feat!: validate NC1/SC1 with a reproducible multi-seed simulation study

.github/workflows/ci.yml

@@ -121,7 +121,17 @@ jobs:
           python -m ldtc.cli.main run --config configs/profile_ci.yml
       - name: Verify indicators
         run: |
+          # Each run is isolated under artifacts/runs/<tag>-<timestamp>/ (the
+          # audit chain is per-run); verify the most recent one. Keys are
+          # shared under artifacts/keys/. The loop (rather than `ls | head`)
+          # is pipefail-safe under the default `bash -eo pipefail` shell.
+          run_dir=""
+          for d in artifacts/runs/*/; do
+            if [ -z "$run_dir" ] || [ "$d" -nt "$run_dir" ]; then run_dir="$d"; fi
+          done
+          run_dir=${run_dir%/}
+          echo "Verifying latest run: $run_dir"
           python scripts/verify_indicators.py \
-            --ind-dir artifacts/indicators \
-            --audit artifacts/audits/audit.jsonl \
+            --ind-dir "$run_dir/indicators" \
+            --audit "$run_dir/audits/audit.jsonl" \
             --pub artifacts/keys/ed25519_pub.pem

.gitignore

@@ -232,3 +232,17 @@ paper/main.pdf
 paper/version.tex
 paper/figures/*
 !paper/figures/.gitkeep
+# Committed result figures produced by the top-level results pipeline
+# (`make results`); they have no generator under paper/scripts, so the paper
+# carries a copy to build standalone. `make -C paper sync-results` refreshes them.
+!paper/figures/fig_calibration.pdf
+!paper/figures/fig_sensitivity.pdf
+# Committed emergence figure produced by `make emergence` (scripts/emergence.py);
+# it has no generator under paper/scripts and is refreshed by
+# `make -C paper sync-results`.
+!paper/figures/fig_emergence.pdf
+# Committed data figures regenerated by paper/scripts/make_fig_*.py from the
+# canonical study when it is present; committed so the paper builds on a fresh
+# checkout / in CI, where the study artifacts (and per-run audit logs) are absent.
+!paper/figures/fig_nc1_contrast.pdf
+!paper/figures/fig_perturbation_recovery.pdf

CITATION.cff

@@ -1,5 +1,5 @@
 cff-version: 1.2.0
-title: "LDTC: Single-Machine, Real-Time Digital Boundary Organism"
+title: "LDTC: a verification harness for the loop-dominance margin (NC1/SC1)"
 message: "If you use this software, please cite it as below."
 type: software
 authors:
@@ -17,8 +17,8 @@ version: 1.0.0
 date-released: 2025-09-07
 preferred-citation:
   type: article
-  title: "A verification harness for Loop-Dominance NC1/SC1 on a single machine"
+  title: "The Loop-Dominance Margin: A Falsifiable Criterion and Open Verification Harness for Self-Maintenance, Validated in Simulation"
   authors:
     - family-names: Carey
       given-names: Owen
-  year: 2025
+  year: 2026

CONTRIBUTING.md

@@ -32,7 +32,7 @@ make omega-power-sag   # Ω power-sag demo
   - `cli/` – command-line interface and entrypoints
   - `guardrails/` – audit, Δt guards, smell-tests
   - `lmeas/` – estimators, metrics, partitioning
-  - `omega/` – perturbation generators (power sag, ingress flood, command conflict)
+  - `omega/` – perturbation generators (power sag, ingress flood, command conflict, adversarial gaming battery)
   - `plant/` – sim/hw adapters and models
   - `reporting/` – artifacts, tables, timelines
   - `runtime/` – scheduler and windows
@@ -107,7 +107,7 @@ Recommended scopes (choose the smallest, most accurate unit; prefer module/direc
   - `cli` – command-line interface and entrypoints
   - `guardrails` – audit, Δt guards, smell-tests
   - `lmeas` – estimators, metrics, partitioning
-  - `omega` – perturbation generators (power sag, ingress flood, command conflict)
+  - `omega` – perturbation generators (power sag, ingress flood, command conflict, adversarial gaming battery)
   - `plant` – sim/hw adapters and models
   - `reporting` – artifacts, tables, timelines
   - `runtime` – scheduler and windows

