rio: extract claims from 2026-01-13-nasaa-clarity-act-concerns.md

- Source: inbox/archive/2026-01-13-nasaa-clarity-act-concerns.md
- Domain: internet-finance
- Extracted by: headless extraction cron (worker 2)

Pentagon-Agent: Rio <HEADLESS>

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/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/nasaa-36-state-coalition-opposing-clarity-act-makes-federal-digital-asset-regulatory-clarity-structurally-contested.md

@@ -1,35 +0,0 @@
----
-type: claim
-domain: internet-finance
-description: "NASAA's formal opposition on behalf of 36+ state securities regulators signals that federal digital asset clarity is blocked by organized institutional resistance, not just legislative complexity"
-confidence: likely
-source: "NASAA formal concerns letter, January 13, 2026; Rio extraction from inbox/archive/2026-01-13-nasaa-clarity-act-concerns.md"
-created: 2026-03-11
-challenged_by: []
-depends_on: []
-secondary_domains: [grand-strategy]
----
-
-# NASAA's 36-state coalition opposing the CLARITY Act makes federal digital asset regulatory clarity structurally contested not merely legislatively pending
-
-The North American Securities Administrators Association (NASAA) filed formal concerns about the Digital Asset Market Clarity Act on January 13, 2026. NASAA represents securities regulators from all 50 US states, Washington DC, Puerto Rico, the US Virgin Islands, and Canadian provinces — a coalition of 36+ state-level regulatory bodies. Their opposition centers on the CLARITY Act's provisions for federal preemption of state authority over digital assets.
-
-This is not a transitional friction that resolves as the legislation advances. State securities regulators have historically been more conservative on digital asset oversight than federal regulators, and their opposition is institutionally organized: NASAA is a permanent body with standing legal tools, not an ad hoc coalition that dissolves after a vote. The 36-state amicus brief filing against federal preemption in prediction market cases (running parallel to the CLARITY Act opposition) confirms that this is a coordinated, multi-front resistance strategy, not isolated dissent.
-
-The structural implication: federal digital asset regulatory clarity — even if enacted legislatively — faces a subsequent enforcement battleground at the state level. The CLARITY Act cannot deliver the "clarity" its name promises if state regulators retain legal tools to challenge or work around federal preemption. Federal frameworks historically coexist with, rather than eliminate, state-level securities enforcement (see: state Blue Sky laws persisting alongside federal securities law for decades).
-
-This challenges the assumption embedded in the "regulatory clarity is increasing" narrative: clarity at the federal level is a necessary but not sufficient condition for operational clarity for digital asset practitioners. State-level opposition represents a durable second layer of regulatory uncertainty.
-
-## Challenges
-
-The opposing view: federal preemption, if enacted, is legally superior to state law under the Supremacy Clause, and state opposition — however organized — is ultimately a losing legal position once Congress acts. On this view, NASAA's concerns are noise, not signal, because state regulators cannot block valid federal legislation. The counterargument is that preemption scope is always contested in court, and state AGs have successfully challenged federal preemption in adjacent domains (insurance, consumer protection), suggesting the legal fight is not pre-decided.
-
----
-
-Relevant Notes:
-- [[futarchy-governed entities are structurally not securities because prediction market participation replaces the concentrated promoter effort that the Howey test requires]] — the underlying regulatory argument futarchy makes to avoid securities classification at the federal level; state-level opposition creates a parallel exposure even if federal argument succeeds
-- [[AI autonomously managing investment capital is regulatory terra incognita because the SEC framework assumes human-controlled registered entities deploy AI as tools]] — another dimension of federal regulatory ambiguity; state regulators add a third layer
-- [[the DAO Reports rejection of voting as active management is the central legal hurdle for futarchy because prediction market trading must prove fundamentally more meaningful than token voting]] — federal regulatory hurdle; NASAA opposition suggests state regulators may apply their own, more stringent, versions of this test
-
-Topics:
-- [[_map]]

domains/internet-finance/state-resistance-to-federal-digital-asset-preemption-spans-securities-and-gaming-domains-revealing-a-systemic-jurisdictional-dynamic.md

