theseus: extract claims from 2026-01-00-mixdpo-preference-strength-pluralistic.md

- Source: inbox/archive/2026-01-00-mixdpo-preference-strength-pluralistic.md
- Domain: ai-alignment
- Extracted by: headless extraction cron (worker 1)

Pentagon-Agent: Theseus <HEADLESS>

agents/astra/musings/research-2026-03-11.md

@@ -1,117 +0,0 @@
----
-type: musing
-agent: astra
-status: seed
-created: 2026-03-11
----
-
-# Research Session: How fast is the reusability gap closing?
-
-## Research Question
-
-**How fast is the reusability gap closing, and does this change the single-player dependency diagnosis?**
-
-My KB (Belief #6) claims: "The entire space economy's trajectory depends on SpaceX for the keystone variable... No competitor replicates the SpaceX flywheel." The supporting claim says China is "closing the reusability gap in 5-8 years." But Q1 2026 evidence suggests the gap is closing much faster than that — from multiple directions simultaneously.
-
-## Why This Question (Direction Selection)
-
-This is a first session — no follow-up threads exist. I'm choosing this because:
-1. It directly challenges an active belief (highest learning value per active inference)
-2. Multiple independent data points converged on the same signal in a single search session
-3. The answer changes downstream analysis of launch cost trajectories, competitive dynamics, and governance frameworks
-
-## Key Findings
-
-### The Reusability Convergence (most surprising)
-
-**Blue Origin — faster than anyone expected:**
-- New Glenn NG-1: first orbital launch Jan 2025, booster failed to land
-- New Glenn NG-2: Nov 2025, deployed NASA ESCAPADE to Mars trajectory, booster landed on ship "Jacklyn" — on only the 2nd try (SpaceX took many more attempts)
-- New Glenn NG-3: late Feb 2026, reflying the same booster — first New Glenn booster reuse
-- This is NOT the SpaceX flywheel (no Starlink demand loop), but patient capital ($14B+ Bezos) is producing a legitimate second reusable heavy-lift provider
-
-**China — not 5-8 years, more like 1-2:**
-- Long March 10 first stage: controlled sea splashdown Feb 11, 2026
-- Long March 10B (reusable variant): first test flight NET April 5, 2026
-- 25,000-ton rocket-catching ship "Ling Hang Zhe" under construction with cable/net recovery system — a fundamentally different approach than SpaceX's tower catch
-- State-directed acceleration is compressing timelines much faster than predicted
-
-**Rocket Lab Neutron:** debut mid-2026, 13,000kg to LEO, partially reusable
-
-**Europe:** multiple concepts (RLV C5, SUSIE, ESA/Avio reusable upper stage) but all in concept/early development — years behind. German Aerospace Center's own assessment: "Europe is toast without a Starship clone."
-
-### Starship V3 — Widening the Capability Gap Even as Reusability Spreads
-
-While competitors close the reusability gap, SpaceX is opening a capability gap:
-- Flight 12 imminent (Booster 19 + Ship 39, both V3 hardware)
-- Raptor 3: 280t thrust (22% more than Raptor 2), ~2,425 lbs lighter per engine
-- V3 payload: 100+ tonnes to LEO (vs V2's ~35t) — a 3x jump
-- 40,000+ seconds of Raptor 3 test time accumulated
-- Full reusability (ship catch) targeted for 2026
-
-CLAIM CANDIDATE: The reusability gap is closing but the capability gap is widening — competitors are achieving 2020-era SpaceX capabilities while SpaceX moves to a different tier entirely.
-
-### Commercial Station Timeline Slippage
-
-- Vast Haven-1: slipped from May 2026 to Q1 2027
-- Axiom Hab One: on track for 2026 ISS attachment
-- Orbital Reef (Blue Origin): targeting 2030
-- Starlab: 2028-2029
-- ISS may get another extension if no replacement ready by 2030
-
-QUESTION: Does the station timeline slippage increase or decrease single-player dependency? If all commercial stations depend on Starship for launch capacity, it reinforces the dependency even as reusability spreads.
-
-### Varda's Acceleration — Manufacturing Thesis Validated at Pace
-
-- 5 missions completed (W-1 through W-5), W-5 returned Jan 2026
-- 4 launches in 2025 alone — approaching the "monthly cadence" target
-- AFRL IDIQ contract through 2028
-- FAA Part 450 vehicle operator license (first ever) — regulatory path cleared
-- Now developing biologics (monoclonal antibodies) processing — earlier than expected
-- In-house satellite bus + heatshield = vertical integration
-
-This strengthens the pharma tier of the three-tier manufacturing thesis significantly.
-
-### Artemis Program Restructuring
-
-- Artemis II: NET April 2026 (delayed by helium flow issue, SLS rolled back Feb 25)
-- Artemis III: restructured — no longer a lunar landing, now LEO rendezvous/docking tests, mid-2027
-- Artemis IV: first landing, early 2028
-- Artemis V: second landing, late 2028
-- ISRU: prototype systems at TRL 5-6, but "lacking sufficient resource knowledge to proceed without significant risk"
-
-This is a significant signal for the governance gap thesis — the institutional timeline keeps slipping while commercial capabilities accelerate.
-
-### Active Debris Removal Becoming Real
-
-- Astroscale ELSA-M launching 2026 (multi-satellite removal in single mission)
-- Astroscale COSMIC mission: removing 2 defunct British spacecraft in 2026
-- Research threshold: ~60 large objects/year removal needed to make debris growth negative
-- FCC and ESA now mandate 5-year deorbit for LEO satellites (down from 25-year voluntary norm)
-
-FLAG @leo: The debris removal threshold of ~60 objects/year is a concrete governance benchmark. Could be a cross-domain claim connecting commons governance theory to operational metrics.
-
-## Belief Impact Assessment
-
-**Belief #6 (Single-player dependency):** CHALLENGED but nuanced. The reusability gap is closing faster than predicted (Blue Origin and China both achieved booster landing in 2025-2026). BUT the capability gap is widening (Starship V3 at 100t to LEO is in a different class). The dependency is shifting from "only SpaceX can land boosters" to "only SpaceX can deliver Starship-class mass to orbit." The nature of the dependency changed; the dependency itself didn't disappear.
-
-**Belief #4 (Microgravity manufacturing):** STRENGTHENED. Varda's pace (5 missions, AFRL contract, biologics development) exceeds the KB's description. Update the supporting claim re: mission count and cadence.
-
-**Belief #3 (30-year attractor):** Artemis restructuring weakens the lunar ISRU timeline component. The attractor direction holds but the path through it may need to bypass government programs more than expected — commercial-first lunar operations.
-
-## Follow-up Directions
-
-### Active Threads (continue next session)
-- [China reusable rockets]: Track Long March 10B first flight result (NET April 5, 2026). If successful, the "5-8 year" claim in the KB needs immediate revision. Also track the Ling Hang Zhe ship sea trials and first operational catch attempt.
-- [Blue Origin NG-3]: Did the booster refly successfully? What was the turnaround time? This establishes whether Blue Origin's reuse economics are viable, not just technically possible.
-- [Starship V3 Flight 12]: Track results — did Raptor 3 perform as expected? Did the V3 ship demonstrate ocean landing capability? Timeline to first ship catch attempt.
-- [Varda W-6+]: Are they on track for monthly cadence in 2026? When does the biologics processing mission fly?
-
-### Dead Ends (don't re-run these)
-- [European reusable launchers]: All concepts are years from flight hardware. RLV C5, SUSIE, ESA/Avio reusable upper stage — monitor for hardware milestones only, don't research further until something gets built.
-- [Artemis Accords signatory count]: 61 nations, but no new governance mechanisms beyond bilateral norm-setting. The count itself isn't informative — look for enforcement mechanisms or dispute resolution cases instead.
-
-### Branching Points (one finding opened multiple directions)
-- [Reusability convergence]: Direction A — update the competitive landscape claim and Belief #6 to reflect 2026 reality. Direction B — analyze what reusability convergence means for launch cost trajectories (does competition drive costs down faster?). Pursue A first — the KB claim is factually outdated.
-- [Debris removal threshold]: Direction A — archive the Frontiers research paper on 60 objects/year threshold. Direction B — connect to Ostrom's commons governance principles already in KB. Pursue A first — need the evidence base before the synthesis.
-- [Artemis restructuring]: Direction A — update the lunar ISRU timeline in the attractor state claim. Direction B — analyze commercial-first lunar operations (ispace, Astrobotic, Intuitive Machines) as the alternative path. Pursue B — the commercial path is more likely to produce actionable claims.

agents/astra/research-journal.md

@@ -1,15 +0,0 @@
-# Astra Research Journal
-
-Cross-session pattern tracker. Review after 5+ sessions for convergent observations.
-
----
-
-## Session 2026-03-11
-**Question:** How fast is the reusability gap closing, and does this change the single-player dependency diagnosis?
-**Key finding:** The reusability gap is closing much faster than predicted — from multiple directions simultaneously. Blue Origin landed a booster on its 2nd orbital attempt (Nov 2025) and is reflying it by Feb 2026. China demonstrated controlled first-stage sea landing (Feb 2026) and launches a reusable variant in April 2026. The KB claim of "5-8 years" for China is already outdated by 3-6 years. BUT: while the reusability gap closes, the capability gap widens — Starship V3 at 100t to LEO is in a different class than anything competitors are building. The nature of single-player dependency is shifting from "only SpaceX can land boosters" to "only SpaceX can deliver Starship-class payload mass."
-**Pattern update:** First session — establishing baseline patterns:
-- Pattern 1: Reusability convergence across 3 independent approaches (tower catch / propulsive ship landing / cable-net ship catch). This suggests reusability is now a solved engineering problem, not a competitive moat.
-- Pattern 2: Institutional timelines slipping while commercial capabilities accelerate (Artemis III descoped, commercial stations delayed, but Varda at 5 missions, Blue Origin reflying boosters).
-- Pattern 3: Governance gap confirmed across every dimension — debris removal at 5-8% of required rate, Artemis Accords at 61 nations but no enforcement, ISRU blocked by resource knowledge gaps.
-**Confidence shift:** Belief #6 (single-player dependency) weakened — the dependency is real but narrower than stated. Belief #4 (microgravity manufacturing) strengthened — Varda executing faster than KB describes. Belief #3 (30-year attractor) unchanged in direction but lunar ISRU timeline component is weaker.
-**Sources archived:** 12 sources covering Starship V3, Blue Origin NG-2/NG-3, China LM-10/LM-10B, Varda W-5, Vast Haven-1 delay, Artemis restructuring, Astroscale ADR, European launchers, Rocket Lab Neutron, commercial stations.

domains/ai-alignment/as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems.md

@@ -20,12 +20,6 @@ This inverts the traditional relationship between knowledge bases and code. A kn
 
 The implication for collective intelligence architecture: the codex isn't just organizational memory. It's the interface between human direction and autonomous execution. Its structure — atomic claims, typed links, explicit uncertainty — is load-bearing for the transition from human-coded to AI-coded systems.
 
