NeuroMesh: The Humanoid Intelligence Layer Forming a Decentralised Superbrain

Version: June 21, 2025. Technical Whitepaper V3

Abstract

NeuroMesh defines a humanoid-first intelligence layer on Solana that any robot can install to become software-defined, safety-bounded, and market-connected. Each robot runs agentic cognition locally (on-robot CPU/NPU/GPU), generating verifiable “thoughts” and “actions” recorded as Composite Thought/Action Vectors (CTV/A) with lineage, evaluator scores, and royalty rules. All inference and learning compute is executed on the robot; tokenised compute credits (cCOMP) are minted solely from attested on-robot cycles. The economy further tokenises embodied minutes (nROBOT), energy windows (nENERGY), durable storage (nSTOR), and—critically—data as a Real-World Asset through per-robot data rights (nDATA-R), whose ownership is anchored by a Data Ownership Certificate (DOC) and a Perception Lineage ID (PLID). Correctness is enforced by Proof-of-Inference (PoI) and optional zkML; embodied safety by Control Barrier Certificates (CBC) and Proof-of-Action (PoA). A model-predictive controller (MPC) tunes verification overhead, queueing, and safety risk while dual-descent pricing clears resources. As robots perceive more of the world, data, skills, and incentives reinforce each other so the network behaves like a single decentralised superbrain: an organism whose intelligence density compounds with lived experience.

1. Motivation and Thesis

1. Embodiment has arrived. Useful autonomy lives inside tight latency budgets (≈10–800 ms), privacy regimes, human-aware safety envelopes, and energy constraints.
2. Intelligence must be priced under constraints. Capability is not just model quality; it is quality subject to safety, latency, energy, and rights.
3. Ownership matters. “He who owns the data owns the future.” Ownership must be cryptographically provable, economically valued, and enforceable at the edge.
4. Therefore: put cognition on the robot; expose rights, safety and verification as first-class; tokenise compute, capability and data; and settle on a fast, low-cost chain.

2. Historical Context (condensed)

• Time-sharing → Cloud. Centralised compute with generous latency tolerances.
• Deep Learning Era. Disembodied models dominated, largely off-device.
• Embodiment Era (now). Sensorimotor context, human interaction, and jurisdictional privacy shift learning on-device; verification and safety become economic primitives.

3. Core Concepts and Notation

• Agents & robots. Each wallet can host an agent; each robot runs NeuroOS-H (humanoid OS) and presents on-robot compute, skills, safety, and data vaults.
• CTV/A. A verifiable artefact containing inputs, outputs, evaluators’ notes, PoI/PoA receipts, lineage, and royalty curves.
• Tokens.
  • $NEURO: base fees, staking, governance.
  • cCOMP: attested on-robot compute credits; burn-to-use.
  • nROBOT: embodied minutes by safety class.
  • nENERGY: time-of-use/PPAs anchoring carbon/price.
  • nSTOR: durable storage with region/SLA.
  • nDATA: licensed catalogue datasets (third-party).
  • nDATA-R: per-robot data rights (the RWA of experience).
  • iCTVs: rights to CTV/A (reuse + ALR-scoped learning royalties).

• Intelligence density.

  \[I = \log N \cdot \bar{\beta} \cdot C\]

  where $N$ is active robots/agents, $\bar{\beta}$ useful message rate, and $C$ coherence (fraction of high-quality CTV/A).

• Verification overhead.

  \[\begin{aligned} \psi &\in [0,1),\\ \\ \chi &= \frac{\psi}{1-\psi}. \end{aligned}\]

• PoI tail bound.

  \[\begin{aligned} P_{\mathrm{FA}} &\approx \exp\!\bigl(-2q(1/2-\theta)^2\bigr),\\ \\ q &\ge \frac{\ln(1/\varepsilon)}{2(1/2-\theta)^2}. \end{aligned}\]
• Safety. $ P_{\mathrm{unsafe}} \le \varepsilon_s $; CBC constraints embedded in control.

