Tootfinder

Google DeepMind says four Gemini 4 Argon agents beat Minecraft without any human help in 27 hours and 14 minutes, costing $1,620 at Argon's planned 2027 price (Matthias Bastian/The Decoder)
https://the-decoder.com/four-gemini-4-argon-agents-beat-…

Four Gemini 4 Argon agents beat Minecraft without any human help
Four AI agents running on Google's Gemini 4 Argon have beaten Minecraft for the first time without human help, slaying the Ender Dragon in a fully autonomous run. With no predefined roles or strategies, they coordinated on their own, wrote 2,783 programs, exchanged 229 chat messages, and died 605 times. The session cost $1,620.

"The US has maintained plausible deniability as services and programmes provided to support Ukraine have suddenly begun to fail.".
A phrase from my youth: plausible deniability. Nixon moved it from the spy world to the political world. "The expression gained public prominence in the mid-1970s, when the Church Committee of the US Senate concluded that the doctrine, originally intended to protect the United States from the consequences of disclosure, had been expanded …

US gave us no warning of Russian strikes on Kyiv power plants, Zelenskyy says
Ukrainian president accuses Washington of ‘denial of reality’ in ongoing row with Trump over apparent support for Moscow

"The US has maintained plausible deniability as services and programmes provided to support Ukraine have suddenly begun to fail.".
A phrase from my youth: plausible deniability. Nixon moved it from the spy world to the political world. "The expression gained public prominence in the mid-1970s, when the Church Committee of the US Senate concluded that the doctrine, originally intended to protect the United States from the consequences of disclosure, had been expanded …

US gave us no warning of Russian strikes on Kyiv power plants, Zelenskyy says
Ukrainian president accuses Washington of ‘denial of reality’ in ongoing row with Trump over apparent support for Moscow

Replaced article(s) found for math.DG. https://arxiv.org/list/math.DG/new
[1/3]:
- Family index for Fredholm extensions of semi-Fredholm operators
Marina Prokhorova
https://arxiv.org/abs/2401.16060 https://mastoxiv.page/@arXiv_mathDG_bot/111843359116350796
- Constructing stable Hilbert bundles via Diophantine approximation
Yucheng Liu, Biao Ma
https://arxiv.org/abs/2501.15784
- Ricci Solitons of Left-Invariant Metrics on Lie Groups with Derived Algebra of Dimension at Most Two
Hamid Reza Salimi Moghaddam
https://arxiv.org/abs/2601.10730 https://mastoxiv.page/@arXiv_mathDG_bot/115920688396400257
- Mass and rigidity in almost K\"ahler geometry
Partha Ghosh
https://arxiv.org/abs/2603.08627 https://mastoxiv.page/@arXiv_mathDG_bot/116204063956883755
- Classifying Slice-Regular Polynomials via Group Actions on the Twistor Space
Chunlin Liu, Giovanni Moreno, Haipan Shi
https://arxiv.org/abs/2605.22788 https://mastoxiv.page/@arXiv_mathDG_bot/116617182455085202
- $G_2$ and the Maximally Symmetric (3, 8) Distribution with 6-Dimensional Square
Nicklas Day, Boris Doubrov, Igor Zelenko
https://arxiv.org/abs/2605.25910 https://mastoxiv.page/@arXiv_mathDG_bot/116639966776117301
- Cohn--Vossen-Type Inequalities for Three-Manifolds and Locally Conformally Flat Manifolds
Jialong Deng
https://arxiv.org/abs/2606.01368 https://mastoxiv.page/@arXiv_mathDG_bot/116679534951132380
- From Gradient Descent to Potential Theory: A Geometric Dictionary for Binary Classification
Catalin Vasii
https://arxiv.org/abs/2607.00988 https://mastoxiv.page/@arXiv_mathDG_bot/116849328935224137
- A sharp relative comparison inequality for conformal fillings of Poincar\'e--Einstein manifolds
Nan Wu
https://arxiv.org/abs/2607.13742 https://mastoxiv.page/@arXiv_mathDG_bot/116928601640640156
- A degenerate free boundary minimal annulus and non-uniqueness in spherical caps beyond the hemisp...
Alexander Pigazzini
https://arxiv.org/abs/2607.23534 https://mastoxiv.page/@arXiv_mathDG_bot/116996542129550830
- Special Lagrangians with multiple isolated singularities
Bryan Dimler, Filippo Gaia
https://arxiv.org/abs/2607.26302 https://mastoxiv.page/@arXiv_mathDG_bot/117007805427278588
- Demailly-Koll\'ar continuity on klt pairs, and applications to alpha and delta invariants
Tam\'as Darvas, Kewei Zhang
https://arxiv.org/abs/2608.23505 https://mastoxiv.page/@arXiv_mathDG_bot/117155289132945927
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from my link log —
Meritocracy is not only false, it's bad for you.
https://press.princeton.edu/ideas/a-belief-in-meritocracy-is-not-only-false-its-bad-for-you
saved 2020-07-24

A belief in meritocracy is not only false: it’s bad for you
Meritocracy has become a leading social ideal. Politicians across the ideological spectrum continually return to the theme that the rewards of life—money, power, jobs, university admission—should be distributed according to skill and effort.