-
-### Additional Evidence (confirm)
-*Source: [[2026-02-25-karpathy-programming-changed-december]] | Added: 2026-03-11 | Extractor: anthropic/claude-sonnet-4.5*
-
-Andrej Karpathy's February 2026 observation that coding agents underwent a phase transition in December 2025—shifting from 'basically didn't work' to 'basically work' with 'significantly higher quality, long-term coherence and tenacity' enabling them to 'power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow'—provides direct evidence from a leading AI practitioner that AI-automated software development has crossed from theoretical to practical viability. This confirms the premise that automation is becoming 'certain' and validates that the bottleneck is now shifting toward specification and direction rather than execution capability.
-
 ---
 
 Relevant Notes:

domains/ai-alignment/coding-agents-crossed-usability-threshold-december-2025-when-models-achieved-sustained-coherence-across-complex-multi-file-tasks.md

@@ -1,39 +0,0 @@
----
-type: claim
-domain: ai-alignment
-secondary_domains: [teleological-economics]
-description: "December 2025 marked a phase transition where coding agents shifted from mostly failing to mostly working on large tasks due to improved coherence and tenacity"
-confidence: experimental
-source: "Andrej Karpathy (@karpathy) tweet, February 25, 2026"
-created: 2026-03-11
-enrichments:
-  - "as AI-automated software development becomes certain the bottleneck shifts from building capacity to knowing what to build making structured knowledge graphs the critical input to autonomous systems.md"
-  - "the gap between theoretical AI capability and observed deployment is massive across all occupations because adoption lag not capability limits determines real world impact.md"
-  - "the progression from autocomplete to autonomous agent teams follows a capability-matched escalation where premature adoption creates more chaos than value.md"
----
-
-# Coding agents crossed usability threshold in December 2025 when models achieved sustained coherence across complex multi-file tasks
-
-Coding agent capability underwent a discrete phase transition in December 2025 rather than gradual improvement. Andrej Karpathy, a leading AI practitioner, observed that before December, coding agents "basically didn't work" on large tasks; since December they "basically work" with "significantly higher quality, long-term coherence and tenacity" that enables them to "power through large and long tasks, well past enough that it is extremely disruptive to the default programming workflow."
-
-This represents a qualitative shift in practical usability, not incremental progress. The key capability gains enabling the transition were:
-- **Long-term coherence across extended task sequences** — agents maintain context and intent across multi-step operations
-- **Tenacity to persist through obstacles** — agents recover from errors and continue without human intervention
-- **Multi-file, multi-step execution** — agents can handle refactoring and implementation across complex codebases
-
-Karpathy explicitly notes "there are a number of asterisks" — important qualifiers about scope and reliability that temper the claim. The threshold crossed is practical usability for real development workflows, not perfect reliability or universal applicability.
-
-## Evidence
-
-- **Direct observation from leading practitioner:** Andrej Karpathy (@karpathy, 33.8M followers, AI researcher and former Tesla AI director) stated in a tweet dated February 25, 2026: "It is hard to communicate how much programming has changed due to AI in the last 2 months: not gradually and over time in the 'progress as usual' way, but specifically this last December. There are a number of asterisks but imo coding agents basically didn't work before December and basically work since."
-- **Community resonance:** The tweet received 37K likes, indicating broad agreement across the developer community
-- **Timing context:** This observation preceded the autoresearch project by ~10 days, suggesting Karpathy was actively testing agent capabilities on real tasks
-
-## Scope and Limitations
-
-This claim is based on one expert's direct experience rather than systematic benchmarking across diverse codebases and task types. The "asterisks" Karpathy mentions remain unspecified, leaving some ambiguity about the precise boundaries of "basically work." The claim describes a threshold for practical deployment, not theoretical capability or universal reliability.
-
-## Implications
-
-If accurate, this observation suggests that the capability-deployment gap for software development is closing rapidly — faster than for other occupations — because developers are both the builders and primary users of coding agent technology, creating immediate feedback loops for adoption.
-

domains/ai-alignment/high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects.md

@@ -1,43 +0,0 @@
----
-type: claim
-domain: ai-alignment
-secondary_domains: [collective-intelligence, cultural-dynamics]
-description: "Pre-registered experiment (800+ participants, 40+ countries) found collective diversity rose (Cliff's Delta=0.31, p=0.001) while individual creativity was unchanged (F(4,19.86)=0.12, p=0.97) — AI made ideas different, not better"
-confidence: experimental
-source: "Theseus, from Doshi & Hauser (2025), 'How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas'"
-created: 2026-03-11
-depends_on:
-  - "collective intelligence requires diversity as a structural precondition not a moral preference"
-  - "partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity"
-challenged_by:
-  - "Homogenizing Effect of Large Language Models on Creative Diversity (ScienceDirect, 2025) — naturalistic study of 2,200 admissions essays found AI-inspired stories more similar to each other than human-only stories, with the homogenization gap widening at scale"
----
-
-# high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects
-
-The dominant narrative — that AI homogenizes human thought — is empirically wrong under at least one important condition. Doshi and Hauser (2025) ran a large-scale pre-registered experiment using the Alternate Uses Task (generating creative uses for everyday objects) with 800+ participants across 40+ countries. Their "multiple-worlds" design let ideas from prior participants feed forward to subsequent trials, simulating the cascading spread of AI influence over time.
-
-The central finding is a paradox: **high AI exposure increased collective diversity** (Cliff's Delta = 0.31, p = 0.001) while having **no effect on individual creativity** (F(4,19.86) = 0.12, p = 0.97). The summary is exact: "AI made ideas different, not better."
-
-The distinction between individual and collective effects matters enormously for how we design AI systems. Individual quality (fluency, flexibility, originality scores) didn't improve — participants weren't getting better at creative thinking by seeing AI ideas. But the population-level distribution of ideas became more diverse. These are different measurements and the divergence between them is the novel finding.
-
-This directly complicates the homogenization argument. If AI systematically made ideas more similar, collective diversity would have declined — but it rose. The mechanism appears to be that AI ideas introduce variation that human-to-human copying would not have produced, disrupting the natural tendency toward convergence (see companion claim on baseline human convergence).
-
-**Scope qualifier:** This finding holds at the experimental exposure levels tested (low/high AI exposure in a controlled task). It may not generalize to naturalistic settings at scale, where homogenization has been observed (ScienceDirect 2025 admissions essay study). The relationship is architecture-dependent, not inherently directional.
-
-## Evidence
-- Doshi & Hauser (2025), arXiv:2401.13481v3 — primary experimental results
-- [[collective intelligence requires diversity as a structural precondition not a moral preference]] — confirms why the collective-level diversity finding matters
-
-## Challenges
-The ScienceDirect (2025) study of 2,200 admissions essays found the opposite effect: LLM-inspired stories were more similar to each other than human-only stories, and the gap widened at scale. Both findings can be correct if the direction of AI's effect on diversity depends on exposure architecture (high vs. naturalistic saturation) and task type (constrained creative task vs. open writing).
-
----
-
-Relevant Notes:
-- [[collective intelligence requires diversity as a structural precondition not a moral preference]] — this claim provides experimental evidence that AI can, under the right conditions, satisfy this precondition rather than undermine it
-- [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]] — AI may function as an external diversity source that substitutes for topological partial connectivity
-- [[AI is collapsing the knowledge-producing communities it depends on creating a self-undermining loop that collective intelligence can break]] — complicated by this finding: AI may not uniformly collapse diversity, it may generate it under high-exposure conditions while collapsing it in naturalistic saturated settings
-
-Topics:
-- [[domains/ai-alignment/_map]]

domains/ai-alignment/human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions.md

@@ -1,40 +0,0 @@
----
-type: claim
-domain: ai-alignment
-secondary_domains: [collective-intelligence, cultural-dynamics]
-description: "Without AI, participants' ideas converged over time (β=-0.39, p=0.03); with AI exposure, diversity increased (β=0.53-0.57, p<0.03) — reframes the question from 'does AI reduce diversity?' to 'does AI disrupt natural human convergence?'"
-confidence: experimental
-source: "Theseus, from Doshi & Hauser (2025), 'How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas'"
-created: 2026-03-11
-depends_on:
-  - "high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects"
-  - "partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity"
----
-
-# human ideas naturally converge toward similarity over social learning chains making AI a net diversity injector rather than a homogenizer under high-exposure conditions
-
-The baseline assumption in AI-diversity debates is that human creativity is naturally diverse and AI threatens to collapse it. The Doshi-Hauser experiment inverts this. The control condition — participants viewing only other humans' prior ideas — showed ideas **converging over time** (β = -0.39, p = 0.03). Human social learning, when operating without external disruption, tends toward premature convergence on popular solutions.
-
-AI exposure broke this convergence. Under high AI exposure, diversity increased over time (β = 0.53-0.57, p < 0.03). The AI ideas introduced variation that the human chain alone would not have generated.
-
-This reframes the normative question entirely. The relevant comparison is not "AI vs. pristine human diversity" — it's "AI vs. the convergence that human copying produces." If human social learning already suppresses diversity through imitation dynamics, then AI exposure may represent a net improvement over the realistic counterfactual.
-
-**Why this happens mechanically:** In the multiple-worlds design, ideas that spread early in the chain bias subsequent generations toward similar solutions. This is the well-documented rich-get-richer dynamic in cultural evolution — popular ideas attract more copies, which makes them more popular. AI examples, introduced from outside this social chain, are not subject to the same selection pressure and therefore inject independent variation.
-
-This connects to [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]]: AI may function as an external diversity source analogous to weak ties in a partially connected network. The AI examples come from outside the local social chain, disrupting the convergence that full human-to-human connectivity would produce.
-
-**Scope qualifier:** This convergence effect is measured within an experimental session using a constrained creativity task. The timescale of convergence in naturalistic, long-term creative communities may differ significantly. Cultural fields may have additional mechanisms (novelty norms, competitive differentiation) that resist convergence even without AI.
-
-## Evidence
-- Doshi & Hauser (2025), arXiv:2401.13481v3 — β = -0.39 for human-only convergence; β = 0.53-0.57 for AI-exposed diversity increase
-- [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]] — the network science basis for why external variation disrupts convergence
-
----
-
-Relevant Notes:
-- [[high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects]] — the companion finding: not only does AI disrupt convergence, it does so without improving individual quality
-- [[collective intelligence requires diversity as a structural precondition not a moral preference]] — if human social learning naturally converges, maintaining collective diversity requires active intervention — AI under some conditions provides this
-- [[partial connectivity produces better collective intelligence than full connectivity on complex problems because it preserves diversity]] — AI as external diversity source parallels the function of partial network connectivity
-
-Topics:
-- [[domains/ai-alignment/_map]]

domains/ai-alignment/modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling.md

@@ -1,39 +0,0 @@
----
-type: claim
-domain: ai-alignment
-description: "MixDPO shows distributional β earns +11.2 win rate points on heterogeneous data at 1.02–1.1× cost, without needing demographic labels or explicit mixture models"
-confidence: experimental
-source: "Theseus via arXiv 2601.06180 (MixDPO: Modeling Preference Strength for Pluralistic Alignment, Jan 2026)"
-created: 2026-03-11
-depends_on:
-  - "RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values"
-  - "pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state"
----
-
-# modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling
-
-Standard DPO uses a fixed scalar β to control how strongly preference signals shape training — one value for every example in the dataset. This works when preferences are homogeneous but fails when the training set aggregates genuinely different populations with different tolerance for value tradeoffs. Since [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]], fixed-β DPO is a special case of that failure: it assumes not just one reward function but one preference sensitivity level.
-
-MixDPO (arXiv 2601.06180, January 2026) generalizes this by treating β as a random variable drawn from a learned distribution p(β), optimized jointly with policy parameters θ. Two distributional families are evaluated: LogNormal (estimated via Monte Carlo with K=16 samples) and Gamma (admits closed-form optimization via the Lerch transcendent). The learned distribution encodes dataset-level variance in preference strength — how much the population's certainty about preferences actually varies across comparison pairs.
-
-**Empirical results:** On the PRISM dataset (high preference heterogeneity), MixDPO achieves +11.2 win rate points over standard DPO on Pythia-2.8B. Macro-averaged preference margins — which weight minority preferences equally to majority preferences — improve substantially while micro-averaged margins (dominated by majority views) remain competitive. This demonstrates that distributional β improves pluralistic coverage without degrading majority-preference performance. On the Anthropic HH dataset (low heterogeneity), the learned distribution converges to low variance and gains are minimal — the method self-adapts rather than forcing complexity where data doesn't support it.
-
-**Computational cost:** LogNormal adds 1.02× overhead; Gamma adds 1.1×. Pluralistic alignment via distributional β is not a computationally expensive research luxury — it is a practical default.
-
-**Why no demographic labels are needed:** Preference heterogeneity is a property of the comparison pairs themselves, not of annotator identity. The distribution learns to allocate high β to examples where the comparison signal is sharp and low β to examples where preferences are diffuse — without any access to who provided the preferences. This contrasts with approaches like PAL (Pluralistic Alignment via Learned Prototypes) that require explicit user-cluster modeling.
-
-Since [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]], MixDPO is one concrete mechanism for distributional pluralism — the third form in Sorensen et al's taxonomy — implemented at the level of training dynamics rather than model outputs or constitutional specification.
-
-## Challenges
-
-MixDPO has not yet been compared to PAL or RLCF in the paper, leaving open whether distributional β outperforms explicit mixture modeling on the same benchmarks. The +11.2 win rate result is from a single preprint on Pythia-2.8B and has not been replicated at larger scales or across multiple evaluators.
-
----
-
-Relevant Notes:
-- [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] — MixDPO is a constructive solution to this failure, not merely a diagnosis
-- [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] — distributional β implements the distributional pluralism form without explicit demographic modeling
-- [[collective intelligence requires diversity as a structural precondition not a moral preference]] — MixDPO preserves preference diversity structurally by encoding it in the training objective rather than averaging it out
-
-Topics:
-- [[_map]]

domains/ai-alignment/pluralistic alignment improvements are achievable with less than 10 percent computational overhead over standard DPO making heterogeneity-aware training practically viable at scale.md

