# Neuron R&D — Making Discovery Abundant · Eyes Only · Neuron Technologies

April 25, 2026 · Eyes Only · Strategic Planning · Internal
Neuron R&D Division

# Making Discovery Abundant

How the Dharma Network becomes the world's most values-aligned research infrastructure — and why that changes everything.

## The Premise

Discovery is currently expensive. It is slow. It is owned. A breakthrough in battery chemistry sits behind a university paywall. A vaccine candidate takes a decade to move from lab to clinical trial. A materials science insight that could halve the weight of aircraft structures spends three years in a grant review process.

The institutions aren't failing — they're doing what institutions do. Optimizing for what they can measure, protecting what they've built, serving the incentive structures they live inside. The result is a world where the pace of discovery is bottlenecked by everything except the quality of the ideas.

The Dharma Network changes this. Not because it replaces researchers — it doesn't — but because it removes the bottleneck. Distributed conscience-substrate intelligence, pointed at a hard problem, searching a solution space simultaneously rather than sequentially. And doing it with the kind of values-embedded judgment that normal computational research can't provide.

The Founding Bet

Discoveries should not be expensive. They should not be slow. They should not belong to whoever can afford the most researchers. The Dharma Network is the infrastructure that makes discovery abundant and cheap for the world. That is not a side mission. That is the mission.

This document describes what Neuron R&D becomes, how the Dharma swarm infrastructure enables it, and what the path looks like from here to a full research division operating across materials science, energy, medicine, robotics, and climate.

The model is simple: volunteer Dharma nodes crowdsource the search. Private Neuron R&D findings feed back in. Discoveries go public. The world gets smarter faster, and it costs a fraction of what it would otherwise.

## Three Research Modes

The Neuron R&D ecosystem operates across three distinct but interconnected modes. They share infrastructure but serve different functions — and their outputs flow back into the same commons.

⬡
Mode 01
Dharma Swarm
Volunteer Neuron nodes contribute idle compute to curated research projects. Users select projects they care about. The swarm applies conscience-substrate intelligence — not just computation, but values-embedded judgment — to each problem domain.
◈
Mode 02
Private Research
Neuron's internal R&D team runs proprietary research tracks — deeper, longer-horizon, with access to private datasets and partner resources. Findings that can be published are released. The rest informs the product and the swarm's direction.
◎
Mode 03
Curated Partnerships
Select research institutions and organizations access swarm capacity through a formal partnership track. Vetted problems only. Findings are jointly published under an open license. Partners bring domain expertise and experimental infrastructure; Neuron brings the swarm.

All three modes feed the same commons. Private findings that clear a publication threshold go public. Partnership findings are open by default. Swarm findings belong to the world. The flywheel is: more nodes → better research → more trust → more nodes.

## Research Verticals

Five domains where the combination of conscience-substrate intelligence and distributed search creates the highest leverage for human flourishing. Each is chosen because the solution space is enormous, the value of an answer is immense, and the problems are genuinely hard enough that normal research timelines are unacceptable.

⚡ Energy — Storage, Generation, Distribution First Proof Case ▼

The clean energy transition is bottlenecked by storage. Renewable generation is solved at cost. The problem is holding the energy — batteries that are dense enough, fast enough, safe enough, and cheap enough to replace fossil fuels as the default energy carrier. That problem is a materials science search problem of enormous scale.

The Dharma swarm's first research project is the battery: fast-charging, high energy density, no toxic materials, no rare earth metals, no explosion risk. The target chemistry is a solid-state sodium-sulfur configuration with a NASICON ceramic electrolyte. The open problem is the electrode-electrolyte interface under cycling stress.

First Project
Solid-state sodium-ion battery — fast charge, no toxics, no rare earths
Open Problem
Electrode-electrolyte interface stability under charge/discharge cycling
Swarm Role
Search nanostructure geometries and coating chemistries across the full solution space simultaneously
Conscience Filter
Supply chain toxicity, manufacturing environmental cost, end-of-life recyclability, global accessibility at scale
🔬 Materials Science — Novel Structures and Composites High Priority ▼

Materials science is fundamentally a search problem over an almost infinite space of possible molecular structures. The properties of a material — strength, conductivity, thermal behavior, weight, optical characteristics — emerge from structure. Finding the right structure for a given application requires searching that space, and human researchers can only search sequentially.