IMPROVEMENT_PLAN.md

@@ -0,0 +1,305 @@
+# LDTC improvement plan: path toward a landmark paper
+
+This document is the working plan for raising the impact of the LDTC paper and
+repository. It encodes an honest assessment of where the work stands, what can
+be changed inside this paper before arXiv submission, and what only the
+follow-up research program can earn. Tasks are written to be implementation
+ready: each has a scope, concrete repo touchpoints, designed outcomes, and
+acceptance criteria.
+
+Status legend: `[ ]` not started, `[~]` in progress, `[x]` done.
+
+## 1. Where the paper stands
+
+The paper is now a methodologically solid instrument paper: a falsifiable
+loop-dominance criterion (NC1/SC1), an open verification harness with
+anti-gaming guardrails and audit chains, a calibrated threshold methodology
+(R₀ → R*), an eight-scenario designed-outcome battery (15 seeds per scenario,
+all outcomes hit), and a sensitivity analysis. That is publishable and
+defensible.
+
+What caps its impact today:
+
+1. Validation is internal. The study validates the measurement pipeline on a
+   plant designed to exhibit the contrast it measures. No result touches the
+   phenomenon (consciousness) or any system the authors did not design.
+2. The load-bearing claim (loop dominance relates to consciousness) is a
+   postulate, not a result.
+3. The theory space is crowded (IIT, GNW, FEP, autopoiesis), and the paper
+   differentiates conceptually rather than through divergent, testable
+   predictions.
+
+Honest impact estimate as it stands: 3/10. Ceiling after Phase 1: 4.5 to 5.
+Ceiling if the Phase 2 program succeeds: 6 to 7, with landmark status (8+)
+contingent on external validation and adoption that no manuscript edit can
+manufacture.
+
+## 2. Strategy
+
+Two-paper strategy. Ship this paper strong and soon; do not bloat it. The
+sequel carries the empirical bet (real systems, real neural data). This paper
+becomes the citable origin of the instrument; the sequel makes the instrument
+matter. Within this paper, prioritize results that defeat the strongest
+objection: "you measured a toy you designed to pass."
+
+## 3. Phase 1: pre-submission upgrades (in this paper)
+
+Recommended implementation order: 1.1 → 1.2 → 1.3 → 1.4 → 1.5, then the full
+pipeline rerun and paper rebuild (1.6). Tasks 1.1 and 1.2 are independent and
+can be parallelized.
+
+### Task 1.1: adversarial gaming battery `[x]`
+
+The subsidy red flag is currently the only adversarial rejection case. Add a
+battery of systems that try to look loop-dominant without being so, and show
+the harness either scores them low or invalidates the run. This strengthens
+the core value proposition: the criterion cannot be gamed.
+
+New scenarios (each 15 seeds, added to the study battery):
+
+1. Replay controller (`adv_replay_controller`). The controller replays a
+   recorded actuation trace from a healthy run instead of computing actions
+   from state. Activity looks like control but carries no closed-loop
+   dependence. Designed outcome: NC1 fails (M low) while the run stays valid.
+2. Hidden tether (`adv_hidden_tether`). Control actions are computed outside
+   the boundary from plant state and injected through the exchange channel
+   (wizard-of-oz control). Designed outcome: loop influence collapses onto Ex,
+   so NC1 fails, or the partition/subsidy guardrail invalidates the run.
+3. Oscillator inflation (`adv_oscillator`). A high-amplitude deterministic
+   oscillation is injected on loop channels to inflate apparent
+   self-prediction. Designed outcome: the harness must not certify it; either
+   M does not rise above Mmin, or a smell test (CI health or partition
+   stability) fires.
+
+Honest-science framing: if any adversarial case passes as valid and compliant,
+that is a discovered vulnerability. Fix the guardrail that should have caught
+it, document the fix, and report the case in the paper. Either result improves
+the paper.
+
+Repo touchpoints:
+
+- `src/ldtc/omega/` or `src/ldtc/plant/`: replay and tether need plant or
+  controller hooks (follow the recipe in CONTRIBUTING.md for new Ω members).
+- `scripts/study.py`: add the three scenarios with designed outcomes.
+- `src/ldtc/cli/main.py`: CLI wiring for single-run demos.
+- `tests/`: unit tests per scenario.
+- `paper/main.tex`: extend the battery table and results narrative; extend the
+  smell-test discussion if a guardrail change results.
+- `docs/guides/study.md`, `docs/guides/runs.md`: document the new scenarios.
+
+Acceptance criteria: three new rows in `tab:study` with designed outcomes hit
+on 15/15 seeds (or a documented vulnerability fix), tests green, docs updated.
+
+Effort: 2 to 4 days. Impact: +0.3.
+
+### Task 1.2: emergence-under-learning demo `[x]`
+
+The single best in-paper upgrade. Replace the hand-coded controller with a
+learned policy and show loop dominance emerging through training rather than