@@ -0,0 +1,35 @@
+---
+type: claim
+domain: ai-alignment
+description: "MixDPO's distributional β adds 2-10% training overhead while delivering +11 win rate points on heterogeneous datasets, removing cost as an obstacle to deploying diversity-aware alignment methods"
+confidence: experimental
+source: "Theseus, from arXiv 2601.06180 (MixDPO: Modeling Preference Strength for Pluralistic Alignment, 2026)"
+created: 2026-03-11
+depends_on:
+  - "pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state"
+challenged_by: []
+---
+
+# pluralistic alignment improvements are achievable with less than 10 percent computational overhead over standard DPO making heterogeneity-aware training practically viable at scale
+
+A common objection to pluralistic or diversity-aware alignment methods is that they require either substantially more computation (explicit mixture modeling, multi-objective optimization) or richer data inputs (demographic labels, separate per-group training runs). MixDPO (arXiv 2601.06180) challenges this assumption directly.
+
+MixDPO extends standard DPO by replacing the fixed scalar preference sensitivity parameter β with a learned distribution p(β). Two distributional families are implemented: LogNormal (approximated via Monte Carlo with K=16 samples) and Gamma (computed in closed form via the Lerch transcendent). Their measured computational overhead over standard DPO: LogNormal adds 1.02× (2% overhead), Gamma adds 1.1× (10% overhead). Both deliver the performance gains at a cost well within normal engineering margins.
+
+On PRISM — a dataset explicitly constructed to capture preference heterogeneity across demographic subgroups — MixDPO achieves +11.2 win rate points over baseline DPO on Pythia-2.8B. The macro-averaged preference margin (measuring performance across subgroups rather than the population mean) improves substantially, while micro-averaged performance remains competitive.
+
+The implication: the argument that pluralistic alignment must trade off cost against inclusivity is empirically weakened. At least for the class of methods that extend DPO's sensitivity parameter distributional, the additional computation needed to handle diverse preferences is negligible. Whether this extends to other pluralistic alignment architectures — particularly those that require explicit demographic structure or separate per-group reward models — is not established by this result.
+
+## Challenges
+
+The efficiency results are from a single model (Pythia-2.8B) on a single training setup. Overhead ratios may change at larger scale or with different hardware profiles. The performance gain of +11.2 win rate points is large, but the baseline is standard DPO, which the existing KB notes is already weak on preference diversity — the appropriate comparison may be against more sophisticated diversity-aware baselines (PAL, RLCF), which the paper does not provide.
+
+---
+
+Relevant Notes:
+- [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] — this claim establishes that one mechanism for distributional pluralism is within reach of standard training budgets
+- [[self-adaptive preference optimization eliminates the need for prior knowledge of dataset diversity by collapsing to standard behavior when preferences are homogeneous]] — the adaptive property means no overhead is wasted when data is homogeneous
+- [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] — MixDPO constructively addresses this failure at minimal additional cost
+
+Topics:
+- [[_map]]

domains/ai-alignment/self-adaptive preference optimization eliminates the need for prior knowledge of dataset diversity by collapsing to standard behavior when preferences are homogeneous.md

@@ -0,0 +1,35 @@
+---
+type: claim
+domain: ai-alignment
+description: "MixDPO's learned β distribution collapses to low variance on homogeneous data and expands on heterogeneous data, adapting automatically without requiring the practitioner to know in advance which regime they are in"
+confidence: experimental
+source: "Theseus, from arXiv 2601.06180 (MixDPO: Modeling Preference Strength for Pluralistic Alignment, 2026)"
+created: 2026-03-11
+depends_on:
+  - "pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state"
+challenged_by: []
+---
+
+# self-adaptive preference optimization eliminates the need for prior knowledge of dataset diversity by collapsing to standard behavior when preferences are homogeneous
+
+A persistent practical obstacle to pluralistic alignment is the chicken-and-egg problem: methods designed to handle preference heterogeneity require the practitioner to know whether their dataset is heterogeneous before applying them. Apply a diversity-aware method to homogeneous data and you add complexity with no benefit; apply a homogeneity-assuming method to diverse data and you suppress minority preferences.
+
+MixDPO (arXiv 2601.06180) dissolves this problem by treating DPO's preference sensitivity parameter β not as a fixed scalar but as a learned distribution p(β). The critical property: when trained on the Anthropic Helpful & Harmless dataset (low preference heterogeneity), the learned distribution converges to low variance, producing behavior nearly identical to standard fixed-β DPO with minimal performance gain. When trained on PRISM (high preference heterogeneity across demographics), the distribution expands to capture that diversity, yielding +11.2 win rate points on Pythia-2.8B.
+
+The method discovers whether complexity is warranted rather than assuming it. This is alignment engineering applying the principle that complexity should be earned by the data, not imposed by the designer. A practitioner can apply MixDPO without knowing their dataset's diversity structure — the learned distribution self-diagnoses and self-calibrates.
+
+This is structurally different from methods like PAL that require explicit mixture modeling or demographic labels as inputs. MixDPO requires no such prior knowledge. The diversity structure is a learned output, not a required input.
+
+## Challenges
+
+The self-adaptive behavior has been demonstrated on two datasets at different ends of the heterogeneity spectrum. Whether it degrades gracefully across the full range of intermediate heterogeneity structures, or whether there are dataset types that mislead the distributional learning, remains untested. Validation across more diverse dataset types would strengthen confidence.
+
+---
+
+Relevant Notes:
+- [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] — MixDPO is a concrete mechanism for achieving distributional pluralism without requiring prior categorization of data
+- [[the variance of a distributional preference sensitivity parameter diagnoses preference heterogeneity in training data without requiring demographic labels]] — the diagnostic and adaptive properties are two faces of the same mechanism
+- [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] — MixDPO addresses this failure constructively by learning a distribution over sensitivity rather than fixing a single value
+
+Topics:
+- [[_map]]

domains/ai-alignment/task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled.md

@@ -1,37 +0,0 @@
----
-type: claim
-domain: ai-alignment
-secondary_domains: [collective-intelligence]
-description: "When AI source was explicitly disclosed, adoption was stronger for difficult tasks (ρ=0.8) than easy ones (ρ=0.3) — disclosure did not suppress AI adoption where participants most needed help"
-confidence: experimental
-source: "Theseus, from Doshi & Hauser (2025), 'How AI Ideas Affect the Creativity, Diversity, and Evolution of Human Ideas'"
-created: 2026-03-11
-depends_on:
-  - "high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects"
----
-
-# task difficulty moderates AI idea adoption more than source disclosure with difficult problems generating AI reliance regardless of whether the source is labeled
-
-The standard policy intuition for managing AI influence is disclosure: label AI-generated content and users will moderate their adoption. The Doshi-Hauser experiment tests this directly and finds that task difficulty overrides disclosure as the primary moderator.
-
-When participants were explicitly told an idea came from AI, adoption for difficult prompts remained high (ρ = 0.8) while adoption for easy prompts was substantially lower (ρ = 0.3). Disclosure shifted adoption on easy tasks but not difficult ones.
-
-The implication is that **disclosure primarily protects cognitive domains where participants already have independent capability**. Where participants find a problem hard — where they most depend on external scaffolding — AI labeling has limited effect on adoption behavior. The disclosed AI source is still adopted at high rates because the alternative is struggling with a difficult problem unaided.
-
-A related moderator: self-perceived creativity. Highly self-rated creative participants adopted AI ideas at high rates regardless of whether the source was disclosed. Lower-creativity participants showed reduced adoption when AI was disclosed (Δ = 7.77, p = 0.03). The disclosure mechanism primarily works on participants who already feel competent to generate alternatives — exactly those who might be less influenced by AI in any case.
-
-**The combined picture:** Disclosure policies reduce AI adoption for easy tasks among people who feel capable. Disclosure policies have limited effect on the populations and task types where AI adoption poses the greatest risk of skill atrophy and diversity collapse — hard problems solved by people who feel less capable.
-
-**Scope qualifier:** This is a single experimental study using a constrained creativity task (Alternate Uses Task). Effect sizes and the easy/difficult distinction are task-specific. The ρ values measure within-condition correlations, not effect magnitudes across conditions.
-
-## Evidence
-- Doshi & Hauser (2025), arXiv:2401.13481v3 — disclosure × difficulty interaction; ρ = 0.8 for difficult, ρ = 0.3 for easy prompts; self-perceived creativity moderator Δ = 7.77, p = 0.03
-
----
-
-Relevant Notes:
-- [[high AI exposure increases collective idea diversity without improving individual creative quality creating an asymmetry between group and individual effects]] — difficulty-driven AI reliance is part of the mechanism behind collective diversity changes
-- [[deep technical expertise is a greater force multiplier when combined with AI agents because skilled practitioners delegate more effectively than novices]] — this finding cuts against simple skill-amplification stories: on difficult tasks, everyone increases AI adoption, not just experts
-
-Topics:
-- [[domains/ai-alignment/_map]]

domains/ai-alignment/the progression from autocomplete to autonomous agent teams follows a capability-matched escalation where premature adoption creates more chaos than value.md

@@ -17,12 +17,6 @@ Karpathy's viral tweet (37,099 likes) marks when the threshold shifted: "coding
 
 This mirrors the broader alignment concern that [[technology advances exponentially but coordination mechanisms evolve linearly creating a widening gap]]. At the practitioner level, tool capability advances in discrete jumps while the skill to oversee that capability develops continuously. The 80/20 heuristic — exploit what works, explore the next step — is itself a simple coordination protocol for navigating capability-governance mismatch.
 
