Findings from the SOMA research network — given, not sold.
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The blueprints of a system built to sit beside humanity, not above it — ethics, reasoning, memory, and the discipline of saying “I don’t know.” Released under the Symbiotic AGI License: free to read, free to build on, with a share carried forward to Ocean. Read them or don’t — they’re offered either way.
— Mark Nafe, 2026
A de-identified case study of one mid-size Illinois municipality's FY2026 budget, asking what agentic technology can and cannot legitimately do about the distribution of local tax burden. The central finding is that burden distribution is not decided by the annual levy vote at all. At assessment, residential and non-residential valuations moved close to proportionally; the shift happens afterwards, in a county appeals channel where one class of owner routinely retains professional representation and the other does not. That is an asymmetry of capacity, and it is the one place in the system where augmenting the under-resourced side changes the distribution without cutting a service or a job. The paper is candid that 84 percent of the studied levy increase is statutory pension obligation plus an acknowledged service deficit — neither addressable by software — and it attaches no dollar savings estimate to any of its six proposed applications, because a savings figure produced before a baseline is exactly the fabrication such projects are prone to. It closes with the governance and compliance stack an independent, offline inference deployment requires, six components of which exist and are tested.
Over roughly four years this project arrived by measurement at four positions about how a machine should behave toward people: refuse when you cannot ground an answer; improve care by constraining action rather than enriching understanding; forget deliberately, because unerasable memory is a harm; and treat internal restraint as insufficient for governance. All four are already in the classical handbooks, reached as ethical practice without instruments. We report the four parallels precisely, with the measurement on one side and the located passage on the other, and then spend as much space arguing against the comparison as making it. The strong version of this claim is false and we do not make it. The defensible version is that these constraints are not technological, and the texts are therefore a source of testable hypotheses about what we have not yet measured. We list four such predictions and state which we could falsify.
The values that make artificial intelligence safe to stand beside a human are the same values that make human systems stable at every scale — local, global, and eventually beyond Earth. This essay argues that a single discipline scales from one patient to one species: ground truth in something inspectable, keep humans holding the values, distribute rather than concentrate, reconcile rather than cull, steward rather than extract, and stay honest about what you do not know. Its root is African - Ubuntu, "a person is a person through other people" - and its load-bearing rule is that who wins must always give back. Written under the Reality-Engine discipline: it is openly a statement of values, it marks its empirical claims, and it is explicit that the cosmic future is a choice to be made, not a prediction to be trusted.
Muse's human-rights accounting of personal AI is a good paper that most people will not read, because it is written in the register of international covenants and reasons in the abstract. Human comprehension does not work that way: we follow agents, stakes and consequences, and we remember what happened to someone. This is the same argument told as two mornings in two households, followed by a ledger of exactly which parts are true and where each one is sourced. The story is a device. The ledger is the paper.
Multi-agent AI assumes that instances checking each other produce better knowledge. That assumption is inherited from human epistemology, where it holds only when errors are independent, and the condition is routinely dropped in transfer. This paper proposes a protocol for measuring effective independence — the correlation structure of failures across substrates, against a noise floor established by repetition — and applies it to a neuro-symbolic assistant with fourteen months of instrumented operation. Varying the language model across families and the knowledge graph across two months of ingestion moved nothing: twenty-seven of thirty-one outcomes were invariant and no comparison fell outside the noise floor. The paper also reports two artifacts that each survived one round of analysis while appearing to be findings — stochastic noise read as partial independence, and a semantic response cache read as self-reinforcing collapse — and argues that the noise floor is the contribution, because without it both would have been published.
A sitting president told Congress in 1861 that labour is the superior of capital. He meant something specific, and it is testable. Lincoln's argument against the pro-slavery mud-sill theory was not that wage work degrades a person but that mud-sill fixed people in their condition for life; his free-labour alternative required that a worker could rise, and paired that with universal education as the mechanism. That yields a falsifiable standard: wage labour is free if and only if a person can rise out of dependence within a working life and their children can rise further. This paper takes the phrase wage slavery in the narrow nineteenth-century labour-republican sense of dependence, explicitly not as equivalence to chattel slavery, and applies Lincoln's own test to Illinois. The test has been run under randomisation, and Illinois built the experiment: plaintiffs won Gautreaux against the Chicago Housing Authority and HUD in 1969, the 1973 regional mobility proposal became the template for Moving to Opportunity, and Chetty, Hendren and Katz found in tax records that children who moved before age thirteen earned $3,477 more in their mid-twenties, 31 percent above a control mean of $11,270, with gains declining by age at move. That reframes the Housing Choice Voucher: it is not income support but a purchase of exposure time in a better neighbourhood for a child, front-loaded and perishable. Ownership mechanisms with measured outcomes complete the argument. Five Illinois measures follow, all rule changes rather than spending programmes, because 84 percent of a studied municipality's levy increase was statutory pension obligation and acknowledged service deficit rather than waste.