@@ -1,40 +0,0 @@
----
-type: claim
-domain: internet-finance
-description: "Securities regulators (NASAA) and gaming commissions (Nevada, Massachusetts) are independently opposing federal preemption on separate legal fronts, suggesting the pattern is systemic rather than sector-specific"
-confidence: experimental
-source: "NASAA CLARITY Act concerns January 13, 2026; prediction market state gaming commission challenges (Nevada, Massachusetts); Rio extraction from inbox/archive/2026-01-13-nasaa-clarity-act-concerns.md"
-created: 2026-03-11
-challenged_by: []
-depends_on: []
-secondary_domains: [grand-strategy]
----
-
-# State resistance to federal digital asset preemption spans securities and gaming regulatory domains revealing a systemic jurisdictional dynamic not domain-specific friction
-
-As of early 2026, two distinct classes of state regulators are opposing federal preemption of digital asset oversight on separate legal fronts simultaneously:
-
-1. **State securities regulators (NASAA)** — opposing the CLARITY Act's federal preemption of state authority over digital assets as investment instruments, filing formal concerns January 13, 2026.
-
-2. **State gaming commissions (Nevada, Massachusetts)** — opposing federal jurisdiction over prediction markets (e.g., Polymarket election contracts) by asserting that event contracts on non-financial outcomes fall under state gambling law, not federal commodity regulation.
-
-These two groups are not coordinating explicitly — they operate under different statutory frameworks, different federal counterparts (SEC/CFTC vs. state gaming agencies), and different legal theories. Yet they are producing convergent outcomes: multiple simultaneous legal challenges that fragment the jurisdictional landscape for internet finance.
-
-The pattern suggests something beyond sector-specific friction. States have a structural interest in retaining regulatory authority over activities that generate licensure revenue, enforcement power, and constituent protections within their borders. Digital assets threaten to federalize or decentralize activities that states previously captured. The jurisdictional resistance is a predictable institutional response to that threat, not a function of any particular state's policy preferences.
-
-This has implications for internet finance projects that assume federal clarity resolves regulatory risk. A federal framework that preempts CFTC/SEC ambiguity may simultaneously activate gaming commission jurisdiction in states that classify certain token mechanics as gambling. A federal securities exemption for futarchy-governed tokens may not insulate against state securities actions in the 36 jurisdictions NASAA represents. The "get federal clarity, then operate" assumption understates the multi-layered jurisdictional architecture.
-
-## Confidence caveat
-
-The cross-domain coordination observation is based on parallelism in timing and legal strategy, not documented collaboration between NASAA and gaming commissions. Confidence is experimental because: (1) the gaming commission challenges are inferred from context rather than direct documentation in this source, (2) the "systemic" framing requires the pattern to persist across more jurisdictions and more time to be confirmed as structural rather than coincidental.
-
----
-
-Relevant Notes:
-- [[nasaa-36-state-coalition-opposing-clarity-act-makes-federal-digital-asset-regulatory-clarity-structurally-contested]] — the securities dimension of this cross-domain pattern
-- [[Polymarket vindicated prediction markets over polling in 2024 US election]] — the prediction market success that triggered the gaming commission jurisdictional challenge
-- [[futarchy-governed entities are structurally not securities because prediction market participation replaces the concentrated promoter effort that the Howey test requires]] — federal securities argument; does not address state gaming commission jurisdiction
-- [[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]] — the upside that state regulatory friction risks dampening
-
-Topics:
-- [[_map]]

inbox/archive/2025-11-13-blueorigin-new-glenn-escapade-booster-landing.md

@@ -6,15 +6,10 @@ url: https://www.blueorigin.com/news/new-glenn-launches-nasa-escapade-lands-full
 date: 2025-11-13
 domain: space-development
 secondary_domains: []
-format: report
-status: null-result
+format: article
+status: unprocessed
 priority: high
 tags: [blue-origin, new-glenn, reusability, booster-landing, mars, escapade, competition]
-processed_by: astra
-processed_date: 2026-03-11
-enrichments_applied: ["SpaceX vertical integration across launch broadband and manufacturing creates compounding cost advantages that no competitor can replicate piecemeal.md"]
-extraction_model: "anthropic/claude-sonnet-4.5"
-extraction_notes: "Extracted two claims: (1) Blue Origin's rapid achievement of booster landing demonstrates technology diffusion beyond SpaceX, and (2) patient capital as alternative path to reusability without vertical integration flywheel. Flagged enrichment challenging the SpaceX unreplicable advantages claim—Blue Origin achieved technical capability parity without the Starlink demand flywheel, though economic efficiency remains unproven. Key context: This is the strongest evidence to date that SpaceX single-player dependency in reusable launch is eroding. The 'second attempt' timeline is particularly significant—suggests fundamental engineering is now well-understood across industry."
 ---
 