Crosslisted article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/1]:
- Stable convergence of partial sum processes towards discontinuous limits
Johannes Brutsche
https://arxiv.org/abs/2608.12740 https://mastoxiv.page/@arXiv_mathPR_bot/117092840163122403
- Bayesian Inference Procedures for A/B Testing: An Overview
M{\aa}rten Schultzberg, Mattias Fr{\aa}nberg
https://arxiv.org/abs/2608.12949 https://mastoxiv.page/@arXiv_statME_bot/117092799825341949
- Estimation of distribution functions, their jumps and interval probabilities under measurement error
Kairat Mynbaev, Carlos Martins-Filho, Chad Brown
https://arxiv.org/abs/2608.13152 https://mastoxiv.page/@arXiv_econEM_bot/117092724945077876
- Extreme principal minors of Wishart and deformed GOE matrices
Zhanrui Dong, Tiefeng Jiang, Tuan Pham, Jianfeng Yao
https://arxiv.org/abs/2608.13154 https://mastoxiv.page/@arXiv_mathPR_bot/117092881421641444
- Sinkhorn Linearization and the Spectral Proxy: Unifying the Statistical and Algorithmic Theory of...
Han Dong, Jiaming Li, Yongqiang Gong, Ruixi Li, Yin Liu
https://arxiv.org/abs/2608.13201 https://mastoxiv.page/@arXiv_statML_bot/117092803789923153
- Weighted cumulative past inaccuracy and Kullback-Leibler divergence based on extropy: properties,...
Bighneswar Sahoo, Suchandan Kayal
https://arxiv.org/abs/2608.13363 https://mastoxiv.page/@arXiv_statME_bot/117092858417590746
- Nearly sharp comparison results for sliced and max-sliced Wasserstein distances
Jonathan Niles-Weed, Jacob Shkrob
https://arxiv.org/abs/2608.13374 https://mastoxiv.page/@arXiv_mathPR_bot/117092901477246948
- Theoretical Properties of Covariate-Adaptive Randomization with a Diverging Number of Covariates
Yuhang Tao, Li-Xin Zhang
https://arxiv.org/abs/2608.13442 https://mastoxiv.page/@arXiv_statME_bot/117092865494823071
- The data geometry of masking diffusion: Certified-optimal schedules via unmasking growth complexity
Martin J. Wainwright
https://arxiv.org/abs/2608.13520 https://mastoxiv.page/@arXiv_csLG_bot/117092942960850473
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Sensitivity of silicon-to-water dose conversion and Bragg-peak metrics to stopping-power datasets in proton dosimetry
F. Matias, J. M. B. Shorto, P. de Vera, R. Garcia-Molina, I. Abril, T. F. Silva, J. Pereira, H. Yoriyaz
https://arxiv.org/abs/2607.16441 https://arxiv.org/pdf/2607.16441 https://arxiv.org/html/2607.16441
arXiv:2607.16441v1 Announce Type: new
Abstract: Accurate proton dosimetry requires consistent stopping-power data for detector-to-water conversion and Monte Carlo radiation transport. We quantify the sensitivity of water-to-silicon stopping-power ratios, converted dose-to-water distributions, and Bragg-peak metrics to the stopping-power dataset for a 67.5 MeV pristine proton beam in water. PHITS simulations used SRIM-2013, PSTAR/NIST, ATIMA, and TDDFT-Penn stopping powers up to 10 MeV, together with a common SBETHE-based extension at higher energies. The water-to-silicon stopping-power ratio showed relative entrance-to-distal variations of 17.40%$-$22.10%. Conversion of normalized PTW silicon-diode percentage-depth-ionization data yielded relative percentage-depth-dose curves with a common maximum at 36.86 mm, consistent with the experimental Bragg-peak depth of (36.81 $\pm$ 0.15) mm. The converted curves were approximately 3% below the reference throughout the entrance and plateau regions, while deviations of up to 2.1% occurred near the Bragg peak. Direct PHITS calculations predicted Bragg-peak depths of 36.575$-$36.675 mm; only the TDDFT-Penn result lay within the quoted experimental uncertainty. These results show that stopping-power treatment affects the magnitude of the converted dose more strongly than the Bragg-peak position does, and that it should be harmonized or explicitly included in uncertainty budgets for proton-beam calibration, commissioning, quality assurance, and Monte Carlo validation.
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Uniform-Loss Automated Market Making for Prediction Markets
Ciamac C. Moallemi, Dan Robinson, Brian Zhu
https://arxiv.org/abs/2607.17428 https://arxiv.org/pdf/2607.17428 https://arxiv.org/html/2607.17428
arXiv:2607.17428v1 Announce Type: new
Abstract: Automated market makers (AMMs) for prediction markets descend from market scoring rules, where a mechanism operator subsidizes a market to aggregate beliefs about uncertain events. The existing literature has focused on bounding the total worst-case loss to the subsidizer, but has not addressed how that loss is distributed across price states or over time. We use the framework of loss-versus-rebalancing (LVR) to study this distribution and introduce \textit{uniform AMMs}, defined by the property that instantaneous LVR is proportional to pool value and independent of the current token price. In a static setting, we show that for a broad class of \textit{win-martingales} -- processes that converge to 0 or 1 at a fixed resolution time -- there exists a pricing function that achieves uniform LVR under that process, and conversely, that any sufficiently regular pricing function induces a win-martingale under which it is uniform. We then extend the framework to dynamic liquidity management, showing that liquidity levels can be adjusted over time to implement a prescribed target expected cumulative loss schedule. This theory is illustrated with canonical examples of win-martingales and pricing functions. Our results can inform AMM designers and liquidity providers on how the inevitable cost of subsidizing price discovery can be shaped and controlled across both price and time.
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Esto es horroroso. Amazon, entre otras, comprando toneladas de libros antiguos para destruirlos en el proceso de aprendizaje de sus IAs. Los destruyen literalmente porque para escanearlos los guillotinan separando las hojas de la encuadernación. https://www.404media.co/we-…