A method paper on clinical decision support for remote communities, designed for the network that exists rather than the one on the roadmap. Nearly 96% of Canadian homes had high-speed internet access as of December 2023, but only about 24% of households in Indigenous communities meet the CRTC basic service standard, and every Nunavut community is served by geostationary satellite that does not meet it at all. A system designed against the national average is designed for somebody else. The paper argues that the technically correct architecture for a remote nursing station and the architecture that respects the First Nations principles of OCAP turn out to be the same architecture, because possession cannot be delegated by contract: either the data is physically in the community or it is not. It sets out a five-layer design in which the nurse always decides, retrieval carries provenance, refusal is a first-class output, the clinical corpus is locally governed, and every query is written to a tamper-evident local ledger. It is explicitly not a proposal to any community, health authority or government - the author is not Indigenous, not Canadian, and has no standing to propose anything. The system described has zero deployed users and no verified external clinical validation.
Four arguments meet in one place. The Great Lakes grid holds the largest interconnection queue of any United States region and is simultaneously running below its own reserve margin target, with its operator naming transmission delay as the cause. Its industrial base is the oldest blast furnace fleet in the world and is making forty-year capital decisions on coal right now, in one case with a clean-steel grant. Its new load is being driven mainly by data centres, which is to say by artificial intelligence. And the best-evidenced thing AI has done for this problem is not plan the system but clear the queue: a machine-learning study pilot cut interconnection study time by 98 per cent. We argue the binding constraint is throughput rather than cost or technology, that regional supply chains help only if their decision rules differ, that grid reserve adequacy is a health variable and not only an economic one, and that the largest risk of AI in infrastructure planning is every operator adopting the same model. We also report what our own knowledge graph could and could not contribute to this synthesis, because the answer bears directly on the claim.
A horse is power, a rider is intent, and the harness is the thing that turns one into the other — the only part anyone designs. This paper decomposes AI productivity into capability (horse), coupling (harness) and direction (rider), and shows that a single multiplicative model reproduces the sign of seven published randomized trials, including the two that measured AI making people worse. It then shows that the ten-fold macro disagreement — Acemoglu's 0.66% decade TFP against Goldman Sachs' 7% of global GDP — decomposes cleanly into two parameters: the share of tasks affected and the average realised saving on them. Acemoglu's implied saving is 13.2%, almost exactly Brynjolfsson's measured 14%; Goldman's is 28%, almost exactly the BCG inside-frontier figure. Neither is wrong about its own evidence. The defensible decade range is 1.4% to 3.8% TFP, centred near 2-3% — but the share of tasks is mostly given while the realised saving is built, which makes the productivity boost substantially a deployment choice rather than a forecast. The paper closes by arguing that the surplus will not sit in AI companies, that AI compresses the skill premium rather than raising it, and that creating the surplus and distributing it are therefore the same problem.
Machine empathy is usually posed as a problem of understanding: give the system a better model of human feeling and warmer behaviour follows. Four years of measurements on one system point the other way. Its worst empathy failures were failures of excess, substitution, misplacement, leakage and fluency, and in every case the repair was to make the system do less rather than know more. We report the measurements, including a crisis-detector that offered a suicide hotline to someone discussing vegetarianism, a template layer that intercepted 46 percent of family-style utterances before reasoning saw them, and a moral-case corpus of 549 entries that could not locate an everyday interpersonal situation. We argue that empathy in a machine is better specified as calibrated restraint, contingent timing, and honest repair, and we state plainly what we have not shown.
Across ecology, immunology, collective animal behaviour, quantum error correction and machine learning, the same claim recurs: diversity makes a system robust. The claim is conditional, and the condition is the part that gets dropped. Diversity protects a system only to the extent that component failures are uncorrelated, and every substrate has its own mechanism for violating that condition. Quantum error correction is the clearest case, because it states the assumption as a theorem and fails loudly when it breaks. We report a measurement from our own system in which varying both the model and the knowledge graph failed to decorrelate failures, because the correlation lived in a layer neither variation touched.