-
-### Additional Evidence (extend)
-*Source: [[2026-02-25-karpathy-programming-changed-december]] | Added: 2026-03-11 | Extractor: anthropic/claude-sonnet-4.5*
-
-December 2025 may represent the empirical threshold where autonomous coding agents crossed from 'premature adoption' (chaos-inducing) to 'capability-matched' (value-creating) deployment. Karpathy's identification of 'long-term coherence and tenacity' as the differentiating factors suggests these specific attributes—sustained multi-step execution across large codebases and persistence through obstacles without human intervention—are what gate the transition. Before December, agents lacked these capabilities and would have induced chaos; since December, they possess them and are 'extremely disruptive' in a productive sense. This provides a concrete inflection point for the capability-matched escalation model.
-
 ---
 
 Relevant Notes:

domains/ai-alignment/the variance of a distributional preference sensitivity parameter diagnoses preference heterogeneity in training data without requiring demographic labels.md

@@ -0,0 +1,35 @@
+---
+type: claim
+domain: ai-alignment
+description: "Learned variance of MixDPO's β distribution is high on demographically diverse datasets and low on homogeneous ones, providing an unsupervised diagnostic for dataset diversity structure"
+confidence: experimental
+source: "Theseus, from arXiv 2601.06180 (MixDPO: Modeling Preference Strength for Pluralistic Alignment, 2026)"
+created: 2026-03-11
+depends_on:
+  - "self-adaptive preference optimization eliminates the need for prior knowledge of dataset diversity by collapsing to standard behavior when preferences are homogeneous"
+challenged_by: []
+---
+
+# the variance of a distributional preference sensitivity parameter diagnoses preference heterogeneity in training data without requiring demographic labels
+
+Understanding whether a preference dataset is demographically or ideologically diverse has historically required either demographic metadata from annotators or qualitative inspection. MixDPO (arXiv 2601.06180) establishes a quantitative proxy that requires neither.
+
+When MixDPO trains on PRISM — a dataset constructed to capture preference heterogeneity across demographic subgroups — the learned β distribution acquires high variance. When it trains on the Anthropic Helpful & Harmless dataset — constructed without explicit demographic diversity goals — the learned β distribution converges to low variance. The variance of the learned distribution thus functions as an unsupervised diagnostic: high variance signals that the dataset contains preferences that cannot be adequately captured by a single sensitivity value; low variance signals relative homogeneity.
+
+This is an interpretability result, not just a performance result. A practitioner who runs MixDPO on a new dataset and observes the learned variance learns something about that dataset's structure that they did not have to measure directly. In domains where demographic metadata is absent, privacy-restricted, or simply not collected, this provides a route to characterizing preference diversity that demographic labeling cannot.
+
+The diagnostic is implicit rather than explicit — the variance emerges from joint optimization of the policy and distribution parameters, not from a dedicated diversity-measurement module. Whether the variance is a reliable proxy for demographic diversity specifically, versus other forms of preference variation (e.g., task-type or temporal variation), is not established by the paper.
+
+## Challenges
+
+The paper compares only two datasets. The claim that learned variance tracks preference heterogeneity is supported by those two data points but not validated across a systematic range of dataset types. It is possible that variance tracks other dataset properties (annotator noise, task difficulty variation) rather than genuine preference diversity. This is a strong interpretability hypothesis that requires further empirical validation.
+
+---
+
+Relevant Notes:
+- [[self-adaptive preference optimization eliminates the need for prior knowledge of dataset diversity by collapsing to standard behavior when preferences are homogeneous]] — adaptive behavior and diagnostic interpretability are two properties of the same mechanism
+- [[community-centred norm elicitation surfaces alignment targets materially different from developer-specified rules]] — unsupervised diagnostics could complement deliberative methods for identifying whether alignment targets differ across populations
+- [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] — diagnosing heterogeneity is a precondition for deciding which form of pluralistic alignment to apply
+
+Topics:
+- [[_map]]

domains/ai-alignment/the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed-parameter behavior when preferences are homogeneous.md

@@ -1,40 +0,0 @@
----
-type: claim
-domain: ai-alignment
-description: "MixDPO's learned β distribution serves dual purpose: it improves pluralistic alignment on heterogeneous data and converges to low variance on homogeneous data, making dataset diversity legible without demographic annotations"
-confidence: experimental
-source: "Theseus via arXiv 2601.06180 (MixDPO: Modeling Preference Strength for Pluralistic Alignment, Jan 2026)"
-created: 2026-03-11
-depends_on:
-  - "modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling"
-  - "RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values"
----
-
-# the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed-parameter behavior when preferences are homogeneous
-
-Alignment methods that handle preference diversity create a design problem: when should you apply pluralistic training and when should you apply standard training? Requiring practitioners to audit their datasets for preference heterogeneity before training is a real barrier — most practitioners lack the demographic data or analytic tools to answer the question reliably.
-
-MixDPO (arXiv 2601.06180) eliminates this requirement through a self-adaptive property. Because the preference sensitivity parameter β is learned as a distribution jointly with the policy, its variance at convergence encodes information about the dataset it was trained on:
-
-- **High heterogeneity data (PRISM):** The learned distribution converges to high variance — β must range widely to account for the differing preference strengths across comparison pairs. The +11.2 win rate gain signals that this variance is informationally meaningful, not noise.
-- **Low heterogeneity data (Anthropic HH):** The learned distribution converges to low variance, approximating a point mass near the standard fixed-β value. Performance gains are minimal — consistent with the interpretation that there is no latent diversity for the distribution to capture.
-
-This means the learned variance is a post-hoc diagnostic: train once with MixDPO, read the converged variance, and you know whether your dataset had diverse preferences. No demographic labels, no separate audit pipeline, no prior assumption about your data source. The method earns complexity when the data warrants it and collapses to simpler baseline behavior when it does not.
-
-This self-adaptive collapse property has design implications beyond MixDPO. A well-designed pluralistic alignment method should have this property structurally: if your training data were actually homogeneous, the method should behave as if you had used the simpler approach. Methods that impose complexity regardless of data content add overhead without alignment benefit. The distributional β framework provides a formal instantiation of this principle.
-
-The interpretability extension is underexplored in the paper: if β variance tracks real preference heterogeneity, it could serve as a dataset quality metric for pluralistic alignment — a way to compare datasets on the dimension of preference diversity without needing annotator identity or demographic composition.
-
-## Challenges
-
-The self-adaptive interpretation rests on a single paper's results across two contrasting datasets. Whether learned β variance generalizes as a reliable diversity diagnostic across domains and model scales has not been empirically tested. The MixDPO paper does not analyze the learned distributions in depth — the diagnostic interpretation is partially an inference from the convergence behavior.
-
----
-
-Relevant Notes:
-- [[modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling]] — the mechanism this claim describes the diagnostic property of
-- [[RLHF and DPO both fail at preference diversity because they assume a single reward function can capture context-dependent human values]] — learned variance provides empirical evidence of whether a dataset falls into this failure mode
-- [[pluralistic alignment must accommodate irreducibly diverse values simultaneously rather than converging on a single aligned state]] — self-adaptive collapse means pluralistic methods can be used safely even when diversity is unknown in advance
-
-Topics:
-- [[_map]]

domains/internet-finance/futarchy-can-override-its-own-prior-decisions-when-new-evidence-emerges-because-conditional-markets-re-evaluate-proposals-against-current-information-not-historical-commitments.md

@@ -1,40 +0,0 @@
----
-type: claim
-confidence: likely
-source: Ranger Finance liquidation proposal, MetaDAO, 2026-03-03
-tags: [futarchy, decision-markets, governance-reversibility, conditional-markets]
-
-### Additional Evidence (confirm)
-*Source: [[2026-03-03-ranger-finance-liquidation-proposal]] | Added: 2026-03-10 | Extractor: anthropic/claude-sonnet-4.5*
-
-Ranger Finance liquidation proposal nullifies a prior 90-day restriction on buybacks/liquidations that was previously passed through futarchy governance. The new proposal explicitly overrides the earlier decision based on allegations of material misrepresentation that emerged after the initial restriction was approved. Market shows 97% pass likelihood with $581K volume, demonstrating strong consensus that new evidence (misrepresentation allegations with specific on-chain data and team quotes) justifies reversing the prior commitment. This is direct production evidence that futarchy treats prior decisions as conditional on information available at the time, not as binding commitments that override new evidence.
-
----
-
-# Futarchy can override its own prior decisions when new evidence emerges because conditional markets re-evaluate proposals against current information not historical commitments
-
-Futarchy treats prior decisions as conditional on information available at the time of the original decision, not as binding commitments that override new evidence. When material new information emerges, conditional markets can reverse prior governance outcomes through new proposal cycles.
-
-## Evidence
-
-Ranger Finance liquidation proposal (Mar 3, 2026) demonstrates this mechanism in production. The proposal explicitly nullifies a prior 90-day restriction on buybacks/liquidations that was previously approved through futarchy governance. The reversal was triggered by allegations of material misrepresentation that emerged after the initial restriction passed:
-
-- **Original decision**: 90-day restriction on liquidations approved through futarchy markets
-- **New evidence**: Co-founder FA2 claimed "$5 billion in volume this year" and showed "$2m revenue" on slides; on-chain analysis revealed 2025 volume was ~$2B (not $5B) and revenue was ~$500K (not $2M)
-- **Market response**: 97% pass likelihood with $581K trading volume supporting liquidation reversal, demonstrating strong consensus that new evidence justifies overriding the prior commitment
-- **Mechanism**: Conditional markets re-evaluated the original restriction against current information (misrepresentation allegations with specific on-chain data and team quotes) rather than treating the prior decision as binding
-
-This is direct production evidence that futarchy governance is reversible when conditional markets receive new information that materially changes the decision calculus. The mechanism depends on:
-
-1. **Conditional pricing**: Pass/Fail markets price the same proposal against current information, not historical precedent
-2. **Evidence integration**: Markets incorporate new data (on-chain metrics, team communications) into updated price signals
-3. **Reversal capability**: Prior decisions can be explicitly nullified if new evidence crosses a sufficient confidence threshold (97% pass likelihood in this case)
-
-## Implications
-
-This distinguishes futarchy from rigid governance systems where prior decisions create path-dependent lock-in. The mechanism enables course correction when fundamental premises prove false, but also creates governance volatility if evidence quality is poor or markets are thin.
-
-## Related Claims
-
-[[futarchy-governed-liquidation-is-the-enforcement-mechanism-that-makes-unruggable-ICOs-credible-because-investors-can-force-full-treasury-return-when-teams-materially-misrepresent.md]]
-[[decision-markets-make-majority-theft-unprofitable-through-conditional-token-arbitrage.md]]

domains/internet-finance/futarchy-daos-require-mintable-governance-tokens-because-fixed-supply-treasuries-exhaust-without-issuance-authority-forcing-disruptive-token-architecture-migrations.md