Replaced article(s) found for math.ST. https://arxiv.org/list/math.ST/new
[1/2]:
- Handling Covariate Mismatch in Collaborative Linear Prediction
Alexis Ayme, R\'emi Khellaf
https://arxiv.org/abs/2602.02083 https://mastoxiv.page/@arXiv_mathST_bot/116006104977371683
- Estimation of the sub-Gaussian Parameter
Jason Liu, Min Xu, Jinchuan Xing
https://arxiv.org/abs/2606.06384 https://mastoxiv.page/@arXiv_mathST_bot/116696474457760087
- Lambda-quantiles under the microscope
Fabio Bellini, Felix-Benedikt Liebrich
https://arxiv.org/abs/2608.07122 https://mastoxiv.page/@arXiv_mathST_bot/117070129938806713
- Consistent intercept estimation and inference for unit-root INAR(2) processes
Yang Lu, M\'arton Isp\'any
https://arxiv.org/abs/2609.23339 https://mastoxiv.page/@arXiv_mathST_bot/117313641576796810
- Shape without scale: an identifiability dichotomy for a bounded tail observed through a non-addit...
Jiarui Qi
https://arxiv.org/abs/2609.27384 https://mastoxiv.page/@arXiv_mathST_bot/117324926550805284
- Inference in generalized linear models with robustness to misspecified variances
Riccardo De Santis, Jelle J. Goeman, Jesse Hemerik, Samuel Davenport, Livio Finos
https://arxiv.org/abs/2209.13918
- Sharp bounds in perturbed smooth optimization
Vladimir Spokoiny
https://arxiv.org/abs/2505.02002 https://mastoxiv.page/@arXiv_mathOC_bot/114459723303641817
- Equilibrium Distribution for t-Distributed Stochastic Neighbor Embedding with Generalized Kernels
Yi Gu, Antonio Auffinger
https://arxiv.org/abs/2505.24311 https://mastoxiv.page/@arXiv_statML_bot/114612632034300271
- Identification and Estimation of Multi-order Tensor Factor Models
Zetai Cen
https://arxiv.org/abs/2508.13418 https://mastoxiv.page/@arXiv_statME_bot/115060214357911690
- A novel finite-sample testing procedure for composite null hypotheses via pointwise rejection
Joonha Park, Ming Wang
https://arxiv.org/abs/2601.02529 https://mastoxiv.page/@arXiv_statME_bot/115852820264866947
- Focused median bias reduction
Davide Benussi, Ioannis Kosmidis, Alessandra Salvan, Nicola Sartori
https://arxiv.org/abs/2606.28597 https://mastoxiv.page/@arXiv_statME_bot/116837971698715230
- Group Invariant Spectral Embedding
Yeari Vigder, Paulina Hoyos, David Thong, Joakim and\'en, Joe Kileel, Amit Moscovich
https://arxiv.org/abs/2607.08987 https://mastoxiv.page/@arXiv_csLG_bot/116911669269878341
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