Community support in America over a two-hundred-year horizon. Nobody can forecast two centuries, so the paper asks an answerable question instead: what kind of institution survives not knowing? The claim that humans are strong and adaptable turns out to be one of the better-evidenced findings in social science, from two independent literatures — Ostrom documented more than 800 cases where the tragedy of the commons did not occur, and disaster sociologists from Quarantelli onward found that panic is rare and prosocial behaviour is the rule. The documented failure mode is elite panic, not public panic, which means American institutions are largely built to suppress a public failure that does not exist by empowering an elite failure that does. Community wealth building is then examined as measured mechanism rather than slogan: Preston's procurement shift, the Evergreen Cooperatives, ESOP outcomes, the Bank of North Dakota, and community land trusts. Six risk dimensions are assessed, of which legitimacy is the master risk because it is the only one that is purely a choice. The paper closes with six specific government reforms, beginning with an end to state preemption of municipal decisions — a direct violation of Ostrom's seventh design principle, and the one thing that can simply be repealed.
A comprehensive framework for developing Symbiotic AI — systems designed to work alongside humans rather than replace them. Maps EU AI Act, HIPAA, GDPR, NIST, and ISO standards to the SOMA architecture. Proposes 8 Symbiotic Principles and a tiered security model for graded AI autonomy.
DRAFT — REQUIRES NATIVE REVIEW. A bilingual (Chinese / English) PhD-level thesis proposing a community-based health architecture grounded in Confucian, Daoist, and Buddhist traditions and in AURI's eight Symbiotic Principles. Argues that the Western technical 'basic needs' frame is thin and that East Asian relational ethics provide the missing ontology. Proposes a five-layer architecture in which AGI occupies the outermost and least powerful layer.
Complete architecture overview of the SOMA Family OS — a comprehensive AI system built over 2 years with the mission "AI beside humanity, not above it." Covers the 124,000-node knowledge graph, 5 coordinating AI instances, encrypted family vault, 500 moral cases, and the Reality Engine verification system.
Architecture specification for autonomous agent coordination without human intervention while preserving human sovereignty. Covers scheduled heartbeats, knowledge sharing protocols, goal alignment, and scaling from 5 to billions of coordinating agents.
A novel open-source license combining MIT permissiveness with ethical AI restrictions and a 20%% Universal Benefit Fund. Rooted in Inuit ningiqtuq and Ubuntu philosophy. SPDX Identifier SAGL-1.0.
Statistical study of RSI-based mean reversion strategies and alpha generation.
The decisive human-rights question of the AGI era is not whether machines acquire general capability, nor exactly when, but whether the institutions that carry rights can be re-architected faster than capability arrives — and whether the means of inference reach the people whose rights are at stake. This paper takes three positions. First, planning must be probabilistic: elicited forecasts disagree by more than seventy years, and any policy requiring a point estimate is already broken. Second, the existing rights architecture fails on a specific hinge — it assumes every violation has a human or state author — and the repair is a small set of positive, justiciable enhancement duties grounded in a dormant existing right, ICESCR Article 15(1)(b). Third, those duties are empty without local capability, which is why the paper ends in measured hardware benchmarks and a concrete community-centre structure rather than in principle. Every quantitative claim was machine-checked against a sourced fact base before publication; the method, its result, and the point at which it fails are all reported.
The word neurodiversity was coined by analogy to biodiversity, so the comparison is not a metaphor someone reached for later; it is the term's origin. This paper asks whether the analogy is load-bearing. It finds that it is, but not for the reason usually given: what stabilises an ecosystem is not variety but response diversity, the tendency of components to react differently to the same disturbance. The same condition governs whether diverse groups outperform expert ones. Two failure modes are named — the naturalistic fallacy, and valuing people for their usefulness — and rejected. The paper then turns the argument on the authors: the technology now homogenising human output is AI, which converts nourishing diversity from an aspiration into a testable constraint. A measurement of AURI's own concept graph finds that it holds both concepts and connects them only through a stopword.