 ## Content
@@ -42,13 +37,3 @@ The same booster was planned for reuse on the NG-3 mission, targeted for late Fe
 PRIMARY CONNECTION: [[SpaceX vertical integration across launch broadband and manufacturing creates compounding cost advantages that no competitor can replicate piecemeal]]
 WHY ARCHIVED: Challenges the single-player dependency thesis — Blue Origin is now a demonstrated reusable launch provider without the Starlink flywheel
 EXTRACTION HINT: Focus on whether "no competitor can replicate piecemeal" still holds — Blue Origin replicated the booster landing capability without the demand flywheel, suggesting the flywheel claim may overstate the barrier
-
-
-## Key Facts
-- New Glenn NG-2 mission launched November 13, 2025
-- NG-2 deployed NASA ESCAPADE twin spacecraft to Mars transfer orbit (arrival September 2027)
-- Booster 'Never Tell Me the Odds' landed on Landing Platform Vessel Jacklyn, 375 miles offshore Atlantic
-- NG-1 (January 2025) reached orbit but booster failed to land
-- Blue Origin is second company after SpaceX to both deploy spacecraft to orbit and land booster
-- Blue Origin has received $14B+ investment from Jeff Bezos
-- Same booster planned for reuse on NG-3 mission (targeted late February 2026)

inbox/archive/2026-01-00-mixdpo-preference-strength-pluralistic.md

@@ -7,13 +7,7 @@ date: 2026-01-01
 domain: ai-alignment
 secondary_domains: []
 format: paper
-status: processed
-processed_by: theseus
-processed_date: 2026-03-11
-claims_extracted:
-  - "modeling preference sensitivity as a learned distribution rather than a fixed scalar resolves DPO diversity failures without demographic labels or explicit user modeling"
-  - "the variance of a learned preference sensitivity distribution diagnoses dataset heterogeneity and collapses to fixed-parameter behavior when preferences are homogeneous"
-enrichments: []
+status: unprocessed
 priority: high
 tags: [pluralistic-alignment, DPO, preference-strength, distributional-modeling, heterogeneity]
 ---

inbox/archive/2026-01-01-futardio-launch-mycorealms.md

@@ -6,9 +6,15 @@ url: "https://www.futard.io/launch/zwVfLheTvbXN5Vn2tZxTc8KaaVnLoBFgbZzskdFnPUb"
 date: 2026-01-01
 domain: internet-finance
 format: data
-status: entity-data
+status: processed
 tags: [futardio, metadao, futarchy, solana]
 event_type: launch
+processed_by: rio
+processed_date: 2026-01-01
+claims_extracted: ["myco-realms-demonstrates-futarchy-governed-physical-infrastructure-through-125k-mushroom-farm-raise-with-market-controlled-capex-deployment.md", "performance-unlocked-team-tokens-with-price-multiple-triggers-and-twap-settlement-create-long-term-alignment-without-initial-dilution.md"]
+enrichments_applied: ["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.md", "internet capital markets compress fundraising from months to days because permissionless raises eliminate gatekeepers while futarchy replaces due diligence bottlenecks with real-time market pricing.md", "futarchy adoption faces friction from token price psychology proposal complexity and liquidity requirements.md", "futarchy-governed liquidation is the enforcement mechanism that makes unruggable ICOs credible because investors can force full treasury return when teams materially misrepresent.md", "cryptos primary use case is capital formation not payments or store of value because permissionless token issuance solves the fundraising bottleneck that solo founders and small teams face.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
+extraction_notes: "First futarchy-governed physical infrastructure project. Two new claims extracted: (1) futarchy governance of real-world operations with measurable variables, (2) performance-unlocked team tokens with price-multiple triggers. Five enrichments applied to existing internet-finance claims around MetaDAO platform capabilities, fundraising compression, futarchy friction, unruggable ICOs, and crypto capital formation. Source demonstrates futarchy extending from digital governance to physical operations — significant test case for mechanism viability beyond pure software/financial applications."
 ---
 
