@Techmeme@techhub.socialOpenAI VP of Global Policy Ann O'Leary says AI policy in the US is anchored in the states and informed by California's AI transparency law passed last year (Chase DiFeliciantonio/Politico)
https://www.politico.com/news/2026/08/11/openai…
@arXiv_mathST_bot@mastoxiv.pageCrosslisted article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- The optimal information complexity of VC learning
Steve Hanneke, Juexiao Wang
https://arxiv.org/abs/2610.10600 https://mastoxiv.page/@arXiv_statML_bot/117409827258483322
- Anchored multiple testing: a transparent use of e-closure to improve FDR procedures
Aaditya Ramdas
https://arxiv.org/abs/2610.11279 https://mastoxiv.page/@arXiv_statME_bot/117410023315579714
- Conditional Kernel Stein Discrepancy
Federico Matteucci, Florian Kalinke
https://arxiv.org/abs/2610.11863 https://mastoxiv.page/@arXiv_statML_bot/117410021516368940
- Learning structured linear dynamical systems from missing observations
Aravinda Kanchana Ruwanpathirana, Hemant Tyagi, Sunny G. W. Wang
https://arxiv.org/abs/2610.11869 https://mastoxiv.page/@arXiv_statML_bot/117410026430296717
- RobustLDS: Learning linear dynamical systems under adversarial corruptions
Aravinda Kanchana Ruwanpathirana, Hemant Tyagi
https://arxiv.org/abs/2610.11906 https://mastoxiv.page/@arXiv_statML_bot/117410032917776023
- Dense uniqueness and dense nonuniqueness of Fr\'echet means
Stephan F. Huckemann, Alexander Lytchak
https://arxiv.org/abs/2610.11946 https://mastoxiv.page/@arXiv_mathPR_bot/117409989664330233
- Efficient quadratic entropy with distance sketches
Steve Huntsman
https://arxiv.org/abs/2610.11976 https://mastoxiv.page/@arXiv_statML_bot/117410036261374157
- Testing Algebraic Complete Intersections
Alessandro Tamai
https://arxiv.org/abs/2610.12288 https://mastoxiv.page/@arXiv_statML_bot/117410064181263319
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@arXiv_econGN_bot@mastoxiv.pageBeyond headcount and human capital: The Effective Cognitive Population as a decomposable capacity unit for AI-era planning
Kwan Soo Shin
https://arxiv.org/abs/2608.09642 https://arxiv.org/pdf/2608.09642 https://arxiv.org/html/2608.09642
arXiv:2608.09642v1 Announce Type: new
Abstract: National planning counts population, human capital, and artificial-intelligence preparedness in separate ledgers. Demographic accounting has advanced from headcount to skills-adjusted stocks and still debates how much age structure retains once skills are modeled, yet no existing unit carries the conditions under which preparedness becomes productive capacity. This study introduces the Effective Cognitive Population (ECP), a decomposable unit that weights population by capability and by the conditions under which capability is deployed, anchored to the World Bank Human Capital Index Plus (HCI ) and the non-overlapping dimensions of the IMF AI Preparedness Index. The architecture is portable in principle; the case tested here is artificial intelligence, which has a published preparedness index. For 144 countries, HCI becomes a productivity level, AI opportunity uses digital infrastructure and innovation integration, conversion governance uses regulation and ethics, and the benchmark is ECP = N H(1 AC). Against 2024 total output on identical population bases, ECP raises criterion R-squared from 0.849 for the HCI -adjusted stock to 0.882 and lowers leave-one-country-out RMSE from 0.723 to 0.641, with the working-age comparison identical and bootstrap intervals excluding zero. Eighty-nine of 144 countries move at least ten rank positions from headcount, mostly through the human-capital adjustment itself. Results are stable across denominators, vintages, aggregation forms, and a 27-rule multiverse. The direct A by C interaction is not statistically supported, so the conjunction is a planning rule rather than causal complementarity. ECP is a diagnostic ledger whose scope excludes forecasts of population decline and estimates of AI's causal productivity effect.
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@Techmeme@techhub.socialHong Kong-based Qupital, which offers cross-border ecommerce financing to SMEs, raised a $300M Series C led by M Capital as it weighs a possible IPO (FinTech Global)
https://fintech.global/2026/09/15/qupital-eyes-ipo-after-landmark-300m-series-c-round/
@arXiv_mathST_bot@mastoxiv.pageCrosslisted article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/2]:
- Slow Beats Fast at the Kesten-Stigum Threshold: Minimax, Fisher-Information and Belief-Propagatio...
