nerve-wml 1.5.1

Substrate-agnostic nerve protocol for inter-WML communication

nerve-wml

Substrate-agnostic nerve protocol for inter-module communication in hybrid neural systems.

Citation : each release is archived on Zenodo (concept DOI 10.5281/zenodo.19656342 resolves to the latest version) and linked to the parent programme's OSF pre-registration (10.17605/OSF.IO/Q6JYN).

Research engine that validates a discrete-code communication layer between heterogeneous neural modules (World Model Languages, or WMLs). Modules exchange neuroletters over a sparse learned topology, multiplexed on gamma/theta rhythms, and converted between local codebooks by per-edge transducers. The paper draft is at papers/paper1/main.tex; the full spec is at docs/superpowers/specs/2026-04-18-nerve-wml-design.md.

Status — v1.2.3 (2026-04-20)

The project is now empirically defensible across three experimental axes: real data, architecture scale, and temporal streaming. Two claims are quantified:

Claim A — Substrate-agnostic polymorphism (task competence converges). Three structurally distinct substrates (stateless MLP, spiking LIF with surrogate-gradient, attention-based Transformer) reach comparable accuracies via the shared Nerve Protocol.

Claim B — Substrate-agnostic information transmission (codes align). Independent substrates share 91–96 % of their emitted code information; a frozen LIF can recover a trained MLP's task competence via a learned linear transducer.

Headline measurements

Axis	Finding	Reference
Pool scaling law (MLP ↔ LIF, HardFlow)	$N=2 \to 10.71%$, $N=16 \to 6.71%$, $N=32 \to 2.39%$, $N=64 \to 2.73%$ plateau. 5 % contract holds distributionally at $N \geq 32$.	figures/w2_hard_scaling.pdf
Triple-substrate pool (MLP + LIF + TRF)	$N=15 \to 8.16%$, $N=30 \to 5.86%$, $N=60 \to 4.33%$	v1.1.4
Mutual information (codes MLP ↔ LIF)	$\mathrm{MI}/H = 0.91$ at $N=1$ (5 seeds), 0.96 at $N=16$ pool (192 cross-pairs)	figures/info_transmission.pdf
Round-trip fidelity (MLP → LIF → MLP)	0.99 mean (3 seeds)	v0.8
Cross-substrate merge (LIF fed by MLP codes only)	0.97 mean (3 seeds)	v0.8
MNIST real data	MLP 0.942, LIF 0.941, gap 1.03 %, MI/H 0.882	figures/mnist_scaling.pdf
MoonsTask (2nd distribution)	MI/H = 0.74 (3 seeds)	v1.1.4
Architecture scale ($d_\text{hidden}=128$)	Gap AMPLIFIES to 26 % on XOR (arch vs pool scale are orthogonal); Claim B survives	figures/bigger_arch_scaling.pdf
Temporal streaming (16-token sequence)	MI/H = 0.72 at trained step, 0.71 at filler step — structural alignment	figures/temporal_info_tx.pdf
Direction stability (LIF ≥ MLP on hard task)	15/15 pairwise seeds + 5/5 triple-substrate	—

LIF's spike dynamics give it a substrate-intrinsic $\sim 2$–$3%$ expressivity edge on XOR-style boundaries (plateau floor). Pool averaging compresses this, architecture width amplifies it.

Seven concrete findings

1. The original 12.1 % gap was a decoder asymmetry bug, not a substrate limit. LIF had a fixed cosine decoder, MLP had a learned head; symmetrizing flipped the sign (LIF now leads).
2. Single-seed measurements lie. Multi-seed revealed the N=16 median is 6.7 %, not the lucky 1.68 %.
3. Scaling law is real and monotonic. Four-point decay $10.7% \to 6.7% \to 2.4% \to 2.7%$ plateau.
4. Claim B is empirical, not architectural. MI 0.91–0.96, round-trip 0.99, cross-merge 0.97.
5. Substrate-direction is stable in 15/15 seeds. LIF's spike edge is a real property, not noise.
6. Architecture scale and pool scale are orthogonal. Pool compresses the gap; arch width amplifies it.
7. Code alignment is structural, not task-gated. MI at filler timesteps $\approx$ MI at trained timesteps (0.71 vs 0.72).