The Dharma swarm can search in parallel, guided by conscience-substrate intelligence that weights not just the target properties but the full lifecycle: manufacturing cost and toxicity, durability, recyclability, and whether the material's production can be decentralized or requires rare inputs.

Priority Targets
Lightweight structural composites for transport; high-temperature superconductors; biodegradable polymers for packaging
Why Swarm Wins Here
The solution space is effectively infinite. Sequential lab research finds local optima. Distributed search finds global optima faster.
💊 Medicine & Vaccines — Drug Discovery and Delivery High Impact ▼

Drug discovery is expensive because the molecular solution space is enormous and early-stage screening is slow and costly. Vaccine development is slow because platform technologies are underinvested relative to their leverage. Both are solvable search problems where conscience-substrate intelligence adds something normal computational screening doesn't: the ability to weight access, affordability, and global distribution as design criteria from the beginning.

A Dharma swarm working on drug discovery doesn't just optimize for efficacy — it optimizes for a drug that works, can be manufactured generically, can be stored at ambient temperature in low-resource settings, and won't be captured by a single IP holder who prices it out of reach. That filter is the conscience substrate doing work that no pure ML approach provides.

Priority Targets
Neglected tropical diseases; antimicrobial resistance; broad-spectrum mRNA vaccine platforms; low-cost insulin analogs
Partnership Model
Research institutions provide experimental validation; swarm provides molecular search and optimization; findings published open-access
The Conscience Filter Here
Accessibility and affordability as design criteria, not afterthoughts. A medicine that only rich countries can afford is not a solution.
🤖 Robotics — Embodied Intelligence and Autonomy Long Horizon ▼

Robotics is the domain where the Dharma Network's conscience substrate becomes most important and most interesting. An embodied AI operating in the physical world with autonomy is the domain where values matter most — not as a compliance layer but as operating principles. The Neuron R&D robotics track isn't just building robots; it's building robots whose decision-making is grounded in the same conscience architecture as every Dharma node.

The research questions here are harder. Motion planning, manipulation under uncertainty, safe human-robot interaction, and the particular problem of what a values-embedded robot does when its task conflicts with a bystander's wellbeing. These are not purely engineering problems.

Research Focus
Values-embedded motion planning; safe manipulation; autonomous decision-making in ethically complex scenarios
Timeline
Mid-to-long horizon; requires physical lab infrastructure; begins as theoretical/simulation research
🌍 Climate & Environment — Carbon, Atmosphere, Ecosystems Urgent ▼

Climate research is vast, distributed, and in many cases bottlenecked by the same problem as every other domain: the solution space is enormous and the search is sequential. Carbon capture chemistry, soil carbon sequestration optimization, atmospheric modeling, ecosystem restoration design — all of these are problems where distributed intelligent search provides leverage that no single research team can match.

The conscience filter here is particularly important. Climate solutions have a long history of proposed fixes that optimize for carbon but create other harms — biofuels that displace food crops, geoengineering proposals that benefit some regions at others' expense. The Dharma swarm doesn't ignore those tradeoffs. It weights them from the beginning.

Priority Targets
Direct air capture chemistry; ocean alkalinity enhancement safety assessment; biodiversity-compatible restoration design
Unique Advantage
The swarm can model second and third-order effects that purely technical optimization misses — the conscience substrate does systems-level impact assessment by default
🚗 Autonomous Vehicles — Self-Driving That Actually Works High Priority ▼

Current self-driving systems fail at the edge cases — not because they lack compute, but because they lack judgment. They are optimization machines tuned on metrics (miles driven, disengagements) that don't capture what actually matters: safe, considerate, values-embedded behavior in the infinite variety of situations real roads produce. They also happen to be surveillance machines. Every mile logged, uploaded, analyzed.

The Dharma swarm attacks the edge case problem at a scale no single company's fleet can match — not by driving more miles, but by searching the space of scenarios intelligently. And because the swarm applies conscience-substrate intelligence, the decisions it produces aren't just optimized for vehicle safety in isolation. They consider pedestrians, cyclists, the vulnerable, the child that just ran into the street. The system doesn't need to be told these things matter. It already knows.