Soroor Ghandali
https://arxiv.org/abs/2610.08872 https://mastoxiv.page/@arXiv_csIT_bot/117404138992107111
- Projected Normal Models on Recursively Curved Tori
Surojit Biswas, Aron Persson
https://arxiv.org/abs/2610.08974 https://mastoxiv.page/@arXiv_statME_bot/117404188737127024
- On the Computational Complexity of Hidden Markov Model Identification
Markel Zubia, Nils Jansen
https://arxiv.org/abs/2610.09104 https://mastoxiv.page/@arXiv_csCC_bot/117404126997614963
- Sketched Calibration for Conformal Prediction under Covariate Shift
Mehrdad Pournaderi
https://arxiv.org/abs/2610.09208 https://mastoxiv.page/@arXiv_statME_bot/117404252669099326
- Witnessing Quantum Bayesian Inference beyond Classical Learning
Francesco Buscemi
https://arxiv.org/abs/2610.09293 https://mastoxiv.page/@arXiv_quantph_bot/117404333250313464
- The Geometry of Anisotropic Dilation for Optimal Regularization
Carson Newman, Oscar Leong
https://arxiv.org/abs/2610.09310 https://mastoxiv.page/@arXiv_mathOC_bot/117404247557802541
- Sequential resetting procedures and false discovery rate
Qiuqi Wang, Ruodu Wang, Zhenyuan Zhang
https://arxiv.org/abs/2610.09339 https://mastoxiv.page/@arXiv_statME_bot/117404293730100893
- Finite-Rank Logistic Gaussian Processes with Exact Likelihood for Conditional Density Estimation
Jaehoan Kim, Indrajit Ghosh, Debdeep Pati, Dipankar Bandyopadhyay
https://arxiv.org/abs/2610.09452 https://mastoxiv.page/@arXiv_statME_bot/117404317913647249
- Fluctuations of Nonlinear Observables in Mean Field Neural Network Training
Arnaud Descours (UCBL), Geoffrey Lacour (MaIAGE)
https://arxiv.org/abs/2610.09768 https://mastoxiv.page/@arXiv_csLG_bot/117404461246986280
- Outperformance Inverse Optimization: Learning Objective Functions that Outperform Agent Decisions
Akira Kitaoka
https://arxiv.org/abs/2610.09890 https://mastoxiv.page/@arXiv_csAI_bot/117404439028887756
- Anchored Reversible Jump Sequential Monte Carlo
Janna van Assen, Max Hinne
https://arxiv.org/abs/2610.09930 https://mastoxiv.page/@arXiv_statME_bot/117404347800052818
- Multivariate LSTM Modeling for Wastewater Quality Prediction
Sophie Laruelle (LAMA), Innocentia B\'en\'edicta Sossou (LAMA)
https://arxiv.org/abs/2610.09996 https://mastoxiv.page/@arXiv_statAP_bot/117404242610898597
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@luana@wetdry.worldSophias boat can move while anchored? Mmagic boat!
@arXiv_qfinTR_bot@mastoxiv.pageHerding and Liquidity in Order-Book Markets. II. Fundamental Anchoring and the Resilience of Liquidity
Jan Novotny
https://arxiv.org/abs/2607.16970 https://arxiv.org/pdf/2607.16970 https://arxiv.org/html/2607.16970
arXiv:2607.16970v1 Announce Type: new
Abstract: An order-book market whose liquidity provision is anchored to a fundamental value carries a restoring force: the price mean-reverts to value and the book refills after a shock. We show this restoring force is a robust intrinsic stabiliser and identify it causally-dialling the anchor down removes the mean-reversion, and a leverage-driven fire-sale then self-sustains. Separately, we ask whether a stressed market transmits its liquidity stress to a coupled calmer one, and find that it cannot: across six transmission channels of increasing strength-cross-market herding, arbitrage flow, and market-maker withdrawal up to a funding-constrained population fire-sale and a leverage spiral-the receiver's stress is independent of whether its neighbour is stressed, at every anchor strength. Market-maker withdrawal thins the receiving book but does not ignite it. Our order parameter throughout is the one-sidedness of the book-liquidity stress rather than a directional price crash-so a liquidity crisis here means the sustained one-sidedness a failing anchor produces. A liquidity crisis in this model is a failure of fundamental anchoring, not of market making.
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@arXiv_csPL_bot@mastoxiv.pageVirtualSet: Typed Ontology Worlds as an LLM Generation Target for Grounded Queries and Guarded Decisions
Qunhui Zhang
https://arxiv.org/abs/2607.18821 https://arxiv.org/pdf/2607.18821 https://arxiv.org/html/2607.18821
arXiv:2607.18821v1 Announce Type: new
Abstract: Large language models increasingly read and act on enterprise data, but SQL gives a late error signal: hallucinated fields or relations can execute and return plausible wrong answers, while incorrect writes cannot be safely assessed after execution. We present VirtualSet, a live, receiver-typed ontology-world interface and generation target for LLMs. Instead of SQL, the model emits set expressions over entity-edge worlds. Generic Constraint Projection (GCP) checks expressions before execution, while future this preserves concrete receiver types through collection chains, turning invalid fields, edges, receivers, and actions into token-anchored type errors. Type-clean reads use a SQL fast path or bounded stream interpretation, with a parity oracle checking both paths over the exercised operator space. The same substrate supports guarded decisions: actions run first in a simulated world, and world-change events require external approval before actualization. On BIRD, we lift relational schemas into typed worlds and compare VirtualSet with direct SQL while holding the model, evidence, values, zero-shot setting, timeout, glossary, repair/voting, and grader constant where possible. On a frozen 1,072-question split, VirtualSet achieves 67.5% accuracy versus 63.5% for glossary-matched direct SQL with repair and voting ( 4.0 points; McNemar exact p = 0.00117) using deepseek-reasoner. Full-corpus analysis finds no engine mis-computation of a type-clean expression; remaining errors arise from model semantics or gold defects. In a 30-body guard corpus, the write chain intercepts 20/20 hallucinated action bodies with zero false positives. VirtualSet thus remains competitive on SQL's home benchmark while providing pre-execution semantics for guarded decisions.
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