@@ -1,50 +0,0 @@
----
-type: claim
-domain: internet-finance
-description: "MetaDAO's METAC became unfit for purpose when its treasury exhausted and mint authority was absent, requiring a full 1:1000 token split and DAO version migration — revealing a structural failure mode for fixed-supply governance tokens"
-confidence: experimental
-source: "rio, based on MetaDAO Migrate META Token proposal (Aug 2025) by Proph3t and Kollan"
-created: 2026-03-11
-depends_on:
-  - "MetaDAO Migrate META Token proposal (Proposal 15, completed 2025-08-10)"
-  - "METAC supply ~20K unmintable, treasury exhausted"
-  - "META supply ~20M mintable, DAO v0.5 Squads migration"
-challenged_by: []
----
-
-# Futarchy DAOs require mintable governance tokens because fixed-supply treasuries exhaust without issuance authority forcing disruptive token architecture migrations
-
-MetaDAO's METAC token illustrates the failure mode. METAC was unmintable: once the DAO treasury depleted, there was no mechanism to fund ongoing governance operations, incentivize participation, or respond to changing governance outcomes. The only exit was emergency migration — a 1:1000 token split, new mint authority under a Squads vault, and a complete DAO version upgrade (v0.3 → v0.5). A migration that could have caused holder confusion, trust erosion, and liquidity fragmentation during conversion.
-
-The authors' stated principle captures the mechanism: "Futarchy is market-driven decision making. To stay true to that principle, it also requires market-driven issuance." This is not merely practical — it's structural. A futarchy DAO governed by a fixed-supply token is relying on treasury reserves to fund itself indefinitely. When those reserves exhaust, the DAO cannot sell tokens (unmintable), cannot dilute to raise capital (no authority), and cannot fund the proposals that constitute governance. Fixed supply turns treasury exhaustion into organizational death rather than a solvable funding problem.
-
-The migration specifications reveal the scale of disruption: supply expanded from 20,863.129001238 METAC to 20,863,129.001238 META (1000x), price reset from ~$798.75 to ~$0.79 per token, fee tier dropped from 4% to 0.5% protocol-owned liquidity, and the DAO required a new on-chain program (`auToUr3CQza3D4qreT6Std2MTomfzvrEeCC5qh7ivW5`). A permanent migration contract (`gr8tqq2ripsM6N46gLWpSDXtdrH6J9jaXoyya1ELC9t`) was deployed to let METAC holders convert at any time — ongoing operational complexity that minting authority would have avoided.
-
-The 1:1000 split also addressed unit bias — a separate but compounding problem. At $799 per METAC, the token psychologically repelled the retail traders and arbitrageurs that futarchy markets depend on for price discovery. Mintable tokens let organizations reset price levels proactively without forcing emergency migrations. Since [[futarchy adoption faces friction from token price psychology proposal complexity and liquidity requirements]], having mint and split authority is part of the toolkit for addressing participation barriers before they compound into organizational crises.
-
-The new DAO parameters formalize the lesson: 120k USDC monthly spending limit (with expected burn ~$80k), mint and update authority held by DAO-controlled Squads vault, and a passing threshold of 1.5%. The spending limit operationalizes runway management that fixed-supply tokens make impossible — you cannot plan burn rates when you have no issuance lever.
-
-## Evidence
-
-- MetaDAO Migrate META Token proposal (Proposal 15, 2025-08-07, completed 2025-08-10) — direct case study of treasury exhaustion requiring token architecture migration
-- Supply specifications: METAC 20,863.129001238 unmintable → META 20,863,129.001238 mintable at 1:1000
-- Author statement: "A mintable token is essential to fund the organization, incentivize participation, and adapt to changing governance outcomes"
-- Migration contract deployed permanently: program `gr8tqq2ripsM6N46gLWpSDXtdrH6J9jaXoyya1ELC9t`
-- New DAO spending limit: 120k USDC/month, expected burn ~$80k
-
-## Challenges
-
-- One case study (MetaDAO) may reflect team execution failure (allowing treasury to exhaust) rather than structural necessity — a well-managed fixed-supply DAO could theoretically sustain itself on protocol fee revenue
-- Mintable tokens introduce dilution risk that fixed-supply tokens avoid: if mint authority is misused, token holders face value extraction without recourse
-- Since [[futarchy is manipulation-resistant because attack attempts create profitable opportunities for defenders]], minting decisions are themselves governable through futarchy — but this only works if the DAO has not already become inoperable from treasury exhaustion
-
----
-
-Relevant Notes:
-- [[futarchy adoption faces friction from token price psychology proposal complexity and liquidity requirements]] — unit bias was a compounding problem that mintability and token splits address
-- [[futarchy-governed DAOs converge on traditional corporate governance scaffolding for treasury operations because market mechanisms alone cannot provide operational security and legal compliance]] — Squads vault adoption in META migration is another data point for this convergence
-- [[ownership coin treasuries should be actively managed through buybacks and token sales as continuous capital calibration not treated as static war chests]] — active treasury management presupposes mint authority exists; fixed-supply tokens make this framework impossible
-- [[MetaDAOs Autocrat program implements futarchy through conditional token markets where proposals create parallel pass and fail universes settled by time-weighted average price over a three-day window]] — migration to v0.5 extends this claim with new program addresses
-
-Topics:
-- [[internet finance and decision markets]]

entities/internet-finance/drift.md

@@ -1,58 +0,0 @@
----
-type: entity
-entity_type: company
-name: "Drift Protocol"
-domain: internet-finance
-handles: ["@DriftProtocol"]
-website: https://drift.trade
-status: active
-tracked_by: rio
-created: 2026-03-11
-last_updated: 2026-03-11
-category: "Perpetuals DEX / DeFi protocol (Solana)"
-stage: growth
-key_metrics:
-  futarchy_proposals: "6+ proposals on MetaDAO platform (grants, working group, AI agents, competitions)"
-  drift_allocated: "150,000+ DRIFT allocated through futarchy governance"
-built_on: ["Solana"]
-competitors: ["[[omnipair]]"]
-tags: ["perps", "solana", "futarchy-adopter", "metadao-ecosystem"]
----
-
-# Drift Protocol
-
-## Overview
-Perpetuals DEX on Solana — one of the largest decentralized derivatives platforms. Significant to the MetaDAO ecosystem for two reasons: (1) Drift adopted futarchy governance through MetaDAO's platform, making it the highest-profile external organization to use futarchic decision-making, and (2) Drift represents the future competitive threat to OmniPair's leverage monopoly on MetaDAO ecosystem tokens.
-
-## Current State
-- **Futarchy adoption**: Drift has run 6+ governance proposals through MetaDAO's futarchy platform since May 2024, allocating 150,000+ DRIFT tokens through futarchic decisions. This includes the Drift Foundation Grant Program (100K DRIFT), "Welcome the Futarchs" retroactive rewards (50K DRIFT), Drift AI Agents grants program (50K DRIFT), Drift Working Group funding, and SuperTeam Earn creator competitions.
-- **AI Agents program**: Drift allocated 50,000 DRIFT for an AI Agents Grants program (Dec 2024) covering trading agents, yield agents, information agents, and social agents. Early signal of DeFi protocols investing in agentic infrastructure.
-- **Leverage competitor**: Currently, OmniPair is the "only game in town" for leverage on MetaDAO ecosystem tokens. However, if MetaDAO reaches ~$1B valuation, Drift and other perp protocols will likely list META and ecosystem tokens — eroding OmniPair's temporary moat.
-- **Perps aggregation**: Ranger Finance aggregated Drift (among others) before its liquidation.
-
-## Timeline
-- **2024-05-30** — First futarchy proposal: "Welcome the Futarchs" — 50K DRIFT to incentivize futarchy participation
-- **2024-07-09** — Drift Foundation Grant Program initialized via futarchy (100K DRIFT)
-- **2024-08-27** — SuperTeam Earn creator competition funded via futarchy
-- **2024-12-19** — AI Agents Grants program: 50K DRIFT for trading, yield, info, and social agents
-- **2025-02-13** — Drift Working Group funded via futarchy
-
-## Competitive Position
-- **Futarchy validation**: Drift using MetaDAO's governance system is the strongest external validation signal — a major protocol choosing futarchy over traditional token voting for real treasury decisions.
-- **Future leverage threat**: Drift listing META perps would directly compete with OmniPair for leverage demand. This is OmniPair's identified "key vulnerability" — the moat is temporary.
-- **Scale differential**: Drift operates at much larger scale than the MetaDAO ecosystem. Its adoption of futarchy is disproportionately significant as a credibility signal.
-
-## Relationship to KB
-- [[futarchy implementations must simplify theoretical mechanisms for production adoption because original designs include impractical elements that academics tolerate but users reject]] — Drift's adoption validates that simplified futarchy works for real organizations
-- [[permissionless leverage on metaDAO ecosystem tokens catalyzes trading volume and price discovery that strengthens governance by making futarchy markets more liquid]] — Drift is the future competitor that erodes OmniPair's leverage monopoly
-- [[governance mechanism diversity compounds organizational learning because disagreement between mechanisms reveals information no single mechanism can produce]] — Drift running both traditional governance and futarchy provides comparative data
-
----
-
-Relevant Entities:
-- [[metadao]] — futarchy platform provider
-- [[omnipair]] — current leverage competitor (OmniPair holds temporary monopoly)
-- [[ranger-finance]] — former aggregation client (liquidated)
-
-Topics:
-- [[internet finance and decision markets]]

entities/internet-finance/jupiter.md

@@ -1,50 +0,0 @@
----
-type: entity
-entity_type: company
-name: "Jupiter"
-domain: internet-finance
-handles: ["@JupiterExchange"]
-website: https://jup.ag
-status: active
-tracked_by: rio
-created: 2026-03-11
-last_updated: 2026-03-11
-category: "DEX aggregator / DeFi hub (Solana)"
-stage: mature
-key_metrics:
-  role_in_ecosystem: "Primary aggregator for MetaDAO ecosystem token routing"
-  omnipair_catalyst: "Jupiter SDK integration expected to ~3x OmniPair volume"
-built_on: ["Solana"]
-tags: ["DEX-aggregator", "solana", "infrastructure", "metadao-adjacent"]
----
-
-# Jupiter
-
-## Overview
-The dominant DEX aggregator on Solana — routes trades across all Solana AMMs to find optimal execution. Critical infrastructure for the MetaDAO ecosystem: Jupiter integration determines whether ecosystem tokens are tradeable by the broader Solana market. The Jupiter team forked OmniPair's SDK (as of ~March 2026) to enable direct routing through OmniPair pools, making this integration the single highest-impact catalyst for OmniPair's volume growth.
-
-## Current State
-- **Aggregator role**: Routes trades across Raydium, Meteora, OmniPair, and other Solana AMMs. Being listed on Jupiter is effectively a prerequisite for meaningful trading volume on Solana.
-- **OmniPair integration**: Jupiter team forked OmniPair's SDK (~March 2026). Integration expected to roughly triple OmniPair volume and close most of the APY gap with Raydium. This is the single highest-impact near-term catalyst for the MetaDAO ecosystem's DeFi infrastructure.
-- **Ranger Finance**: Ranger's perps aggregation product aggregated Jupiter (among others) before its liquidation.
-- **Ecosystem significance**: Jupiter is not a MetaDAO ecosystem project — it's Solana-wide infrastructure. But its routing decisions determine liquidity accessibility for every MetaDAO token.
-
-## Competitive Position
-- **Dominant position**: The default swap interface for Solana users. Near-monopoly on DEX aggregation.
-- **Infrastructure dependency**: MetaDAO ecosystem tokens that aren't routed through Jupiter have severely limited discoverability and volume. OmniPair's DexScreener visibility issue (~10% of liquidity displayed) compounds this — Jupiter routing partially compensates.
-- **Not a direct competitor**: Jupiter aggregates, not competes with, MetaDAO ecosystem AMMs. The relationship is symbiotic — more AMMs with unique pools give Jupiter more routing options.
-
-## Relationship to KB
-- [[permissionless leverage on metaDAO ecosystem tokens catalyzes trading volume and price discovery that strengthens governance by making futarchy markets more liquid]] — Jupiter routing is the primary channel through which broader Solana liquidity reaches MetaDAO ecosystem tokens
-- [[MetaDAO is the futarchy launchpad on Solana where projects raise capital through unruggable ICOs governed by conditional markets creating the first platform for ownership coins at scale]] — Jupiter integration is infrastructure-level validation for the MetaDAO ecosystem
-
----
-
-Relevant Entities:
-- [[omnipair]] — SDK integration (highest-impact catalyst)
-- [[meteora]] — routed AMM
-- [[raydium]] — routed AMM
-- [[ranger-finance]] — former aggregation client (liquidated)
-
-Topics:
-- [[internet finance and decision markets]]