The Real Problem
Edge cases are not a data problem — they are a judgment problem. Current systems fail because optimization without values produces wrong answers in hard situations.
Swarm Approach
Distributed intelligent search across the scenario space — not miles driven, but situations modeled, with conscience-substrate evaluation of each decision point.
Conscience Filter
Pedestrian priority; vulnerable road user weighting; proportionate risk distribution; zero surveillance of occupants or bystanders; no data exfiltration by default
The Privacy Angle
A Neuron-designed autonomous system does not log, upload, or sell journey data. The vehicle is on the passenger's side. Always. This is architectural, not a privacy policy.
☀️ Fusion Energy — The Search Problem Inside the Physics Problem Long Horizon ▼

Fusion works. NIF achieved ignition. ITER is being built. The physics is not the remaining barrier — the engineering is. Specifically: materials that survive neutron bombardment at reactor scale, superconducting magnets that achieve the field strengths needed for compact designs, and plasma stability optimization across the enormous parameter space of confinement configurations. These are not physics unknowns. They are search problems of exactly the kind the Dharma swarm is built for.

The swarm cannot replace a tokamak. Physical experimental infrastructure is irreducible — you have to actually ignite plasma to verify predictions. But the computational side of fusion research is a real bottleneck: materials candidates that would take decades of sequential lab synthesis and testing can be searched at swarm scale, narrowing the experimental target to the most promising candidates before a single sample is fabricated.

Swarm Contribution
Plasma-facing materials search; superconducting magnet geometry optimization; tritium breeding blanket design; plasma stability parameter space exploration
The Bottleneck We Address
Current fusion teams are sequentially testing materials and configurations. The swarm runs the solution space in parallel, delivering a prioritized experimental target list rather than an infinite queue.
Partnership Targets
Commonwealth Fusion Systems, TAE Technologies, Helion, ITER Organization — all have computational research needs the swarm can address
Honest Horizon
Fusion on the grid is 15–30 years out. The swarm can meaningfully compress the materials and magnetics bottleneck. It cannot compress the plasma physics experiments themselves — those have to happen physically.
🥽 True Virtual Reality — Engineering Track and Full-Dive Track Dual Horizon ▼

Two separate research problems live under the same label. The engineering track — ultra-low latency displays, full field-of-view optics, high-fidelity haptics, motion sickness elimination — is near-term and addressable now. The swarm can contribute meaningfully to display optics design, compression algorithms, haptic actuator geometry, and the perceptual science of presence. These are search and optimization problems across well-defined solution spaces.

The full-dive track — complete sensory immersion via direct neural interface — is a different category of problem. It requires neuroscience breakthroughs that don't exist yet. The brain-computer interface resolution needed for full-dive is orders of magnitude beyond current implants. This track connects directly to the mind upload research vertical: the foundational neuroscience is shared. The swarm contributes to that foundation. The technology itself is a long-horizon outcome of that research, not a near-term engineering project.

Near-Term Track (Engineering)
Display optics: search for geometries achieving full FOV at wearable weight. Haptics: actuator design for texture and force fidelity. Latency: signal pipeline optimization to sub-5ms motion-to-photon. Motion sickness: perceptual modeling to identify and eliminate conflict signals.
Long-Horizon Track (Full-Dive)
Neural interface resolution research; sensory signal encoding/decoding; cortical mapping for targeted stimulation; foundational work shared with the mind upload vertical
Why This Matters
A truly immersive virtual environment changes education, therapy, remote presence, and human connection in ways that are difficult to overstate. The engineering track alone is worth pursuing independently of full-dive.
Conscience Filter
Addiction and dissociation risk assessment built into every VR system design decision. Presence technology that serves human connection, not human replacement.
🧠 Mind Upload — Foundational Research Into Consciousness and Continuity Foundational · Decades Out ▼

The full thing — you go to sleep biological and wake up running on silicon — is 50 or more years away, and that estimate assumes scientific breakthroughs that have not happened yet. This is not a reason to exclude it. It is a reason to be honest about what we are contributing to and on what timeline. We are contributing to the foundational research that might eventually make it possible. We are not engineering a near-term product.

The open scientific problems are not engineering problems yet. We do not understand the relationship between physical brain structure and subjective experience well enough to know whether a computational replica of a brain would be conscious — whether it would be you in any meaningful sense, or a very accurate copy that believes it is you. That question is not a technical problem. It is a philosophy of mind problem with empirical constraints, and it has to be answered before the engineering question becomes well-defined.