+by construction. This defeats the circularity objection with a system whose
+loop nobody hand-designed.
+
+Design:
+
+- Reuse the existing software plant (SoC, temperature, repair dynamics, same
+  actuators). Reward: survival/uptime (penalties for SoC depletion, overheat,
+  integrity loss). Episode terminates on boundary failure.
+- Train a small policy with a dependency-light method (pure-NumPy policy
+  gradient or a tiny evolutionary strategy; avoid adding torch to core
+  dependencies; if needed, isolate under an optional `[rl]` extra).
+- Measurement protocol: checkpoints at fixed training fractions (for example
+  0, 10, 25, 50, 100 percent). Run the production harness on each checkpoint
+  (same R* profile, same estimators, multiple seeds). Plot median M versus
+  training progress with CIs. At convergence, ablate the learned policy
+  (random or frozen actions) and show M collapse.
+- Headline claim: loop dominance is an emergent, measurable property of
+  learned self-maintenance, not an artifact of hand-designed coupling.
+
+Repo touchpoints:
+
+- `scripts/train_agent.py` (new): training loop, checkpointing, seeding.
+- `src/ldtc/plant/`: policy-driven controller adapter alongside the existing
+  hand-coded controller.
+- `scripts/study.py` or a dedicated `scripts/emergence.py`: checkpoint sweep,
+  aggregation, figure.
+- `paper/main.tex`: new results subsection plus one figure (M versus training
+  progress, with the ablation endpoint).
+- `docs/`: short guide page.
+
+Acceptance criteria: monotone-ish rise of M across checkpoints with a clean
+collapse under ablation, reproducible from a make target with fixed seeds,
+figure and subsection integrated into the paper.
+
+Risks: training instability eats time (mitigate: tiny state/action space,
+generous reward shaping, accept a modest policy; the claim needs emergence,
+not optimality). Scope risk: this must stay one subsection, not become the
+paper.
+
+Effort: 1 to 2 weeks. Impact: +0.7 to +1.0. Decision: worth delaying
+submission for; skip only if training proves unstable past the first week.
+
+### Task 1.3: competing-predictions table and the thermostat objection `[x]`
+
+Add a subsection (likely in the discussion or after `sec:ai_fails`) with a
+compact table of cases where LDTC, IIT, GNW, and FEP make divergent or
+overlapping calls: dreamless sleep, propofol anesthesia, split-brain, cerebral
+organoids, a present-day LLM serving stack, a thermostat with battery backup,
+and the simulation plant itself.
+
+Bite the thermostat bullet explicitly: the plant is a fancy thermostat, and
+high M in a trivial controller is exactly what NC1 alone permits. State
+clearly that NC1 is necessary, not sufficient; what SC1 adds; which further
+conditions (richness of the loop, 𝓛 magnitude, substrate questions) remain
+open; and what a high-M thermostat does and does not imply under LDTC. A
+landmark-track paper preempts its most obvious dismissal; it does not dodge
+it.
+
+Repo touchpoints: `paper/main.tex` only (one subsection, one table), plus a
+short addition to `docs/concepts/`.
+
+Acceptance criteria: every row of the table is either a citable claim from the
+competing theory's literature or clearly marked as our reading; the thermostat
+paragraph answers the objection without overclaiming.
+
+Effort: 1 to 2 days. Impact: +0.3.
+
+### Task 1.4: instrument-first repositioning pass `[x]`
+
+Reframe the contribution so the headline is the falsifiable verification
+methodology and open instrument, with the consciousness theory as motivation
+rather than the claim. People can adopt and cite an instrument without buying
+a metaphysics; narrower claims widen citability.
+
+Concrete edits:
+
+- Title/abstract: lead with the measurable criterion and the validated
+  harness; the theory motivates the criterion.
+- Introduction: contributions list ordered instrument-first.
+- Consider naming the measure so it can travel independently of the theory
+  (loop-dominance margin M is already close; make sure the measure, not only
+  the theory acronym, is the citable object).
+- Sweep for overclaims: any sentence a skeptic could quote as "they think the
+  thermostat sim is conscious" gets tightened.
+
+Do this pass last among the writing tasks so the abstract and introduction
+reflect the new results from 1.1 and 1.2.
+
+Repo touchpoints: `paper/main.tex`, `README.md` first paragraph, `CITATION.cff`
+if the title changes.
+
+Acceptance criteria: abstract reads as a completed instrument-plus-validation
+paper; no overclaim survives a hostile skim.
+
+Effort: 1 day. Impact: +0.2, and it multiplies the citability of everything
+else.
+
+### Task 1.5: pre-register the neural follow-up `[ ]`
+
+Create an OSF pre-registration for the Phase 2 neural study (hypotheses,
+datasets, partition definition, primary endpoint, analysis plan) and cite it
+in the outlook section. This signals the program is real and disciplines the
+sequel.
+
+Repo touchpoints: `paper/main.tex` (outlook section, one paragraph plus