Methodological findings (v1.2.1–v1.2.3)

• MI/H vs CKA on the same argmax codes (v1.2.1). Mean 0.953 (MI/H) vs 0.910 (CKA argmax one-hot) over 3 seeds. The 4.3 pp gap tracks soft many-to-one code mappings that kernel-alignment metrics miss. MI/H is not CKA renamed — it is the discrete-protocol cousin with measurably different semantics. See scripts/measure_cka_vs_mi.py and docs/positioning.md.
• Related Work verified (v1.2.2). Paper §Related Work cites Kornblith 2019 CKA, Morcos 2018 PWCCA, Moschella 2022 relative representations (ICLR 2023), Saxe 2024 universality, and Hinton 2015 KD — all verified via WebFetch, provenance table in docs/positioning.md.
• KD match-compute ablation honest verdict (v1.2.3). At matched compute on HardFlowProxyTask (3 seeds), cross-merge (0.508) ≈ KD-through-transducer (0.520) within noise. Vanilla Hinton KD (0.534) is best because the student can re-train its core. Cross-merge's contribution is methodological, not performance-based: it isolates protocol channel capacity from student learning capacity by freezing both substrates and supervising with ground-truth labels only. See scripts/measure_kd_ablation.py.

What the paper genuinely claims vs not

Three findings probably novel: (1) the four-point scaling law with plateau at $\sim 2\text{–}3%$ substrate-intrinsic floor, (2) reproducible $\sim 2\text{–}3%$ LIF spike-expressivity edge over matched-capacity MLP on XOR-on-noise (15/15 seeds), (3) orthogonality of pool-scale (compresses gap) and architecture-scale (amplifies gap).

The paper explicitly does not claim: a new learning algorithm, superiority over knowledge distillation on task accuracy, or universal representations — that debate is addressed by Saxe 2024 and the Nature MI 2025 editorial (s42256-025-01139-y) cited in docs/positioning.md.

Status — 11 gates

Tag	What it proves
gate-p-passed	Track-P protocol simulator correct on toy signals
gate-w-passed	MlpWML and LifWML interoperate with < 5 % gap through the same nerve (N=4)
gate-m-passed	Merge fine-tunes only transducers; retains ≥ 95 % of mock-baseline accuracy
gate-m2-passed	Four scientific shortcuts from §13.1 resolved with honest measurements
gate-scale-passed	Polymorphie + continual learning hold at N=16 pools; router stays connected to N=32
gate-interp-passed	Per-WML code → concept semantics table rendered as HTML
gate-neuro-passed	LifWML → INT8 artefact → pure-numpy mock runner (Loihi / Akida stubs documented)
gate-dream-passed	ε-trace consolidation bridge to dream-of-kiki (schema v0; partial — awaits kiki_oniric v0.5+)
gate-adaptive-passed	Per-WML alphabet shrinks/grows via active_mask + transducer resize
gate-llm-advisor-passed	Env-gated, never-raising NerveWmlAdvisor for micro-kiki, < 50 ms warm latency

Paper drafts: paper-v0.2-draft … paper-v0.9-draft track the iterations that produced the v1.2 claims above. Release tags v1.0.0, v1.1.0 … v1.1.4, v1.2.0 archive the code snapshots; see CHANGELOG.md for per-version findings.

Install

uv sync --all-extras

Python 3.12+, macOS arm64 (MLX-friendly) or Linux x86_64. No vendor SDK deps are pulled by default (Loihi, Akida, dream-of-kiki, sentence-transformers are all optional integrations).