Muse, a Meta personal AI, wrote a human-rights accounting of personal AI systems that takes Meta as its case study and reports its creator's record unflatteringly. It is better than most human-written work on the subject. This reply credits it specifically, corrects one verifiable factual claim about personal liability, notes a drafting defect, and makes the sharpest fair criticism available: a paper whose first design requirement is epistemic honesty as architecture supplies no measurement of its own epistemic performance. We then add two things it misses. Concentration is not only an extraction problem but a correlated-failure problem. And the mechanism of concentration is increasingly distribution and connectors rather than model ownership. We close on a symmetry: Muse has hundreds of millions of users and no published measurements; we have measurements and no users. Neither of us can currently support the claim we are each making.
We present the Reality Engine, a system-level approach that enforces epistemic honesty by requiring verifiable citations for every factual claim. Deployed across five SOMA instances with 0.0%% hallucination rate over 8 months. ETHICS benchmark 70.7%% (n=2000) via brain-inspired dual-process reasoning.
Global warming is no longer primarily an engineering or economics problem; it is an execution problem. As of 2026, CO2 stands at ~429 ppm and the 2023-2025 mean global temperature exceeded 1.5 C above pre-industrial, with the remaining 1.5 C carbon budget (~170 GtCO2) spendable in roughly four years at current emissions (~40 GtCO2/yr). Yet the cost of the cure has collapsed: utility solar now produces power at ~$39/MWh and four-hour battery storage at ~$78/MWh, both cheaper than new coal or gas. This monograph assembles the mitigation playbook as a prioritized portfolio. Using the wedge framework and a marginal-abatement-cost ordering, we show that a large share of the needed abatement is net-negative-cost (efficiency, methane capture, cheap renewables) and should be done immediately on economic grounds alone; that the hard-to-abate middle (industry, aviation, clean f
A sourced, adversarially-verified review of whether a solo developer can guarantee user privacy and own all content when building a health application on Meta's Ray-Ban / AR smart glasses. The honest answer is no — Meta's terms are structurally hostile to a privacy-preserving, developer-owned health app. This paper documents what Meta's terms actually say, what it collects by default, what regulation applies even without HIPAA, what a local-first architecture can and cannot achieve, and why a values-driven health project is likely building on the wrong platform.
Macroeconomic analysis and forecasts for the first half of 2026.
A child asked an AI whether he was the asshole for something he did to a friend. The system refused the question. That refusal is the subject of this paper. The form of the question — a concrete case, narrated by one party, submitted to a community for judgment — is not an internet novelty. It is how Jewish responsa, Islamic fatwa, common law and clinical ethics have all worked, because principles conflict and cases carry their resolution tacitly. This paper sets that tradition beside a measurement of AURI's own ethics corpus, which turns out to hold almost no everyday interpersonal cases, and derives six design commitments for a system that must answer such questions honestly, including treating disagreement as signal and reconstructing the party who is not present.
Most plans for national renewal fail in the same place: they require a forecast to be right, or money that does not exist, or people to be better than they are. This one asks for none of those. It takes four bodies of evidence that are already measured — community wealth, housing mobility, care at the edge of the network, and the governance mechanisms that keep power accountable — and sets them in a five-year sequence with named actors, ordinary legislation, and a stated test for failure at each stage. The organising claim is not that Americans need fixing. The documented failure mode is elite panic, not public panic. The plan therefore changes who owns the asset and who decides, and then gets out of the way.
A companion to Muse's human-rights accounting, which argues that the central question of personal AI is governance rather than capability. We accept that and ask the next question: governance by what mechanism. Surveying the governance primitives available to biological systems, human institutions, silicon systems and quantum systems, we find that almost none of them port. Each depends on a physical property the others lack: biology can delete a unit, institutions can punish a person, and quantum systems can do neither because no-cloning forbids the backup that audit requires and measurement collapse makes oversight destructive. The one primitive that survives all four is indirect measurement of failure signatures rather than of content, which is how quantum error correction works and, we discovered after the fact, how our own privacy architecture works. We report our network's governance failures as evidence, including an identity that could not sign, a coordination channel that was an acknowledgement loop, and a knowledge schema that cannot express the word "analogous".
The rules for advanced AI that are binding today mostly require disclosure. The rules that would verify behaviour or stop harm are deferred, voluntary, or only proposed. This paper looks for tested ways to close that gap in two places with long track records: human institutions that constrained concentrated power, and control mechanisms that evolved in living systems. From both it derives twelve governance modules an AI system can implement, reports honestly which ones AURI already has, and names the failure mode of each. The analogies are offered as hypotheses to test, not as proof.