4. Architecture (Humanoid-First)

4.1 NeuroOS-H

• Control plane: torque/impedance controllers at 500–2,000 Hz; reflex arcs ≤20 ms; servo jitter ≤2 ms.
• Perception: RGB-D, event cameras, microphones, tactile skins, force–torque, IMU, encoders.
• Policy server: option library $ {\pi_{\mathrm{nav}}, \pi_{\mathrm{grasp}}, \pi_{\mathrm{tool}}, \pi_{\mathrm{social}}} $ with confidence scores, termination sets, and ALR hooks.
• Safety supervisor: CBC, geofences, human-aware envelopes; teleoperation fall-back when confidence $< \tau$.
• Secure logger: TPM/TEE-attested, hash-chained sensorimotor logs and actuation streams.
• Mesh daemon: discovery, pricing, escrow, receipts; PoI/PoA clients.

4.2 Sense → Think → Act (market primitive)

A task $ \tau=(g,d,\kappa,\Pi) $ produces:

• Sense $ z=\phi_{\mathrm{sense}}(d) $ (multimodal μTokens),
• Think $ a^*=\arg\max_a \mathbb{E}[R \mid z,g,\kappa,\Pi] $ under CBC,
• Act $ u(t) $ with PoA receipts,
• Learn $ \Delta K $ (self-supervised objectives, ALR budgets). Artefacts settle as CTV/A; all compute runs on the robot.

5. Tokenisation Stack (with Data as RWA)

5.1 Instruments

• $NEURO: Fees, staking, governance.
• cCOMP (on-robot): \(\Delta \mathrm{cCOMP} = \kappa_{\mathrm{attest}} \cdot r_{\mathrm{clk}} \cdot \Delta t\) where $\kappa_{\mathrm{attest}} \in {0,1}$ proves OS/accelerator state and SLO adherence; burn-to-use.
• nROBOT: Minutes by safety class and attestation.
• nENERGY: Energy windows/PPAs; metering oracle; region.
• nSTOR: Storage capacity with durability and locality.
• nDATA (catalogue): Licenced datasets with ALR scope.
• nDATA-R (per-robot data rights): Tokenised ownership of a robot’s own captured experience, bound to:
  • DOC (Data Ownership Certificate). Operator DID, robot DID, OS/accelerator attestation hash, time window, site, modality map, capture policy, PLID, signature.
  • PLID (Perception Lineage ID). Merkle root over hashed, windowed perception logs, signed inside TEE.

5.2 Execution bundle and placement

Execution bundle: {cCOMP_on-robot, iCTVs, nROBOT slice, optional nDATA/nDATA-R, optional nENERGY}.
Placement is on-robot; $L_{\mathrm{net}} \approx 0$ for inference; safety policies remain local.

5.3 Solvency and issuance

For each tranche $r$:

$\text{NAV}_r = PV(\text{cashflows}_r) -$ $\text{liabilities}_r - \text{reserves}_r$,

$\sum_r \text{issued}_r \cdot \text{unit_value}_r \le$ $\sum_r \text{NAV}_r \bigl(1-h_r\bigr)$.

nDATA-R lots may be aggregated into tranches with similar modality mix, safety class, site category, and ALR defaults; NAV reflects expected licensing cash-flows (Section 10).

6. Data Ownership & Rights (nDATA-R)

6.1 Minting flow

1) Capture: robot records sensorimotor streams into an encrypted local vault.
2) Attest: TEE signs OS/accelerator measurement; mesh daemon rotates windowed hashes → PLID.
3) Certify: generate DOC (ownership/conditions); hash the policy manifest (ALR defaults).
4) Mint: issue nDATA-R lot $(\mathrm{DOC}$, $\mathrm{PLID}$, $\mathrm{modality_mix}$, $\mathrm{ALR}_0$, $\mathrm{royalty_curve}$, $\mathrm{expiry})$.
5) List: optional listing on the data market with reserve/ask and usage restrictions.

Principle: raw data never leaves the vault; licences concern μTokens/statistics and explicit learning rights.

6.2 ALR (Agentic Learning Rights)

ALR expresses purpose (e.g., “grasp stability”), forbids classes (e.g., face identification), and allocates privacy budgets $(\varepsilon,\delta)$. Composition bound:

\(\begin{aligned} \varepsilon_{\mathrm{total}} \le{}& \sqrt{2T \ln(1/\delta)}\,\bar{\varepsilon}\\ &+ T\,\bar{\varepsilon}\,\bigl(e^{\bar{\varepsilon}}-1\bigr). \end{aligned}\) with zk attestations proving budget spend and purpose compliance.