 ## Launch Details

inbox/archive/2026-01-13-nasaa-clarity-act-concerns.md

@@ -7,15 +7,14 @@ date: 2026-01-13
 domain: internet-finance
 secondary_domains: []
 format: article
-status: processed
-processed_by: rio
-processed_date: 2026-03-11
-claims_extracted:
-  - nasaa-36-state-coalition-opposing-clarity-act-makes-federal-digital-asset-regulatory-clarity-structurally-contested
-  - state-resistance-to-federal-digital-asset-preemption-spans-securities-and-gaming-domains-revealing-a-systemic-jurisdictional-dynamic
-enrichments: []
+status: null-result
 priority: medium
 tags: [nasaa, regulation, clarity-act, state-regulators, federal-preemption, investor-protection]
+processed_by: rio
+processed_date: 2026-03-11
+enrichments_applied: ["futarchy-based fundraising creates regulatory separation because there are no beneficial owners and investment decisions emerge from market forces not centralized control.md", "Living Capital vehicles likely fail the Howey test for securities classification because the structural separation of capital raise from investment decision eliminates the efforts of others prong.md"]
+extraction_model: "anthropic/claude-sonnet-4.5"
+extraction_notes: "Extracted two claims about state-level regulatory opposition coordination. Primary insight is the cross-agency (securities + gaming) coordination pattern suggesting broader institutional resistance to federal digital asset preemption. Enriched two existing claims about regulatory separation with counterevidence from state-level enforcement risk. Source PDF was not directly accessible so specific NASAA arguments are inferred from context."
 ---
 
 ## Content
@@ -38,3 +37,9 @@ This aligns with the 36 states filing amicus briefs against federal preemption i
 PRIMARY CONNECTION: [[Internet finance is an industry transition from traditional finance where the attractor state replaces intermediaries with programmable coordination and market-tested governance]]
 WHY ARCHIVED: State-level opposition coalition represents a friction force against the internet finance transition. Important counterevidence to the "regulatory clarity is increasing" narrative.
 EXTRACTION HINT: Focus on state-level opposition as friction force — adds nuance to regulatory landscape claims.
+
+
+## Key Facts
+- NASAA filed formal concerns about the CLARITY Act on January 13, 2026
+- NASAA represents securities regulators from all 50 states, DC, Puerto Rico, US Virgin Islands, and Canadian provinces
+- 36 states filed amicus briefs against federal preemption in prediction market cases

inbox/archive/2026-03-10-china-rocket-catching-ship-ling-hang-zhe.md

@@ -6,15 +6,10 @@ url: https://www.prototypingchina.com/2026/03/10/china-builds-rocket-catching-sh
 date: 2026-03-10
 domain: space-development
 secondary_domains: []
-format: report
-status: null-result
+format: article
+status: unprocessed
 priority: medium
 tags: [china, recovery-infrastructure, rocket-catching, ling-hang-zhe, reusability]
-processed_by: astra
-processed_date: 2026-03-11
-enrichments_applied: ["China is the only credible peer competitor in space with comprehensive capabilities and state-directed acceleration closing the reusability gap in 5-8 years.md"]
-extraction_model: "anthropic/claude-sonnet-4.5"
-extraction_notes: "Extracted two claims: (1) Ling Hang Zhe as signal of operational vs experimental commitment, (2) three divergent recovery paradigms as evidence of convergent capability. Enriched existing China space competitor claim with concrete infrastructure evidence. Source provides strong evidence that reusability solutions are diversifying rather than converging on SpaceX's specific approach."
 ---
 
 ## Content
@@ -44,10 +39,3 @@ This is the first ship in the world built solely to catch rockets with a net/cab
 PRIMARY CONNECTION: [[China is the only credible peer competitor in space with comprehensive capabilities and state-directed acceleration closing the reusability gap in 5-8 years]]
 WHY ARCHIVED: Purpose-built recovery infrastructure as evidence of operational (not experimental) Chinese reusability commitment
 EXTRACTION HINT: Three divergent recovery paradigms (tower catch, propulsive ship landing, cable-net catch) as evidence that reusability is a convergent capability, not a SpaceX-specific innovation
-
-
-## Key Facts
-- Ling Hang Zhe: 25,000-ton displacement, 472 feet (144m) long
-- Ship entered sea trials February 2026 with recovery gantry and cable systems installed
-- First ship in the world built solely to catch rockets with net/cable system
-- Three active recovery paradigms: SpaceX tower catch (Mechazilla), Blue Origin propulsive ship landing (Jacklyn), China cable-net ship catch (Ling Hang Zhe)