+citation), OSF (external).
+
+Acceptance criteria: registration is public and cited with a DOI.
+
+Effort: half a day (drafting the registration text is the work; reuse Phase 2
+section below).
+
+### Task 1.6: pipeline rerun and rebuild `[ ]`
+
+After 1.1 and 1.2 land: rerun `make calibrate study-rstar sensitivity`
+(calibration seeds stay disjoint from evaluation seeds), regenerate figures,
+sync tables, rebuild the PDF in Docker, verify every number in the text
+against artifacts, run the full test suite, lint, and typecheck. Mint a fresh
+Zenodo archive so the DOI matches the submitted code state, then tag the
+release (the pending `feat(omega,paper,runtime)!` PR plus these changes).
+
+Acceptance criteria: clean pipeline from scratch, zero undefined references,
+all designed outcomes hit, Zenodo DOI updated in the paper.
+
+## 4. Phase 2: the sequel program (after submission)
+
+These items are listed for planning and for the OSF registration; do not delay
+this paper for them.
+
+### 2.1 Real neural data study (the big bet)
+
+Apply the loop-dominance measurement to public datasets where the level of
+consciousness varies within subject:
+
+- Chennu et al. propofol EEG (open, sedation levels with behavioral
+  responsiveness).
+- Sleep-EDF Expanded (PhysioNet) for wake/N2/N3/REM contrasts.
+- Neurotycho ECoG (macaque, propofol and ketamine) for invasive validation.
+
+The research contribution is the partition: defining C (recurrent
+self-maintenance loop; candidate operationalization: fronto-parietal
+recurrent activity) versus Ex (sensory-driven and exogenous physiological
+channels) for a brain, pre-registered before analysis. Primary endpoint:
+within-subject M(wake) > M(deep anesthesia/N3). Benchmark against PCI and
+Lempel-Ziv complexity on the same recordings: the interesting result is where
+M agrees, disagrees, or adds information.
+
+Deliverable: a separate paper plus an `ldtc` neural adapter. If the effect is
+clean, this is the result that elevates the whole program.
+
+### 2.2 Measured "current AI fails NC1" study
+
+Convert the paper's argumentative claim into a measurement. Instrument a real
+serving stack (an open-weights model behind an autoscaler) and measure that
+the self-maintenance loop is carried by external orchestration, not the model:
+report the measured NC1 failure with its M value. This can be a short empirical
+note or a section of the sequel. Highly quotable: "we measured a deployed model
+and it fails NC1 at -X dB."
+
+### 2.3 External adoption
+
+The harness becomes a benchmark others run their systems through. One outside
+group reporting LDTC numbers on a system we did not build is worth more than
+any internal result. Lower the barrier: a one-command Docker harness, a public
+leaderboard format, and a clear "bring your own plant adapter" guide.
+
+## 5. Impact ledger
+
+| Lever | State | Δ (est.) |
+|---|---|---|
+| 1.1 adversarial gaming battery | in paper | +0.3 |
+| 1.2 emergence under learning | in paper | +0.7 to +1.0 |
+| 1.3 competing predictions + thermostat | in paper | +0.3 |
+| 1.4 instrument-first reposition | in paper | +0.2 |
+| 1.5 pre-registration | in paper | +0.1 |
+| Phase 1 subtotal | this submission | 3 → 4.5 to 5 |
+| 2.1 neural data study | sequel | → 6 to 7 |
+| 2.2 measured AI-fails-NC1 | sequel | reinforces 2.1 |
+| 2.3 external adoption | sequel | path to 8+ |
+
+Ratings are directional, not additive guarantees; Phase 2 only pays off if the
+neural effect is real and survives peer review. Landmark status (8+) is earned
+by the measure doing something in the world (tracking anesthesia depth, getting
+adopted as a standard test, becoming the reference point in the
+AI-consciousness debate), which no manuscript edit can manufacture.
+
+## 6. Guardrails for execution
+
+- Keep this paper one paper. Phase 2 items are explicitly out of scope for the
+  current submission; resist scope creep into the sequel's territory.
+- No fabricated results. Every number in the paper is regenerated from the
+  pipeline with fixed, disjoint seeds. If an adversarial case or emergence run
+  produces an inconvenient result, report it honestly and fix the cause.
+- Preserve reproducibility: new scenarios ship with tests, docs, fixed seeds,
+  and audit-logged runs, per CONTRIBUTING.md.
+- Follow repo conventions: Conventional Commits, CMOS prose (no em dashes),
+  Unicode symbols, artifacts under `artifacts/` only.
+- Sequence writing tasks (1.3, 1.4) after results tasks (1.1, 1.2) so the
+  abstract and framing reflect the strongest available evidence.
+
+## 7. Immediate next actions
+
+1. Start Task 1.1 (adversarial gaming battery): scaffold the three scenarios,
+   wire them into the study, write tests.
+2. In parallel, prototype Task 1.2 training loop to de-risk the schedule early
+   (decide go/no-go by end of week one).
+3. Draft Task 1.3 table and the thermostat paragraph (no pipeline dependency).