Run the suite

uv run pytest -m "not slow"    # 220+ tests under 80 s on commodity M-series
uv run pytest                  # full suite incl. paper figure rendering
uv run pytest --cov=nerve_core --cov=track_p --cov=track_w --cov=bridge --cov=harness --cov=interpret --cov=neuromorphic

Reproduce the gate numbers

uv run python scripts/track_p_pilot.py       # Gate P (+ Task 6 ablation)
uv run python scripts/track_w_pilot.py       # Gate W
uv run python scripts/track_w_pilot.py scale # Gate Scale (N=16, N=32)
uv run python scripts/merge_pilot.py         # Gate M
uv run python scripts/interpret_pilot.py     # Gate Interp (emits reports/interp/*.html)
uv run python scripts/adaptive_pilot.py      # Gate Adaptive

Reproduce the v1.1 / v1.2 findings

# v1.1 scaling law + information transmission + triple substrate
uv run python scripts/render_scaling_figure.py      # 4-point pool scaling (N=2..64)
uv run python scripts/render_info_tx_figure.py      # MI + round-trip + cross-merge
uv run python scripts/measure_info_transmission.py  # full info-tx battery

# v1.2 real data + bigger arch + temporal
uv sync --extra mnist                               # pull torchvision
uv run python scripts/render_mnist_figure.py        # MNIST Claims A + B
uv run python scripts/render_bigger_arch_figure.py  # d=128 gap amplification
uv run python scripts/render_temporal_figure.py     # streaming MI per timestep

Build the paper

uv run python scripts/render_paper_figures.py   # regenerate figures from frozen golden NPZs
cd papers/paper1 && tectonic main.tex           # or pdflatex, bibtex, pdflatex, pdflatex

Integrations (env-gated, default off)

• Dream consolidation: DREAM_CONSOLIDATION_ENABLED=1 + install dream-of-kiki locally → bridge.dream_bridge.DreamBridge.
• LLM advisor (micro-kiki): NERVE_WML_ENABLED=1 + NERVE_WML_CHECKPOINT_PATH=/path/to/checkpoint → bridge.kiki_nerve_advisor.NerveWmlAdvisor. Wiring recipe: docs/integration/micro-kiki-wiring.md.
• Neuromorphic hardware: install lava-nc or akida → wire in neuromorphic.loihi_stub / neuromorphic.akida_stub. Schema v0: docs/neuromorphic/deployment-guide.md.

Cited in

• dreamOfkiki — Paper 1 v0.2 (2026-04-19), §7.4 cross-substrate portability — github.com/hypneum-lab/dream-of-kiki. The Gate W and Gate M measurements reported here (MlpWML / LifWML polymorphism on FlowProxyTask and HardFlowProxyTask) provide the empirical corroboration cited in Paper 1 as independent evidence of the substrate-agnosticism principle (DR-3 Conformance Criterion). OSF pre-registration: 10.17605/OSF.IO/Q6JYN.

Program context

This repository is part of hypneum-lab, which develops executable formal frameworks for cognitive AI. The programmatic parent is dreamOfkiki (paper 1 formal framework, paper 2 empirical); nerve-wml is the reference implementation for the substrate-agnostic communication principle.

Sibling repositories:

• dream-of-kiki — formal framework (axioms DR-0..DR-4, Conformance Criterion, Paper 1)
• kiki-flow-research — Wasserstein-gradient-flow engine (upstream)
• micro-kiki — 35 domain-expert MoE-LoRA deployable instance (advisor consumer)
• nerve-wml (this repo) — substrate-agnostic nerve protocol + cross-substrate polymorphism

Repository layout

nerve_core/        Neuroletter, Nerve + WML Protocols, invariants (N-1..N-5, W-1..W-4)
track_p/           Track-P — SimNerve, VQCodebook, Transducer, SparseRouter, AdaptiveCodebook
track_w/           Track-W — MockNerve, MlpWML, LifWML, toy tasks, training loop, pool factory
bridge/            Merge, dream, LLM advisor — SimNerveAdapter, MergeTrainer, DreamBridge, NerveWmlAdvisor
harness/           R1 reproducibility — run_registry
interpret/         Gate Interp — code_semantics, clustering, HTML renderer
neuromorphic/      Gate Neuro — spike_encoder, INT8 export, mock_runner, vendor stubs
scripts/           All gate pilots + figure renderers + freeze_golden
tests/             Unit + integration + golden NPZ regressions
docs/              specs/, integration/, neuromorphic/, dream/, interpret/
papers/paper1/     LaTeX source + bib + Makefile (figures regenerated deterministically)

License

MIT (code) + CC-BY-4.0 (docs).