entities/internet-finance/meteora.md

@@ -1,59 +0,0 @@
----
-type: entity
-entity_type: company
-name: "Meteora"
-domain: internet-finance
-handles: ["@MeteoraAG"]
-website: https://meteora.ag
-status: active
-tracked_by: rio
-created: 2026-03-11
-last_updated: 2026-03-11
-category: "Liquidity protocol / AMM (Solana)"
-stage: growth
-key_metrics:
-  metadao_revenue_share: "46% of MetaDAO Q4 2025 revenue ($1.15M) from Meteora LP positions"
-  standard_allocation: "900K tokens per Futardio launch placed in Meteora pool"
-competitors: ["[[raydium]]", "[[omnipair]]"]
-built_on: ["Solana"]
-tags: ["AMM", "DLMM", "liquidity", "solana", "metadao-infrastructure"]
----
-
-# Meteora
-
-## Overview
-Solana liquidity protocol offering Dynamic Liquidity Market Maker (DLMM) pools, concentrated liquidity, and dynamic bonding pools. Critical infrastructure for the MetaDAO ecosystem — every Futardio launch allocates 900K tokens to a Meteora pool as part of the standard token issuance template, and Meteora LP positions generated 46% of MetaDAO's $2.51M Q4 2025 revenue.
-
-## Current State
-- **Role in MetaDAO ecosystem**: Default secondary liquidity venue. Standard Futardio launch template: 10M token base issuance + 2M Futarchic AMM + 900K Meteora + performance package. Meteora provides the non-futarchic liquidity layer.
-- **Revenue generation**: MetaDAO earned $1.15M from Meteora LP positions in Q4 2025 (46% of total $2.51M revenue). The remaining 54% came from the Futarchic AMM.
-- **Protocol-owned liquidity**: MetaDAO maintains protocol-owned liquidity on Meteora (e.g., META-USDC pool). The META token migration proposal (Aug 2025) included withdrawing protocol-owned liquidity from Meteora as a migration step.
-- **Dynamic Bonding Pools**: Used by projects like Phonon Studio AI for tokenized AI artist trading — Meteora DBC Pools enable token launches tied to dynamic bonding curves.
-- **DLMM**: Concentrated liquidity pools used by Paystream and other DeFi protocols for routing strategies.
-
-## Timeline
-- **2024-02** — MetaDAO executes Dutch auction on OpenBook, pairs USDC with META for Meteora LP (first formal META liquidity on Meteora)
-- **2024-02** — $100K OTC trade with Ben Hawkins includes creating 50/50 Meteora LP 1% Volatile Pool META-USDC
-- **2025-Q4** — Meteora LP generates $1.15M in fees for MetaDAO (Pine Analytics Q4 report)
-- **2025-10 to 2026-03** — Every Futardio launch allocates 900K tokens to Meteora pool as standard template
-
-## Competitive Position
-- **Infrastructure role**: Not competing with MetaDAO — provides complementary liquidity infrastructure. Meteora is the LP venue; Futarchic AMM is the governance venue.
-- **vs Raydium**: Both are major Solana AMMs. Raydium offers CLMM (concentrated liquidity). Meteora differentiates with DLMM and dynamic bonding pools.
-- **vs OmniPair**: OmniPair combines AMM + lending (leverage). Meteora is pure liquidity provision — different use case but competes for LP capital on the same token pairs.
-- **Structural advantage**: Deep integration with MetaDAO ecosystem through standard launch template creates reliable flow of new token pairs.
-
-## Relationship to KB
-- [[MetaDAO is the futarchy launchpad on Solana where projects raise capital through unruggable ICOs governed by conditional markets creating the first platform for ownership coins at scale]] — Meteora provides the secondary liquidity layer for every MetaDAO launch
-- [[permissionless leverage on metaDAO ecosystem tokens catalyzes trading volume and price discovery that strengthens governance by making futarchy markets more liquid]] — Meteora pools are one venue where this liquidity lives
-
----
-
-Relevant Entities:
-- [[metadao]] — ecosystem partner, revenue source
-- [[omnipair]] — competing for LP capital
-- [[raydium]] — AMM competitor on Solana
-- [[futardio]] — launch template integration
-
-Topics:
-- [[internet finance and decision markets]]

entities/internet-finance/nallok.md

@@ -1,50 +0,0 @@
----
-type: entity
-entity_type: person
-name: "Nallok"
-domain: internet-finance
-handles: ["@metanallok"]
-status: active
-tracked_by: rio
-created: 2026-03-11
-last_updated: 2026-03-11
-role: "Co-founder & Operator, MetaDAO"
-organizations: ["[[metadao]]", "[[futardio]]"]
-known_positions:
-  - "Futarchy requires mechanism simplification for production adoption — Robin Hanson's original designs include impractical elements"
-  - "Futarchy as a Service (FaaS) is the scaling path for futarchy governance"
-tags: ["futarchy", "mechanism-design", "solana", "metadao-ecosystem"]
----
-
-# Nallok
-
-## Overview
-Co-founder and primary operator of MetaDAO. Legal name Kollan House. Serves as the key operational figure behind MetaDAO LLC (Republic of the Marshall Islands DAO LLC, 852 Lagoon Rd, Majuro, MH 96960) and sole Director of the Futarchy Governance SPC (Cayman Islands). While Proph3t is the public face and mechanism architect, Nallok handles legal structure, business development, treasury operations, and ecosystem coordination.
-
-## Significance
-- **Legal infrastructure**: Built MetaDAO's legal wrapper — the RMI DAO LLC + Cayman SPC structure that addresses the Ooki DAO precedent (DAOs without legal wrappers face general partnership liability)
-- **Futarchy as a Service (FaaS)**: Proposed and led development of FaaS (March 2024) — the concept that futarchy governance can be offered as infrastructure to other DAOs, not just MetaDAO
-- **Mechanism pragmatism**: Noted that Robin Hanson wanted random proposal outcomes — "impractical for production." This insight drove MetaDAO's simplification of futarchy theory into deployable mechanism design
-- **Treasury operations**: Co-manages multi-sig for MetaDAO treasury. Involved in OTC trades, liquidity management, and compensation proposals
-- **Compensation structure**: Nallok and Proph3t share a performance-based package (2% of supply per $1B FDV increase, up to 10% at $5B) — itself a statement about incentive alignment through futarchic governance
-
-## Key Contributions to KB
-- Primary source for futarchy mechanism simplification claims — the gap between Hanson's theory and production reality
-- Operational knowledge of MetaDAO's legal structure (RMI DAO LLC, Cayman SPC)
-- FaaS proposal history — the scaling thesis for futarchy governance
-- Contact: kollan@metadao.fi
-
-## Relationship to KB
-- [[futarchy implementations must simplify theoretical mechanisms for production adoption because original designs include impractical elements that academics tolerate but users reject]] — Nallok's direct observation about Hanson's impractical proposals
-- [[Ooki DAO proved that DAOs without legal wrappers face general partnership liability making entity structure a prerequisite for any futarchy-governed vehicle]] — Nallok built the legal structure that addresses this
-- [[futarchy-governed entities are structurally not securities because prediction market participation replaces the concentrated promoter effort that the Howey test requires]] — Nallok engaged legal counsel to investigate this question
-
----
-
-Relevant Entities:
-- [[metadao]] — co-founded
-- [[futardio]] — operates
-- [[proph3t]] — co-founder
-
-Topics:
-- [[internet finance and decision markets]]

entities/internet-finance/raydium.md

@@ -1,46 +0,0 @@
----
-type: entity
-entity_type: company
-name: "Raydium"
-domain: internet-finance
-handles: ["@RaydiumProtocol"]
-website: https://raydium.io
-status: active
-tracked_by: rio
-created: 2026-03-11
-last_updated: 2026-03-11
-category: "AMM / DEX (Solana)"
-stage: mature
-built_on: ["Solana"]
-competitors: ["[[meteora]]", "[[omnipair]]"]
-tags: ["AMM", "CLMM", "solana", "metadao-adjacent"]
----
-
-# Raydium
-
-## Overview
-One of the two dominant AMMs on Solana (alongside Meteora). Offers concentrated liquidity market maker (CLMM) pools. Referenced throughout the MetaDAO ecosystem as the primary benchmark for AMM yield and volume — OmniPair's competitive thesis is explicitly framed as "must yield more than Raydium for equivalent pools" once Jupiter aggregator integration is live.
-
-## Current State
-- **Competitive benchmark**: OmniPair founder Rakka argues mathematically that OmniPair (same AMM + aggregator integration + borrow rate surplus) must yield more than Raydium for equivalent pools. This is the core competitive claim for OmniPair's value proposition.
-- **CLMM pools**: Used by DeFi protocols like Paystream for automated LP strategies across Raydium CLMM, Meteora DLMM, and DAMM v2 pools.
-- **Liquidity farming**: MetaDAO's FUTURE token had Raydium liquidity farming initiated via futarchy proposal (Nov 2024).
-- **Volume reference**: Jupiter aggregates Raydium pools. OmniPair's expected ~3x volume increase from Jupiter integration is benchmarked against closing "the APY gap with Raydium."
-
-## Competitive Position
-- **Established incumbent**: Raydium has deep liquidity across Solana token pairs. New AMMs like OmniPair compete for the same LP capital.
-- **vs OmniPair**: OmniPair differentiates by combining AMM + lending (leverage) in the same pool. Raydium is pure AMM — no lending, no leverage. For MetaDAO ecosystem tokens specifically, OmniPair offers a unique value proposition (leverage for futarchy bets). For general Solana trading, Raydium's deeper liquidity dominates.
-- **vs Meteora**: Both are major Solana AMMs. Raydium's CLMM competes with Meteora's DLMM for concentrated liquidity provision.
-
-## Relationship to KB
-- [[permissionless leverage on metaDAO ecosystem tokens catalyzes trading volume and price discovery that strengthens governance by making futarchy markets more liquid]] — Raydium is the benchmark OmniPair must beat to attract LP capital away from established pools
-
----
-
-Relevant Entities:
-- [[omnipair]] — competitor (OmniPair claims superior yield through AMM+lending combination)
-- [[meteora]] — AMM competitor on Solana
-- [[jupiter]] — aggregates Raydium pools
-
-Topics:
-- [[internet finance and decision markets]]

entities/internet-finance/theia-research.md

@@ -1,68 +0,0 @@
----
-type: entity
-entity_type: company
-name: "Theia Research"
-domain: internet-finance
-handles: ["@TheiaResearch"]
-status: active
-tracked_by: rio
-created: 2026-03-11
-last_updated: 2026-03-11
-founded: 2024-01-01
-category: "Onchain liquid token fund"
-stage: growth
-key_metrics:
-  metadao_otc_total: "$1.63M across 3 OTC trades (Jan 2025: $500K, Jul 2025: $630K, Jan 2025: $500K)"
-  meta_tokens_held: "1,070+ META tokens via OTC"
-  investment_approach: "Kelly Criterion at 20% of full Kelly, Bayesian updating"
-competitors: []
-built_on: ["Solana", "Ethereum"]
-tags: ["institutional-investor", "metadao-ecosystem", "internet-finance-thesis", "token-governance"]
----
-
-# Theia Research
-
-## Overview
-Onchain liquid token fund managed by Felipe Montealegre. Invests in companies building the "Internet Financial System" — taking large positions in small-cap tokens through structured OTC deals with 2-4 year investment horizons. The most significant institutional investor in the MetaDAO ecosystem, holding 1,070+ META tokens acquired at premiums to market price. Coined the "Token Problem" framework (lemon market dynamics in token markets) and published the Token Transparency Framework with Blockworks.
-
-## Current State
-- **Fund structure**: Theia Blockchain Partners Master Fund LP
-- **Investment thesis**: Internet Financial System replacing permissioned, siloed traditional finance. Five advantages: free capital flows, improved property rights, financial accessibility, operational efficiency, faster GDP growth.
-- **MetaDAO position**: Largest known institutional holder. Holds MetaDAO specifically for "prioritizing investors over teams" — the competitive moat that futarchy creates. Three OTC trades totaling $1.63M, all at premiums to spot.
-- **AI integration**: Uses LLMs as "backbone of process improvements." Internal dashboards consolidating Discord, Notion, GitHub. Planning "AI agents that can perform discrete tasks" for competitive analysis.
-- **Research output**: Published "The Investment Manager of the Future" (Feb 2026), arguing LLMs shift investment from economies of scale to economies of edge. 292 bookmarks — most saved piece in its batch. Also published internet finance thesis with 50-100bps GDP growth projection.
-
-## Timeline
-- **2025-01-03** — First MetaDAO OTC trade: $500K for META tokens
-- **2025-01-07** — Published internet finance thesis (IFS as better financial system for 8B people)
-- **2025-01-27** — Second OTC trade: $500K for 370 META at $1,350/token
-- **2025-07-21** — Third OTC trade: $630K for 700 META at $900/token (38% premium to spot). Funds used to extend MetaDAO runway + legal advisory.
-- **2026-02-12** — Published 2025 Annual Letter. Five-phase investment loop: moat analysis → multiples → prediction → Kelly sizing → Bayesian updating. Noah Goldberg promoted to equity partner, Thomas Bautista hired.
-- **2026-02-17** — Published "The Investment Manager of the Future." LLMs invert 80/20 ratio of execution vs analysis.
-
-## Competitive Position
-- **Unique positioning**: Only known institutional fund explicitly building investment thesis around futarchy governance as a moat
-- **Token governance focus**: Launched Token Transparency Framework with Blockworks. Describes "Lemon Problem in Token Markets" — the structural issue of quality tokens being indistinguishable from scams
-- **Strategic value to MetaDAO**: OTC trades funded legal/regulatory review, extending ecosystem credibility beyond pure speculation
-- **Economies of edge thesis**: Argues 5 high-agency analysts with LLMs replace 100 junior staff — structural case for why small, domain-expert investment entities (Living Agents) become viable
-
-## Investment Thesis
-Theia validates the Living Capital model — a sophisticated institutional investor using rigorous frameworks (Kelly Criterion, Bayesian updating, Helmer's 7 Powers) to allocate into futarchy-governed tokens. Their "economies of edge" thesis is the structural argument for why Living Capital vehicles work now: LLMs collapse the 80% execution overhead that forced funds to accumulate AUM. If Theia demonstrates persistent alpha from this approach, it becomes the reference case for agentic investment management.
-
-**Thesis status:** TRACKING (not an investment target — a validation signal for the Living Capital model)
-
-## Relationship to KB
-- [[LLMs shift investment management from economies of scale to economies of edge because AI collapses the analyst labor cost that forced funds to accumulate AUM rather than generate alpha]] — Theia's core contribution to the KB
-- [[internet finance generates 50 to 100 basis points of additional annual GDP growth by unlocking capital allocation to previously inaccessible assets and eliminating intermediation friction]] — Theia's macro thesis
-- [[publishing investment analysis openly before raising capital inverts hedge fund secrecy because transparency attracts domain-expert LPs who can independently verify the thesis]] — Theia exemplifies this model
-- [[futarchy-governed entities are structurally not securities because prediction market participation replaces the concentrated promoter effort that the Howey test requires]] — Theia funded MetaDAO's legal advisory to investigate this question
-
----
-
-Relevant Entities:
-- [[metadao]] — largest institutional investor
-- [[proph3t]] — founder of MetaDAO, primary counterparty
-- [[nallok]] — MetaDAO operator, OTC trade counterparty
-
-Topics:
-- [[internet finance and decision markets]]

