. Minimal model: `z_{t+1}=Kz_t+Phi_u r_t+w_t`.
3. Critique: scalar/temporal smoothing is not amplifier-aware.
4. Response: local surrogate, Lyapunov certificate, `M=Phi_u^T P Phi_u`, P-ALE, smoothing, projection.
5. Evidence: CI-1, CI-2, 2D ablation, projection burden, residual-feedback proxy.
6. Limitations: local surrogate, simulation evidence, residual feedback, projection feasibility, P-ALE does not reduce nonnormality.

**Main evidence anchors:**

- `experiment_claim_matrix.md`
- `evidence_bank.md`, E13-E18 and E22
- `project_evidence_dossier.md`, Sections 3-4
- `evidence_materials/selected_figures/fig1_ci1_amplifier_isolation.png`
- `evidence_materials/selected_figures/fig2_ci2_source_intervention.png`
- `evidence_materials/selected_figures/suite_high_noise_comparison.png`
- `final_paper/main.tex` and `final_paper/main.pdf`

**Claim level:**

Local surrogate diagnostics and numerical simulation. No global nonlinear safety theorem.

**What still needs work before submission:**

- Replace placeholder scaffold prose with evidence-rich paragraphs.
- Verify formal citations from original DOI-level papers.
- Connect every final figure to raw data and plotting script.
- Decide whether CI-3 is included as planned/future work or newly run.

## Article Option B: CG-RVC Method Paper

**Recommended status:** Possible, but not the best use of this folder if the target is a 6-page paper.

**Possible title:**

> Certificate-Guided Residual Variance Control for Nonnormal Reinforcement Learning Closed Loops

**Article type:**

Longer algorithm paper, extended technical report, or journal-style manuscript.

**Core thesis:**

CG-RVC combines a local Koopman/linear surrogate, Lyapunov certificate, P-weighted residual covariance penalty, execution smoothing, rate limiting, and safety projection to reduce downstream variance and repair burden.

**Why it is possible:**

- The folder contains a long source-amplifier manuscript route in `source_inventory.md`.
- `project_evidence_dossier.md` captures 2D ablation, projection burden, residual-feedback proxy, Full3D, and ControlGym.
- The method pieces are already described in `paper_plan.md` and `evidence_bank.md`.

**Why it is weaker as the immediate paper:**

- The user explicitly reframed the work away from a CG-RVC-centered method paper.
- A method paper needs more code/API detail, raw provenance, hyperparameter tables, and broader reproducibility packaging.
- The current `final_paper/main.tex` is a problem-paper scaffold, not a full algorithm paper.

**Claim level:**

Method-level simulation evidence. Still no global nonlinear safety guarantee.

**Needed additions:**

- Full algorithm box.
- Complete training protocol.
- Exact hyperparameters and seed tables.
- Stronger raw-data provenance for each table.
- Possibly a larger-seed ablation if targeting a stricter venue.

## Article Option C: Controlled-Intervention Note

**Recommended status:** Strong as a short note or appendix paper; useful if a sharper mechanism-only contribution is desired.

**Possible title:**

> Separating Sources and Nonnormal Amplifiers in Reinforcement Learning Closed Loops

**Article type:**

2-4 page technical note, appendix note, workshop short paper, or mechanism section.

**Core thesis:**

CI-1 and CI-2 show that downstream covariance depends on both the closed-loop amplifier and the source statistics; action variance alone is not an adequate diagnostic.

**Best evidence:**

- CI-1: fixed eigenvalues/source/input, sweep nonnormal geometry.
- CI-2: fixed amplifier, change input source statistics.
- `fig1_ci1_amplifier_isolation.png`
- `fig2_ci2_source_intervention.png`
- `experiment_claim_matrix.md`

**Strength:**

Very clean causal structure.

**Weakness:**

Without 2D RL and projection burden, it becomes more of a control-theory demonstration than an RL paper.

**Needed additions:**

- CI-3 direction-aware source scan would make this much stronger.
- Variance-matched colored noise and spectrum-matched amplitude scaling would close the CI-2 caveat.

## Article Option D: Diagnostic Protocol / Measurement Paper

**Recommended status:** Possible after one more organization pass.

**Possible title:**

> Source-Amplifier Diagnostics for Low-Variance Safe Reinforcement Learning

**Article type:**

Diagnostic framework paper, benchmark protocol paper, or reproducibility report.

**Core thesis:**

Safe low-variance RL should report source metrics, amplifier metrics, certificate metrics, downstream consequences, and execution repair metrics together.

**Evidence support:**

- `paper_plan.md` diagnostic table.
- `experiment_claim_matrix.md`.
- Projection burden.
- Residual-feedback proxy.
- ControlGym stress test.
- Surrogate/certificate audit.