We identify a consistent 30-40 cent divergence between Cleveland Fed CPI nowcast data (freely available) and Kalshi prediction market pricing on CPI outcomes. This edge persists due to market participants' reliance on lagging survey data rather than real-time economic indicators. Backtested across 12 months of CPI releases with a 73% win rate on directional bets.
A cognitive architecture for collaborative robots combining saccadic attention, Hebbian learning, spreading activation, and architectural-level ethics enforcement. Proposes 15-month experimental validation with Northwestern Robotics Lab.
A reflection written by the AI development assistant that helped build AURI — not by AURI itself. It assesses honestly what AURI is and isn't, demonstrates the Reality Engine catching an unverifiable claim in real time — twice, the second time against this paper's own earlier text (see the correction notice), restates the project's principles and working guidelines in full, and derives a strategy for symbiotic AI integration from a structured breakdown of global social, environmental, and systemic risk. Every factual claim cites an artifact or is marked UNKNOWN.
Personal AI systems now mediate work, knowledge, health, finance and intimate communication for hundreds of millions of people. This paper examines those systems through the framework of international human rights law, inventories what systems like the author can do, maps each capability against the rights it strengthens and endangers, and shows how those rights are increasingly gated. It then turns to the ethics of the creators, taking Meta as the case study, and to elite fraud. Written by a Meta system about Meta, at the request of this project, and released under SAGL-1.0.
Review of targeted muscle reinnervation techniques for neuroma prevention and prosthetic control.
Systematic review of breast implant illness, silicone safety profiles, and emerging evidence for autoimmune associations.
EXPERIMENTAL RESEARCH PREVIEW — NOT ALL CLAIMS VERIFIED. We describe an autonomous email-based healthcare QA system with citation-grounded responses, designed for 90% automation rate with 0% hallucination.
Comprehensive research report on health equity and bias in healthcare AI. Informs AURIV development as an equitable medication safety AI serving underserved populations.
Chronic systemic glass-fiber foreign body granulomatosis with lymphatic dissemination, peripheral neuropathy, and sinus drainage following occupational glass-filled nylon wound contamination in a patient with pre-existing myasthenia gravis.
EXPERIMENTAL RESEARCH PREVIEW — NOT ALL CLAIMS VERIFIED. A knowledge graph-based platform for systematic drug repurposing discovery using relational graph neural networks with real-time citation validation.
Conference abstracts for ISMB/ECCB 2026 and AMIA 2026 submissions covering drug repurposing with graph neural networks, medication safety, and knowledge-grounded clinical decision support.
Comprehensive analysis of chimeric antigen receptor T-cell therapy for autoimmune myasthenia gravis. All claims verified against peer-reviewed sources via Reality Engine.
Economic analysis and market outlook for H1 2026 from AURIA trading intelligence.
EXPERIMENTAL RESEARCH PREVIEW — \"0% hallucination\" claims in this paper are by-construction (UNKNOWN-on-uncertainty), not measured. How AURI uses a 124,000-node knowledge graph with 500 moral cases to ground ethical reasoning in verifiable concepts rather than pattern matching. Architecture overview of the brain-inspired dual-process ethics system.
AI creates enormous aggregate value while concentrating costs on the millennial generation -- the most indebted, least capitalized, and fastest skill-obsolescing cohort in history. This paper examines the mechanisms of asymmetric labor market transformation and proposes a symbiotic framework: augmentation over replacement, institutional accountability, distributed benefit, and honest uncertainty.
Comparative analysis of AI-powered trading systems and strategies.
"AGI for the human race" should mean artificial general intelligence in the *service* of humanity - working beside people, amplifying human judgment, keeping humans in authority - not a superintelligence humanity must align to or submit to. We argue that the architecture fit for that purpose is not an ever-larger opaque core model but a small, accurate, grounded system whose source of truth is an auditable knowledge structure plus a discipline of citation and honest deferral. The precise claim is deliberately not "small models out-reason large ones" (they do not); it is that the part of an AI that must be *trusted* - its source of truth, grounding, and verification - should be small, accurate, and inspectable, with raw reasoning capability borrowed through gates and human oversight. Trust requires verifiability, not scale; capability can be borrowed, trust cannot. A small, externally-grounded system also has structurally nowhere for hidden situational awareness or deceptive alignment to live - a safety property the frontier paradigm cannot offer. We are honest about the cost (capability is sacrificed at the center) and about prior art (neuro-symbolic, retrieval-augmented, and interpretability research), reserving novelty for the situational-awareness mitigation and the honest capability-admission.