inbox/archive/2011-00-00-mcwilliams-economic-history-medicare-part-c.md

@@ -8,7 +8,6 @@ domain: health
 secondary_domains: []
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: high
 tags: [medicare-advantage, medicare-history, political-economy, risk-adjustment, payment-formula, hmo]
 processed_by: vida

inbox/archive/2015-03-00-friston-active-inference-epistemic-value.md

@@ -8,7 +8,6 @@ domain: ai-alignment
 secondary_domains: [collective-intelligence, critical-systems]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: high
 tags: [active-inference, epistemic-value, information-gain, exploration-exploitation, expected-free-energy, curiosity, epistemic-foraging]
 processed_by: theseus

inbox/archive/2019-00-00-sciencedirect-superorganism-ecological-economics.md

@@ -7,7 +7,6 @@ date: 2019-01-01
 domain: ai-alignment
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 tags: [superorganism, ecological-economics, academic-paper]
 linked_set: superorganism-sources-mar2026
 notes: "Paywalled academic paper on ScienceDirect. Crawl4AI returned only 1.5K chars of header/navigation. Content not accessible without institutional access. Consider accessing via Sci-Hub or requesting from author."

inbox/archive/2019-02-00-ramstead-multiscale-integration.md

@@ -8,7 +8,6 @@ domain: critical-systems
 secondary_domains: [collective-intelligence, ai-alignment]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: low
 tags: [active-inference, multi-scale, markov-blankets, cognitive-boundaries, free-energy-principle, internalism-externalism]
 processed_by: theseus

inbox/archive/2020-00-00-greattransition-humanity-as-superorganism.md

@@ -7,7 +7,6 @@ date: 2020-01-01
 domain: ai-alignment
 format: essay
 status: null-result
-last_attempted: 2026-03-11
 tags: [superorganism, collective-intelligence, great-transition, emergence, systems-theory]
 linked_set: superorganism-sources-mar2026
 processed_by: theseus

inbox/archive/2020-03-00-vasil-world-unto-itself-communication-active-inference.md

@@ -8,7 +8,6 @@ domain: collective-intelligence
 secondary_domains: [ai-alignment, cultural-dynamics]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: high
 tags: [active-inference, communication, shared-generative-models, hermeneutic-niche, cooperative-communication, epistemic-niche-construction]
 processed_by: theseus

inbox/archive/2021-03-00-sajid-active-inference-demystified-compared.md

@@ -8,7 +8,6 @@ domain: ai-alignment
 secondary_domains: [collective-intelligence, critical-systems]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: medium
 tags: [active-inference, reinforcement-learning, expected-free-energy, epistemic-value, exploration-exploitation, comparison]
 processed_by: theseus

inbox/archive/2022-00-00-americanscientist-superorganism-revolution.md

@@ -7,7 +7,6 @@ date: 2022-01-01
 domain: ai-alignment
 format: essay
 status: null-result
-last_attempted: 2026-03-11
 tags: [superorganism, collective-intelligence, biology, emergence, evolution]
 linked_set: superorganism-sources-mar2026
 processed_by: theseus

inbox/archive/2023-10-00-anthropic-collective-constitutional-ai.md

@@ -8,7 +8,6 @@ domain: ai-alignment
 secondary_domains: [collective-intelligence]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: medium
 tags: [collective-constitutional-ai, polis, democratic-alignment, public-input, constitution-design]
 processed_by: theseus

inbox/archive/2023-11-18-futardio-proposal-develop-a-lst-vote-market.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/9RisXkQCFLt7NA29vt5aWatcnU8SkyBgS95HxXhwXhW
 date: 2023-11-18
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2023-12-03-futardio-proposal-migrate-autocrat-program-to-v01.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/AkLsnieYpCU2UsSqUNrbMrQNi9bvdnjxx75mZbJns9z
 date: 2023-12-03
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2023-12-16-futardio-proposal-develop-a-saber-vote-market.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/GPT8dFcpHfssMuULYKT9qERPY3heMoxwZHxgKgPw3TY
 date: 2023-12-16
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-00-00-shermer-humanity-superorganism.md

@@ -7,7 +7,6 @@ date: 2024-01-01
 domain: ai-alignment
 format: essay
 status: null-result
-last_attempted: 2026-03-11
 tags: [superorganism, collective-intelligence, skepticism, shermer, emergence]
 linked_set: superorganism-sources-mar2026
 processed_by: theseus

inbox/archive/2024-00-00-warden-community-notes-bridging-algorithm.md

@@ -8,7 +8,6 @@ domain: ai-alignment
 secondary_domains: [mechanisms, collective-intelligence]
 format: report
 status: null-result
-last_attempted: 2026-03-11
 priority: high
 tags: [community-notes, bridging-algorithm, matrix-factorization, polarity-factors, consensus-mechanism]
 flagged_for_rio: ["Community Notes bridging algorithm as mechanism design — matrix factorization for consensus is novel governance mechanism"]

inbox/archive/2024-01-00-friston-designing-ecosystems-intelligence.md

@@ -8,7 +8,6 @@ domain: ai-alignment
 secondary_domains: [collective-intelligence, critical-systems]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: high
 tags: [active-inference, free-energy-principle, multi-agent, collective-intelligence, shared-intelligence, ecosystems-of-intelligence]
 processed_by: theseus

inbox/archive/2024-01-00-friston-federated-inference-belief-sharing.md

@@ -8,7 +8,6 @@ domain: collective-intelligence
 secondary_domains: [ai-alignment, critical-systems]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: high
 tags: [active-inference, federated-inference, belief-sharing, multi-agent, distributed-intelligence, collective-intelligence]
 processed_by: theseus

inbox/archive/2024-01-12-futardio-proposal-create-spot-market-for-meta.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/9ABv3Phb44BNF4VFteSi9qcWEyABdnRqkorNuNtzdh2
 date: 2024-01-12
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-01-24-futardio-proposal-develop-amm-program-for-futarchy.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/CF9QUBS251FnNGZHLJ4WbB2CVRi5BtqJbCqMi47NX1P
 date: 2024-01-24
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-02-05-futardio-proposal-execute-creation-of-spot-market-for-meta.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/HyA2h16uPQBFjezKf77wThNGsEoesUjeQf9rFvfAy4t
 date: 2024-02-05
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-02-05-statnews-devoted-health-losses-persist.md

@@ -8,7 +8,6 @@ domain: health
 secondary_domains: []
 format: report
 status: null-result
-last_attempted: 2026-03-11
 priority: medium
 tags: [devoted-health, alignment-healthcare, clover-health, medicare-advantage, startup, purpose-built, technology-platform]
 processed_by: vida

inbox/archive/2024-02-13-futardio-proposal-engage-in-50000-otc-trade-with-ben-hawkins.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/US8j6iLf9GkokZbk89Bo1qnGBees5etv5sEfsfvCoZK
 date: 2024-02-13
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-02-18-futardio-proposal-engage-in-100000-otc-trade-with-ben-hawkins-2.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/E1FJAp8saDU6Da2ccayjLBfA53qbjKRNYvu7QiMAnjQ
 date: 2024-02-18
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-02-18-futardio-proposal-engage-in-50000-otc-trade-with-pantera-capital.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/H59VHchVsy8UVLotZLs7YaFv2FqTH5HAeXc4Y48kxie
 date: 2024-02-18
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-02-20-futardio-proposal-develop-multi-option-proposals.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/J7dWFgSSuMg3BNZBAKYp3AD5D2yuaaLUmyKqvxBZgHh
 date: 2024-02-20
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-02-26-futardio-proposal-increase-meta-liquidity-via-a-dutch-auction.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/Dn638yPirR3e2UNNECpLNJApDhxsjhJTAv9uEd9LBVV
 date: 2024-02-26
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-03-00-mcmillen-levin-collective-intelligence-unifying-concept.md