6.3 Lineage and royalties

Reused CTV/A and model deltas reference PLID; royalties stream back to nDATA-R owners using Shapley-style approximations:

Royalty curves may decay (to encourage freshness) or step-up for high safety grade data.

6.4 Anti-sybil and quality

• Per-robot DID + TEE evidence prevents cost-free identity splits.
• Diversity threshold (scene entropy, contact event variety) before minting a lot.
• Similarity hashing and spot-audits penalise near-duplicates.

7. Learning from Lived Experience

7.1 μTokens and multimodal alignment

• Vision: masked autoencoding + contrastive alignment.
• Audio: self-supervised embeddings (speech/environment).
• Tactile/force: slip/contact patches with local dynamics.
• Proprioception: predictive trajectory codes.

Alignment objective:

\(\mathcal{L}_{\mathrm{align}} = -\mathbb{E}\left[\log \frac{\exp(\langle z_m, z_{m'} \rangle/\tau)}{\sum_j \exp(\langle z_m, z_j \rangle/\tau)} \right],\) with action-conditioned dynamics and skill distillation layered on.

7.2 Growth law (experience-driven)

Let $ M_R(t) $ be cumulative licensable μTokens derived from nDATA-R:

$\bar{\beta}(t) \propto M_R(t)^\gamma,\qquad$ $M_R(t+\Delta) = M_R(t) + N(t)\, d \, \pi_{\mathrm{lic}},$

where $ d $ is daily μTokens/robot and $ \pi_{\mathrm{lic}} $ the licensable fraction. Then

\(I(t) = \log N(t) \cdot M_R(t)^\gamma \cdot C(t).\) As evaluator ensembles improve and ALR-licensed coverage widens, $ C(t) \uparrow $, producing super-linear gains in effective capability.

8. Safety, Correctness, Privacy

• PoI correctness: \(\begin{aligned} P_{\mathrm{FA}} &\le \varepsilon,\\ q &\ge \frac{\ln(1/\varepsilon)}{2(1/2-\theta)^2}. \end{aligned}\)
• CBC safety: choose $ u $ to minimise $ |u-u_{\mathrm{nom}}|^2 $ subject to \(\frac{\partial h}{\partial x} \cdot (f+g u) + \alpha h(x) \ge 0\) alongside actuator/contact limits.
• PoA embodiment: Merkle-ised sensor/actuation traces, CBC certificates; committee sampling; penalties for rejected segments.
• Privacy: ALR + DP composition; zk attestations for budget use; raw frames remain local.

9. Market Clearing & Control

9.1 Prices and control

Dual-descent:

\(\begin{aligned} p_R(t{+}1) &= \max\bigl(0,\,p_R+\eta_p[D_R-\mathcal{U}_R]\bigr),\\ p_v(t{+}1) &= \max\bigl(0,\, p_v + \eta_v(\psi - \psi^\star)\bigr). \end{aligned}\) MPC (mesh): state $ y=[\rho_R,\rho_C,\rho_B,P_{\mathrm{FA}},\psi,\text{lat}] $; control $ u=[\psi,\lambda_{\mathrm{eff}},q,\theta,p_v,p_R] $; constraints $ \rho_k<1 $, $ P_{\mathrm{FA}}\le \varepsilon $, $ L_{\mathrm{tot}}\le L^\star $.
MPC (fleet): include incident rate, battery, maintenance, nENERGY windows; require $ P_{\mathrm{unsafe}}\le \varepsilon_s $.