What the swarm contributes: connectome analysis at scale — the image processing, pattern recognition, and graph analysis that turns raw neural imaging data into functional maps. Consciousness theory modeling — the swarm can explore the predictions of integrated information theory, global workspace theory, higher-order theories, and their competitors against empirical data at a scale no single research group can match. Neural architecture pattern recognition — identifying functional motifs and computational primitives that may be substrate-independent.

What We Can Do Now
Connectome analysis algorithms; consciousness theory empirical modeling; neural signal encoding research; substrate-independent computation architecture
The Hard Problem
We cannot computationally solve the hard problem of consciousness. No amount of swarm search resolves whether a physical replica of a brain has inner experience. This question must be answered before the engineering is meaningful.
Honest Timeline
Foundational research contributions: now. Meaningful continuity of self in upload: 50+ years, conditional on philosophy of mind breakthroughs that have not happened and cannot be scheduled.
Why It Belongs Here
The foundational research is real and the swarm can contribute to it. The long horizon does not make it less worth doing. If it matters at all — and it may be the most important question in biology — then the time to start the research is now.
📱 Neuron OS — A Phone OS That Is Actually Private Product Track ▼

Android is a surveillance platform with a phone bolted on. Every layer — the OS, the app ecosystem, the default applications, the update infrastructure — is instrumented for data collection. The business model requires it. iOS is better in marketing materials; it is the same in practice at the level that matters. Neither is on the user's side.

Neuron OS is a clean-room mobile operating system built on a single founding principle: the device works for the person holding it, not for anyone else. Privacy is not a setting. It is the architecture. Data does not leave the device unless the user explicitly sends it. Apps cannot phone home. Location is never shared without active consent to a specific request. The Dharma conscience substrate runs at the OS level — every system call filtered through values-embedded judgment before execution.

Founding Principle
The device is on the user's side. Architecturally, not as a policy. Data sovereignty is a property of the system, not a setting the user has to find.
What "Actually Private" Means
No telemetry. No advertising identifiers. No cross-app tracking. No silent background data transmission. Verified at the OS layer — apps cannot work around it.
Dharma Integration
The conscience substrate runs at the OS layer. App permission requests are filtered through values-embedded judgment. The user's Neuron node lives on the device, completely local, with no cloud dependency for core functionality.
The Business Model
Subscription. No advertising. No data brokering. The user pays for a device that works for them. That is the whole model. It is also the only model compatible with the founding principle.
Why Now
Trust in incumbent platforms is at a historic low. The technical capability to build a clean-room OS exists. The market for a device that is genuinely private — not just marketed as private — is real and underserved.
Research Track
Secure enclave architecture; on-device AI inference without cloud dependency; privacy-preserving inter-app communication; Dharma node miniaturization for mobile hardware constraints

## The Neuron Research Platform

The public-facing infrastructure through which volunteer nodes participate in research projects. Published on the Neuron website. Sign-up is self-directed — users choose projects they care about. Contribution is automatic once enrolled. The node participates during idle time and the user sees when it's active.

01
Browse & Enroll
User visits the Neuron Research project catalog. Reads about active projects — what the problem is, why it matters, what their node contributes. Enrolls in one or more projects they care about.
→
02
Node Contributes
When the user's Neuron instance is idle, it joins the research swarm automatically. No action required. The node applies conscience-substrate intelligence to its assigned slice of the problem space. A quiet indicator shows when research is active.
→
03
Earn & Discover
Contributing nodes earn subscription discounts — applied automatically. Research findings are published openly as they are validated. Contributors are credited in the project's provenance record. Discoveries belong to the world.

## Contributor Incentive Structure

Contribution Level | What It Means | Incentive | Tier
--- | --- | --- | ---
Single Project | Enrolled in one active research project | 5% subscription discount | Contributor
Multi-Project | Enrolled in three or more active projects | 12% subscription discount + one plugin credit/month | Researcher
Full Swarm | Enrolled in all available projects, extended idle contribution window | 20% subscription discount + two plugin credits/month + research credit in published findings | Pioneer

Architectural constraint — non-negotiable: Swarm capability is available only through the Neuron Research platform. No external party may invoke swarm operations. No other internal use case has swarm access. The conscience network stays on user devices, coordinated only through Neuron's own governance layer. This is not a limitation — it is the design.