**Main contribution:**

A reporting protocol:

- Source: `tr(Sigma_r)`, applied variance, diff MSE.
- Amplifier: `rho(K)`, `kappa(V)`, `G_peak`, resolvent norm.
- Certificate: `P`, `gamma_P`, `M=Phi_u^T P Phi_u`.
- Consequence: state covariance trace, dense violation, B-L residual.
- Execution: projection trigger/correction.

**Weakness:**

It needs more explicit protocol tables and possibly a checklist-style benchmark definition.

**Good use:**

This can be merged into Option A as Table 1, or become a standalone appendix/report.

## Article Option E: Quadrotor Simulation Relevance Note

**Recommended status:** Use as supporting section or supplementary note, not as the main article.

**Possible title:**

> Source-Side Variance Suppression in Planar and Full3D Quadrotor Numerical Closed Loops

**Article type:**

Simulation note or supplementary section.

**Evidence support:**

- Planar UAV bridge.
- Full3D 5-seed high-noise summary.
- `full3d_5seed_high_noise_summary.png`.
- `suite_high_noise_comparison.png`.
- Code route through `run_planar_quadrotor_generalization_suite.py`.

**Why it should not be the main paper now:**

- The current framing is nonnormality in RL closed loops, not quadrotor control.
- Full3D is simulation-level source-side evidence, not hardware validation.
- Payload Mismatch is a useful boundary case but weakens any simple "quadrotor method wins" story.

**Best use:**

One compact paragraph or supplementary figure in Option A.

## Article Option F: Failure Modes / Boundary Conditions Paper

**Recommended status:** Useful later, not the first paper.

**Possible title:**

> When Source Shaping Is Not Enough: Boundary Cases in Nonnormal RL Closed Loops

**Core thesis:**

Source-side variance control helps only when the residual source remains the right object of control; it can fail or become projection-dominated when feedback, infeasibility, model mismatch, or high-gain alignment dominates.

**Evidence support:**

- Payload Mismatch in planar UAV.
- CM2 ControlGym projection-heavy boundary.
- `gamma_P=1.43` surrogate audit.
- Residual-feedback caveat.

**Weakness:**

Needs more systematic failure experiments to stand alone.

**Best use:**

Use these materials as the limitations section of Option A.

## Articles Not Recommended From This Folder Alone

| Not Recommended Article | Why Not |
|---|---|
| Broad safe-RL benchmark paper | Evidence is not organized as a comprehensive benchmark; matched 2D ablation is strong, but broad claims would overreach. |
| Global nonlinear safety theorem paper | Folder explicitly says certificates are local-surrogate diagnostics; `gamma_P` audit cautions against hard ISS claims for the fitted model. |
| Hardware quadrotor deployment paper | Evidence is numerical simulation only. |
| Full literature survey on nonnormal RL | The reports provide literature scaffolding, but the folder's unique value is mechanism + diagnostics, not exhaustive survey. |
| Pure action-smoothing paper | The central motivation is to move beyond ordinary action smoothing. |

## Ranking

| Rank | Article | Readiness | Best Use |
|---:|---|---|---|
| 1 | Option A: 6-page source-amplifier problem paper | High | Write now. |
| 2 | Option C: controlled-intervention note | Medium-high | Use as core experiment section or workshop note. |
| 3 | Option D: diagnostic protocol paper | Medium | Merge into Option A or develop as report. |
| 4 | Option B: CG-RVC method paper | Medium | Possible longer paper, but not the current best positioning. |
| 5 | Option E: quadrotor simulation note | Medium-low | Supporting/supplementary evidence. |
| 6 | Option F: failure modes paper | Low-medium | Limitations now; standalone later after more systematic failures. |

## PaperSpine Recommendation

Write **Option A** as the main paper and treat all other options as supporting layers:

- Option C supplies the mechanism proof section.
- Option D supplies the diagnostic table and reporting checklist.
- Option B supplies the minimal certificate-guided response framework.
- Option E supplies simulation-level relevance.
- Option F supplies limitations and failure modes.

The current `final_paper/main.tex` already implements the skeleton of Option A. The next writing step should not be choosing a new topic. It should be expanding the scaffold with the evidence now archived in `project_evidence_dossier.md`, `experiment_claim_matrix.md`, and `evidence_bank.md`.

## Immediate Next Drafting Move

Turn `final_paper/main.tex` from a scaffold into a real evidence-rich draft by adding:

1. A stronger abstract with CI-1, CI-2, and 2D ablation numbers.
2. A compact diagnostic table.
3. A CI-1/CI-2 figure block.
4. A compressed 2D ablation table or paragraph.
5. A limitations paragraph that explicitly includes colored input, local surrogate scope, residual feedback, and simulation-only evidence.

This would produce a defensible short paper without changing the folder's claim level.