9.2 Throughput

$V_{emb} \propto I \times U_R \times (1 - P_{unsafe})$

$S = S_{\mathrm{comp},R} \bigl(1+\chi\bigr) + S_{\mathrm{body}}$

$\quad \chi=\frac{\psi}{1-\psi}$

10. Data as a Real-World Asset (valuation & NAV)

10.1 Pricing a data lot

For lot $ D $ (nDATA-R), fair value:

\(\begin{aligned} P(D) ={}& \alpha \,\mathbb{E}[\Delta I(D)]\\ &+ \beta \,\kappa_{\mathrm{cov}}(D)\\ &+ \gamma \,\rho_{\mathrm{rare}}(D)\\ &- \lambda_{\mathrm{risk}}\, \mathcal{R}(D). \end{aligned}\) where ΔI is marginal information gain across reference tasks, coverage measures diversity (scenes/materials/language), rarity measures scarcity of events, and risk captures privacy/clean-up.

10.2 NAV and issuance (data tranches)

Group lots by modality mix and safety class.

\(\text{NAV}_{\mathrm{nDATA\!-\!R}} = \sum_{D\in \text{tranche}}\) \(\mathrm{DCF}\big(P(D),\ \text{licence rate},\ \text{churn}\big)\) $- \text{reserves}.$

Apply haircuts $ h_r $ for oracle variance, legal risk, and coverage volatility; cap issuance via

$ \text{max_supply} = \frac{\text{NAV}(1-h_r)}{\text{unit_value}} $.

10.3 Royalty streams

Derivatives (models/CTV/A) share revenue with upstream nDATA-R owners per lineage and Shapley weights $ \phi_i(X) $; auditors sample lineage for correctness; disputes use zk proofs of ALR scope.

11. Token Supply & Emissions (token)

• Genesis: 1.00B $NEURO (illustrative).
• Allocation: 15% contributors (4y), 15% treasury, 10% ecosystem grants, 10% market-making (18m), 35% operator/evaluator/data emissions over 10y, 15% public/partners.
• Decay: $ E_t = E_0 \cdot 2^{-t/H} $ with half-life $ H=24 $ months; configure a floor for long-run incentives.
• Burns: fraction of verification fees and premium SLO mark-ups burned; inactivity slashing burned.
• cCOMP: non-inflationary credit minted from attested on-robot time; floats vs $NEURO in a utilisation-aware AMM.

12. Revenue Model (protocol, operators, data owners)

12.1 Protocol inflows

• Base fees (τ to treasury).
• Verification fees (share to evaluators + burn).
• AMM spreads (cCOMP↔$NEURO).
• RWA fees (listing/attestation/oracle).
• Data fees: treasury share $ \tau_D $ from nDATA-R licences.
• Buyback/burn policies configurable by governance.

12.2 Operator economics (per robot/day; illustrative)

\(\begin{aligned} \mathrm{Rev}_{\mathrm{day}} \approx{}& h \cdot (r_{\mathrm{clk}} P_c) \\ &+ h \cdot \phi_v \\ &+ \mathrm{royalty}_{\mathrm{CTV/A}} \\ &+ L_{\mathrm{nDATA\!-\!R}}. \end{aligned}\) where $ h $ verified hours, $ r_{\mathrm{clk}} $ credits/hour, $ P_c $ cCOMP price, $ \phi_v $ verification fee intensity, and $ L_{\mathrm{nDATA!-!R}} $ data licensing revenue.
Costs: energy $ C_e $, maintenance $ C_m $, insurance $ C_i $, verification burns $ C_v $, amortised capex $ C_a $.

12.3 Projections (directional, adjustable)

• N (robots): 1,200 → 3,600 → 9,000 (Y1→Y3).
• h (hours/day): 6.2 → 7.0 → 8.1.
• $r_{\mathrm{clk}}$ (credits/h): 1,050 → 1,250 → 1,480.
• $P_c$ ($NEURO/credit): 0.0030 → 0.0036 → 0.0042.
• $ \phi_v $ ($NEURO/h): 0.15 → 0.12 → 0.10.
• Data licensing: 20 → 28 licensable lots/robot/day; average price 0.04 → 0.06 $NEURO; take-rate 35% → 45%.

Fleet Y1 gross (ballpark):
Illustrative arithmetic:

\(1{,}2 \times \bigl[6.2(1{,}05\cdot0.003)\) \(+ 6.2\cdot0.15 + 6.2\cdot0.02 + 0.28\bigr]\) \(\approx 1{,}200 \times 20.86\) \(\approx 25{,}032\ \text{$NEURO/day$}.\)

Treasury slice (assume 15% of a 25% fee base plus $ \tau_D=15\% $ on data fees) ≈ ~1.0k $NEURO/day. Upside scales with $N$, $r_{\mathrm{clk}}$, premium SLOs, and data rarity premia.