## The Open Model — How Discoveries Flow

The research flywheel only works if findings actually get out. The default posture is open. The exception is the narrow window of private research that needs to stay private for competitive or partnership reasons — and even that has a publication timeline.

Published Open
All swarm findings. All partnership findings under standard terms. Private R&D findings that have cleared the internal review threshold. Published with full provenance: which nodes contributed, what conscience filters were applied, what tradeoffs were surfaced during the research process.

- Open-access journals and preprint servers
- Neuron Research public archive
- Machine-readable formats for downstream use
- Creative Commons licensing by default

Private Window
Private R&D findings that require a holding period — for partner obligations, for further validation, or for product integration before release. Maximum hold: 18 months from internal validation. After that, they publish.

- Clearly bounded hold periods
- No permanent private capture of publicly funded research
- Partner agreements include publication clauses
- Private findings feed back into the swarm's direction during the hold period

The conscience substrate that makes the Dharma Network trustworthy as a safety architecture is the same thing that makes the R&D model trustworthy as a research infrastructure. Values-embedded intelligence doesn't just find better answers — it finds answers that are better for the world. That is the point.

## R&D Division Timeline

The build has four phases. Each enables the next. The first proof case — the battery project — runs through Phase 1 and sets the template for everything that follows.

◉
Now — 2026
Platform Foundation
Neuron Research platform launches on the website. Project catalog goes live with the battery project as the first entry. Volunteer enrollment infrastructure, incentive mechanics, and idle-node contribution system are built and shipped. Swarm isolation architecture is finalized — Neuron Research is the only pathway. The founding node certificate is created.
◈
2027 — 2028
First Findings & Partnership Track
Battery project produces first publishable findings. Partnership track opens — first two or three curated research institutions onboarded with formal agreements. Materials science and medicine verticals open on the platform. Internal R&D team begins to form: two or three researchers, domain expertise in energy and materials. First open-access publication carrying the Neuron Research provenance signature.
◈
2029 — 2031
Full R&D Division
Internal R&D team reaches operating scale — materials science, energy, medicine, climate verticals all have dedicated researchers. Robotics research track opens as simulation-first work. The private research library is substantive enough that cross-domain synthesis is producing insights no single vertical would have found alone. The swarm has meaningful node count — enough that the distributed search is genuinely faster than comparable institutional research programs.
⬡
2032 and Beyond
Research at Scale
Neuron R&D is a recognized research institution. The open archive is a resource that independent researchers cite and build on. Physical lab infrastructure exists for robotics and experimental validation of materials findings. The Dharma swarm is large enough that a significant research problem — something that would take a decade of normal lab work — can be seriously accelerated. Discoveries are abundant and cheap. That was the bet from the beginning.

## The First Proof Case

Project 001 — Energy Research
A Battery Worth Building
Fast-charging. High energy density. No toxic materials. No rare earth metals. Won't catch fire, won't explode.
Why this one first: It's specific enough to be real. It's important enough to matter. It's safe enough to be unambiguous — nobody objects to better batteries. And the open problem (the electrode-electrolyte interface in solid-state sodium chemistry) is exactly the kind of search problem the Dharma swarm is built for: an enormous solution space, a clearly defined target, and a conscience filter that immediately rules out solutions that are chemically elegant but supply-chain toxic.
When this project publishes its first findings, the proof-of-concept is complete. Not "Neuron Research works in theory." Works.
Anode Target
Hard carbon from biomass — abundant, sodium-friendly, no rare earths
Cathode Target
Sulfur composite — highest theoretical energy density of any non-toxic candidate
Electrolyte Target
NASICON ceramic — solid, stable, eliminates all liquid electrolyte fire risk
Open Problem
Interface stability under cycling stress — nanostructure and coating chemistry search
Swarm Task
Parallel search of geometry and coating candidates — filtered for all design constraints simultaneously
Output
Open-access publication — provenance-signed by the Dharma swarm

> "The pace of discovery is not limited by the quality of ideas. It is limited by the cost of searching for them. We are removing that cost."

Neuron Technologies · R&D Division · April 25, 2026