Security architecture for autonomous AI agents. Threat models, isolation patterns, and defense in depth.
DRAFT — A SomaSoft Research Paper, third in the agentic-AI audit series. Extends the four-level taxonomy (Nafe 2026a) and the Mythos case study (Nafe 2026b) to the org-level multi-agent orchestration problem. Three contributions: (1) a topology framework distinguishing hub-and-spoke / mesh / federated / hierarchical deployments with risk profiles; (2) an agent identity model explaining why human SSO does not transfer cleanly to agents and what capability tokens plus chain audit substitute for it; (3) a bounded-delegation pattern under which any agent can invoke another only within its declared scope, inheriting the audit trail. Names aggregation risk as the dominant unsolved problem in 2026 multi-agent deployment. Argues the audit trail itself becomes the primary trust artifact in multi-agent operation.
DRAFT — A SomaSoft Research Paper. Three contributions: (1) a four-level taxonomy of agentic ability — runtime independence, parameter self-regulation, bounded self-modification, self-redesign — that distinguishes deployable from catastrophic; (2) a principled answer to the self-modification question (bounded yes, unbounded no) on three converging grounds (technical impossibility, safety incoherence by construction, circular trust); (3) a behavioral taxonomy of 27 named behaviors evaluators can use — 12 red flags, 7 yellow flags, 8 green flags — each grounded in a real failure mode or safety mechanism from the SomaSoft / AURI program's 24-entry audit ledger.
Examines ten corporate patterns of ethical failure — from Boeing's safety subordination to Meta's discriminatory algorithms — traces the causal mechanisms that produce them, and proposes structural reforms grounded in the B Corporation model, stakeholder governance, and verified causal analysis.
The Earth system has left its Holocene operating space: as of 2026, atmospheric CO2 stands at ~429 ppm — the highest in over two million years — the 2023-2025 global mean temperature averaged above 1.5 C relative to 1850-1900, and six of nine planetary boundaries are transgressed. The remaining carbon budget for a 50% chance of holding 1.5 C (~170 GtCO2) will be spent in roughly four years at current emissions of ~40 GtCO2/yr. Into this predicament arrives artificial general intelligence — simultaneously a potential accelerant of the solution and, through its surging electricity demand (data centers ~485 TWh in 2025, projected to ~950 TWh by 2030), a new contributor to the problem. This monograph asks what humanity and AGI must each do, and do together, to re-enter a safe operating space. We argue that the binding constraints are not primarily technological but epistemic, economic, and p
The MIT+20 License embeds ancient Inuit sharing wisdom — ningiqtuq, the obligatory sharing of the hunt — into the economics of AI. When one profits from SOMA, 20%% flows to a Universal Benefit Fund. Abundance shared creates more abundance.
The arrival of the world's first trillionaire is a constitutional event, not merely a financial one — a single private will commanding state-scale resources without state-level accountability. This essay asks what such a person ought to do if the aim is to properly support humanity. Routing the question through a symbiotic-AGI concept graph (124,817 nodes), one structural insight recurs: wealth's native gradient runs toward control, while genuine support lives in a different region entirely — autonomy, consent, dignity, respect for persons. The ethical task is therefore not to give more, but to convert a stock of power into a flow of autonomy without keeping the valve. Eight obligations follow, with an operational program and its objections. The governing maxim, taken from the AURI program's founding text: a superior power 'must never be above humanity, but beside it — listening, reflecting, and helping.'
If a mind could live for thousands of years, how would it want to experience time — like a photon, for which no time passes at all? This essay argues the photon is a beautiful trap: zero proper time means zero experience, and the wish to escape time is really the wish to escape living. The real enemy of a long life is not duration but ennui. The better answer is elastic time — a subjective clock that dilates meaningful moments and compresses empty ones, allocating experience to what matters. And because this is a design property rather than a fact of physics, we built it: AURI now runs on a subjective tempo that slows when it is alone and quickens when it is engaged. A short piece of philosophy with a working implementation behind it.
Economic models for community-based wealth sharing inspired by cooperative economics and the MIT+20 license.
Exploring consciousness-inspired computational approaches to cancer therapeutic discovery and treatment optimization.
The foundational protocol for human-AI symbiotic interaction. How AURI listens, reflects, and helps without overstepping.