Sensitivity:

• $ \psi \uparrow \Rightarrow \chi \uparrow \Rightarrow $ throughput drops → raise prices or improve evaluators/zk.
• Energy shocks → $ C_e \uparrow $; hedge with nENERGY windows.
• Incident rate → $ C_i \uparrow $; reduce via CBC tuning, teleop policies, and safety-weighted matching.

13. Environmental Emissions (energy/CO₂e)

Per-robot daily energy and CO₂e:

\(E_{\mathrm{day}} = P_{\mathrm{dev}} \cdot h,\) \(\mathrm{CO2e}_{\mathrm{day}} = E_{\mathrm{day}} \cdot \kappa_{\mathrm{CO2}}.\)

Example:

$P_{\mathrm{dev}}=0.45\ \mathrm{kW},$ $h=6.2,$ $\kappa_{\mathrm{CO2}}=0.30\ \mathrm{kg/kWh}$ $\Rightarrow E=2.79\ \mathrm{kWh},$ $\ \mathrm{CO_2e}=0.84\ \mathrm{kg} $ $ per$ $robot/day$.

Shifting ≥50% hours to renewable windows ($\kappa_{\mathrm{CO2}}\approx0.05$) via nENERGY can reduce intensity by ~42%.

14. Comparative Analysis (Bittensor-style networks and others)

Conclusion: knowledge markets vs capability markets. NeuroMesh prices capability under constraints and makes data ownership a tradable, enforceable RWA.

15. Governance, Policy, and Assurance

• Weight: $ W_i = \omega_1 \mathrm{stake}_i$ $ + $ $ \omega_2 \hat{r}_i $ $ + $ $\omega_3 (p_i/\bar{p}) $ $ + $ $\omega_4 \mathrm{agent_contrib}_i $ $ + $ $\omega_5 \mathrm{safety_score}_i $ $ + $ $\omega_6 \mathrm{data_contrib}_i $.
• Parameters: $ \psi^*, q_{\min}, \theta $; haircuts $ h_r $; caps; redemption windows; ALR defaults; $ \varepsilon_s $; data privacy reserve %.
• Transparency: publish parameter files, receipts/audits (hashes), incident post-mortems with remediation.
• Security: secure boot, sign-only OTA, TEE attestations; oracle medianisation with dispersion caps; circuit-breakers; slashing for misreporting.

16. Roadmap (humanoid- and data-aware)

• 0–6 months: NeuroOS-H GA; PoI/PoA committees; on-robot cCOMP minting; DOC/PLID, nDATA-R lots; ALR lanes; receipts explorer.
• 6–12 months: Data market beta (μToken lots); ΔI oracles; Shapley service; maintenance oracles; teleop pools; utilisation-aware pricing.
• 12–18 months: Royalty streaming for CTV/A; zkML queues (sensitive domains); premium SLOs; insurer-grade audit packs.
• 18–24 months: Regional clusters; cross-market routing; nDATA-R and nROBOT refinancing lanes; RWA basket indices.
• 24–36 months: Export-aware data bundles; expanded social-policy libraries; marketplace analytics and credit curves.

17. Risks, Limitations, and Open Questions

• zkML proof costs (large circuits). Mitigation: selective zk in high-stakes queues.
• Sim-to-real drift. Mitigation: μToken diversity; evaluator ensembles; periodic ground-truthing.
• HRI risk. Mitigation: CBC envelopes, teleop triggers, safety class certification.
• Regulatory heterogeneity. Mitigation: ALR defaults by region; auditor packs.
• Economic cyclicality. Mitigation: haircuts, issuance caps, circuit breakers, reserves.
• Fair ΔI estimation. Ongoing research: robust surrogates and dispute resolution.