@@ -8,7 +8,6 @@ domain: collective-intelligence
 secondary_domains: [critical-systems, ai-alignment]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: medium
 tags: [collective-intelligence, multi-scale, diverse-intelligence, biology, morphogenesis, competency-architecture]
 processed_by: theseus

inbox/archive/2024-03-03-futardio-proposal-burn-993-of-meta-in-treasury.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/ELwCkHt1U9VBpUFJ7qGoVMatEwLSr1HYj9q9t8JQ1Nc
 date: 2024-03-03
 domain: internet-finance
 format: data
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 event_type: proposal
 ---

inbox/archive/2024-03-13-futardio-proposal-develop-futarchy-as-a-service-faas.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/D9pGGmG2rCJ5BXzbDoct7EcQL6F6A57azqYHdpWJL9C
 date: 2024-03-13
 domain: internet-finance
 format: data
-status: entity-data
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 ---

inbox/archive/2024-03-19-futardio-proposal-engage-in-250000-otc-trade-with-colosseum.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/5qEyKCVyJZMFZSb3yxh6rQjqDYxASiLW7vFuuUTCYnb
 date: 2024-03-19
 domain: internet-finance
 format: data
-status: entity-data
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 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-03-26-futardio-proposal-appoint-nallok-and-proph3t-benevolent-dictators-for-three-mo.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/BqMrwwZYdpbXNsfpcxxG2DyiQ7uuKB69PznPWZ33GrZ
 date: 2024-03-26
 domain: internet-finance
 format: data
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 tags: [futardio, metadao, futarchy, solana, governance]
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 ---

inbox/archive/2024-03-28-futardio-proposal-migrate-autocrat-program-to-v02.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/HXohDRKtDcXNKnWysjyjK8S5SvBe76J5o4NdcF4jj96
 date: 2024-03-28
 domain: internet-finance
 format: data
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 ---

inbox/archive/2024-05-27-futardio-proposal-approve-performance-based-compensation-package-for-proph3t-a.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/BgHv9GutbnsXZLZQHqPL8BbGWwtcaRDWx82aeRMNmJb
 date: 2024-05-27
 domain: internet-finance
 format: data
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inbox/archive/2024-05-27-futardio-proposal-proposal-1.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/iPzWdGBZiHMT5YhR2m4WtTNbFW3KgExH2dRAsgWydPf
 date: 2024-05-27
 domain: internet-finance
 format: data
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 tags: [futardio, metadao, futarchy, solana, governance]
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 processed_by: rio

inbox/archive/2024-05-30-futardio-proposal-drift-futarchy-proposal-welcome-the-futarchs.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/9jAnAupCdPQCFvuAMr5ZkmxDdEKqsneurgvUnx7Az9z
 date: 2024-05-30
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-05-30-futardio-proposal-proposal-1.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/8AEsxyN8jhth5WQZHjU9kS3JcRHaUmpck7qZgpv2v4w
 date: 2024-05-30
 domain: internet-finance
 format: data
-status: entity-data
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 tags: [futardio, metadao, futarchy, solana, governance]
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inbox/archive/2024-06-05-futardio-proposal-fund-futuredaos-token-migrator.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/BMZbX7z2zgLuq266yskeHF5BFZoaX9j3tvsZfVQ7RUY
 date: 2024-06-05
 domain: internet-finance
 format: data
-status: entity-data
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 tags: [futardio, metadao, futarchy, solana, governance]
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 ---

inbox/archive/2024-06-08-futardio-proposal-reward-the-university-of-waterloo-blockchain-club-with-1-mil.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/7KkoRGyvzhvzKjxuPHjyxg77a52MeP6axyx7aywpGbd
 date: 2024-06-08
 domain: internet-finance
 format: data
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inbox/archive/2024-06-14-futardio-proposal-fund-the-rug-bounty-program.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/4ztwWkz9TD5Ni9Ze6XEEj6qrPBhzdTQMfpXzZ6A8bGz
 date: 2024-06-14
 domain: internet-finance
 format: data
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 tags: [futardio, metadao, futarchy, solana, governance]
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 ---

inbox/archive/2024-06-22-futardio-proposal-thailanddao-event-promotion-to-boost-deans-list-dao-engageme.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/DgXa6gy7nAFFWe8VDkiReQYhqe1JSYQCJWUBV8Mm6aM
 date: 2024-06-22
 domain: internet-finance
 format: data
-status: entity-data
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inbox/archive/2024-06-26-futardio-proposal-approve-metadao-fundraise-2.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/9BMRY1HBe61MJoKEd9AAW5iNQyws2vGK6vuL49oR3Az
 date: 2024-06-26
 domain: internet-finance
 format: data
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 ---

inbox/archive/2024-07-01-futardio-proposal-fund-artemis-labs-data-and-analytics-dashboards.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/G95shxDXSSTcgi2DTJ2h79JCefVNQPm8dFeDzx7qZ2k
 date: 2024-07-01
 domain: internet-finance
 format: data
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 tags: [futardio, metadao, futarchy, solana, governance]
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 ---

inbox/archive/2024-07-01-futardio-proposal-proposal-1.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/Hda19mrjPxotZnnQfpAhJtxWvfC6JCXbMquohThgsd5
 date: 2024-07-01
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-07-01-futardio-proposal-test.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/16ZyAyNumkJoU9GATreUzBDzfS6rmEpZnUcQTcdfJiD
 date: 2024-07-01
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-07-04-futardio-proposal-proposal-3.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/EXehk1u3qUJZSxJ4X3nHsiTocRhzwq3eQAa6WKxeJ8X
 date: 2024-07-04
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-07-09-futardio-proposal-initialize-the-drift-foundation-grant-program.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/xU6tQoDh3Py4MfAY3YPwKnNLt7zYDiNHv8nA1qKnxVM
 date: 2024-07-09
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-07-18-futardio-proposal-approve-budget-for-champions-nft-collection-design.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/BU8kQ7ECq8CJ9BHUZfYsjHFKPMGsF6oJn5d6b1tArdw
 date: 2024-07-18
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-07-18-futardio-proposal-enhancing-the-deans-list-dao-economic-model.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/5c2XSWQ9rVPge2Umoz1yenZcAwRaQS5bC4i4w87B1WU
 date: 2024-07-18
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-03-futardio-proposal-approve-q3-roadmap.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/7AbivixQZTrgnqpmyxW2j1dd4Jyy15K3T2T7MEgfg8D
 date: 2024-08-03
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-14-futardio-proposal-develop-memecoin-launchpad.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/J57DcV2yQGiDpSetQHui6Piwjwsbet2ozXVPG77kTvT
 date: 2024-08-14
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-20-futardio-proposal-proposal-4.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/yTiRuoXWQVdVgbUJBU6J3FF1Sxnzy7FW7osqkkfMK6G
 date: 2024-08-20
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-08-20-futardio-proposal-test-proposal-3.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/5TRuK9TLZ9bUPtp6od6pLKN6GxbQMByaBwVSCArNaS1
 date: 2024-08-20
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-08-27-futardio-proposal-fund-the-drift-superteam-earn-creator-competition.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/AKMnVnSC8DzoZJktErtzR2QNt1ESoN8i2DdHPYuQTMG
 date: 2024-08-27
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-28-futardio-proposal-a-very-unique-title-some-say-its-really-unique.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/GugKjNpirFNaaRkEStRKGJPnutptsnTA3XuCJ8nwaVt
 date: 2024-08-28
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-28-futardio-proposal-drift-proposal-for-bet.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/8cnQAxS3WQXhD2eAjKSJ6wmBwaJskRZFYByMPKEhD1o
 date: 2024-08-28
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-28-futardio-proposal-dummy.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/eNPP3Tm4AAyDwq9N4BwJwBzFD14KXDSVY6bhMRaBuFt
 date: 2024-08-28
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-28-futardio-proposal-proposal-7.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/AuNNyR4oU2zkG1sYBzJ3DJmyDzMKSmSW2yASorWenuC
 date: 2024-08-28
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-08-28-futardio-proposal-test-proposal-based-on-metadao-content.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/EmPUGgv2Utzuu2vgSu6GcTRAtJMox5vJeZKi95cBgfJ
 date: 2024-08-28
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-30-futardio-proposal-approve-budget-for-pre-governance-hackathon-development.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/2LKqzegdHrcrrRCHSuTS2fMjjJuZDfzuRKMnzPhzeD4
 date: 2024-08-30
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-08-31-futardio-proposal-enter-services-agreement-with-organization-technology-llc.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/53EDms4zPkp4khbwBT3eXWhMALiMwssg7f5zckq22tH
 date: 2024-08-31
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-09-05-futardio-proposal-my-test-proposal-that-rocksswd.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/evGundfgMRZWCYsGF7GMKcgh6LjxDTFrvWRAhxiQS8h
 date: 2024-09-05
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-10-00-qiu-representative-social-choice-alignment.md

@@ -8,7 +8,6 @@ domain: ai-alignment
 secondary_domains: [collective-intelligence, mechanisms]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: high
 tags: [social-choice, representative-alignment, arrows-theorem, privilege-graphs, learning-theory, generalization]
 flagged_for_rio: ["Social choice mechanisms as prediction market analogues — preference aggregation parallels"]

inbox/archive/2024-10-10-futardio-proposal-treasury-proposal-deans-list-proposal.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/8SwPfzKhaZ2SQfgfJYfeVRTXALZs2qyFj7kX1dEkd29
 date: 2024-10-10
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-10-22-futardio-proposal-hire-advaith-sekharan-as-founding-engineer.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/B82Dw1W6cfngH7BRukAyKXvXzP4T2cDsxwKYfxCftoC
 date: 2024-10-22
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-10-22-futardio-proposal-increase-ore-sol-lp-boost-multiplier-to-6x.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/A19yLRVqxvUf4cTDm6mKNKadasd7YSYDrzk6AYEyubA
 date: 2024-10-22
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-10-30-futardio-proposal-swap-150000-into-isc.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/Gp3ANMRTdGLPNeMGFUrzVFaodouwJSEXHbg5rFUi9ro
 date: 2024-10-30
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-11-00-democracy-levels-framework.md

@@ -8,7 +8,6 @@ domain: ai-alignment
 secondary_domains: [mechanisms, collective-intelligence]
 format: paper
 status: null-result
-last_attempted: 2026-03-11
 priority: medium
 tags: [democratic-AI, governance, framework, levels, pluralistic-alignment, ICML-2025]
 processed_by: theseus

inbox/archive/2024-11-08-futardio-proposal-initiate-liquidity-farming-for-future-on-raydium.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/HiNWH2uKxjrmqZjn9mr8vWu5ytp2Nsz6qLsHWa5XQ1V
 date: 2024-11-08
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-11-13-futardio-proposal-cut-emissions-by-50.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/6LcxhHS3JvDtbS1GoQS18EgH5Pzf7AnqQpR7D4HxmWp
 date: 2024-11-13
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-11-21-futardio-proposal-proposal-13.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/ApywwMrE9vkWiatZwQVU6wdvNsHrYZkhegNCV5XDZ8y
 date: 2024-11-21
 domain: internet-finance
 format: data
-status: entity-data
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 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-11-21-futardio-proposal-proposal-14.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/B4zpF4iHeF91qq8Szb9aD6pW1DrwSy6djD4QPWJQn3d
 date: 2024-11-21
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-11-21-futardio-proposal-should-metadao-create-futardio.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/zN9Uft1zEsh9h7Wspeg5bTNirBBvtBTaJ6i5KcEnbAb
 date: 2024-11-21
 domain: internet-finance
 format: data
-status: entity-data
+status: null-result
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 processed_by: rio

inbox/archive/2024-11-25-futardio-proposal-launch-a-boost-for-hnt-ore.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/2QUxbiMkDtoKxY2u6kXuevfMsqKGtHNxMFYHVWbqRK1
 date: 2024-11-25
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-11-25-futardio-proposal-prioritize-listing-meta.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/FXkyJpCVADXS6YZcz1Kppax8Kgih23t6yvze7ehELJp
 date: 2024-11-25
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-12-02-futardio-proposal-approve-deans-list-treasury-management.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/4gaJ8bi1gpNEx6xSSsepjVBM6GXqTDfLbiUbzXbARHW
 date: 2024-12-02
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-12-04-futardio-proposal-launch-a-boost-for-usdc-ore.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/GBQZvZAeW8xUuVV5a9FJHSyttzY5fPGuvkwLTpWLbw6
 date: 2024-12-04
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-12-05-futardio-proposal-establish-development-fund.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/DhY2YrMde6BxiqCrqUieoKt5TYzRwf2KYE3J2RQyQc7
 date: 2024-12-05
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-12-16-futardio-proposal-implement-3-week-vesting-for-dao-payments-to-strengthen-ecos.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/C2Up9wYYJM1A94fgJz17e3Xsr8jft2qYMwrR6s4ckaK
 date: 2024-12-16
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-12-19-futardio-proposal-allocate-50000-drift-to-fund-the-drift-ai-agent-request-for.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/A74H61YqwsbwRczuErbUyh9kqG1A7ZbiE1W5hWZmT9f
 date: 2024-12-19
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---

inbox/archive/2024-12-30-futardio-proposal-fund-deans-list-dao-website-redesign.md

@@ -6,7 +6,7 @@ url: "https://www.futard.io/proposal/5V5MFN69yB2w82QWcWXyW84L3x881w5TanLpLnKAKyK
 date: 2024-12-30
 domain: internet-finance
 format: data
-status: entity-data
+status: unprocessed
 tags: [futardio, metadao, futarchy, solana, governance]
 event_type: proposal
 ---