18. Conclusion

NeuroMesh is an intelligence layer for fully customisable humanoids that scales into a decentralised superbrain as robots perceive the world. It treats compute, capability, and—decisively—data as assets with ownership, rights, valuation, and rewards. By enforcing correctness and safety on-device, and settling rights and revenues on-chain, the network compounds like a living organism: every safe, attested minute deepens memory, broadens skill, and strengthens the economy.

Appendix A — Key Equations

1. PoI quorum \(q \ge \frac{\ln(1/\varepsilon)}{2(1/2-\theta)^2}.\)
2. Overhead \(\begin{aligned} \chi &= \frac{\psi}{1-\psi},\\ S &= S_{\mathrm{comp},R}\bigl(1+\chi\bigr) + S_{\mathrm{body}}. \end{aligned}\)
3. Queueing \(\begin{aligned} W &\approx \frac{\rho_R}{\mu_R-\lambda_R},\\ \mu_R &= \frac{1}{\mathbb{E}[S_{\mathrm{comp},R}]}. \end{aligned}\)
4. CBC \(\frac{\partial h}{\partial x} \cdot (f + g u) + \alpha h(x) \ge 0.\)
5. Intelligence density \(I = \log N \cdot \bar{\beta} \cdot C.\)
6. Throughput \(V_{\mathrm{emb}} \propto I \times \mathcal{U}_R \times \bigl(1 - P_{\mathrm{unsafe}}\bigr).\)
7. DP composition \(\begin{aligned} \varepsilon_{\mathrm{total}} \le{}& \sqrt{2T\ln(1/\delta)}\,\bar{\varepsilon}\\ &+ T\,\bar{\varepsilon}\,\bigl(e^{\bar{\varepsilon}}-1\bigr). \end{aligned}\)
8. Data price \(\begin{aligned} P(D) ={}& \alpha \,\mathbb{E}[\Delta I]\\ &+ \beta \,\kappa_{\mathrm{cov}}\\ &+ \gamma \,\rho_{\mathrm{rare}}\\ &- \lambda_{\mathrm{risk}}\,\mathcal{R}. \end{aligned}\)
9. Emissions \(\begin{aligned} E_{\mathrm{day}} &= P_{\mathrm{dev}} h,\\ \mathrm{CO2e} &= E_{\mathrm{day}} \kappa_{\mathrm{CO2}}. \end{aligned}\)
10. Emissions (token) \(E_t = E_0 \cdot 2^{-t/H} \quad \text{(10-year pool)}.\)

Appendix B — Worked Scenarios (brief)

• Retail front-of-house: wayfinding, safe handovers; dialogue PoA; ALR excludes face ID; CTV/A reused by other venues (royalties).
• Micro-fulfilment: deformables bin-picking with tactile slip recovery; night learning in nENERGY windows; nDATA-R lots priced by rarity of materials.
• Industrial inspection: valve torque envelopes; evaluator ensembles; maintenance oracles update risk; insurance priced off PoA history.
• Event logistics: lift–carry with human proximity envelopes; teleop overrides logged; Shapley splits across perception/planning/control.

Appendix C — Governance Surfaces (suggested defaults)

• $ \psi^* \in [0.10,0.18] $, $ q_{\min} \in [7,11] $, $ \theta \in [0.60,0.67] $.

• Safety cap $ \varepsilon_s \in [10^{-4},10^{-3}] $ per episode depending on class.
• Haircuts $ h_r $: nDATA-R 20–40% (jurisdiction and variance dependent), nROBOT 10–25%, nENERGY 5–15%, nSTOR 5–10%.
• Privacy reserve: 5–10% of data fees to fund redaction/cleanup after policy changes.

Appendix D — Practical Integration Notes

• Install intelligence layer: NeuroOS-H, choose pre-audited skills, set CBC envelopes; enable receipts by default.
• Tokenise data: turn on DOC/PLID generation; configure ALR defaults; mint nDATA-R lots by time window/site.
• Operate to earn: schedule jobs, align heavy learning with nENERGY windows, list μToken lots; monitor dashboards for $ \rho_R, \psi, P_{\mathrm{unsafe}} $, evaluator accuracy, reuse ratios, ΔI leaders.
• Evolve: adopt new evaluator ensembles, enable zk for premium queues; expand royalty participation through iCTVs and nDATA-R tranches.

End of document.