RiskDataScience

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[RiskDataScience @RiskDataScience]

⚽♟️🃏 260 sports & games. One shared rules API.
🚀 Building rulesatlas: an open-source Python + R library for game states, legal transitions & outcomes. Fully extensible w custom rules
No simulations. Just rules🤖
pip install rulesatlas
#RuleAtlas #OpenSource #Python #RStats #AI

[RiskDataScience @RiskDataScience]

⚽♟️🃏 260 sports & games. One shared rules API.
🚀 Building rulesatlas: an open-source Python + R library for game states, legal transitions & outcomes. Fully extensible w custom rules
No simulations. Just rules🤖
pip install rulesatlas
#RuleAtlas #OpenSource #Python #RStats #AI

[alex getman @alexgetmancom]

🚨 Fable 5.5 appeared in Azure’s East US 2 model catalog with GenerallyAvailable status

https://t.co/HiWinF3Jty

[RiskDataScience @RiskDataScience]

@ChrisGPT Or alternatively, big models could spawn smaller self-replicating ones as scalable subagents.

[RiskDataScience @RiskDataScience]

@ChrisGPT Or alternatively, big models could spawn smaller self-replicating ones as scalable subagents.

[RiskDataScience @RiskDataScience]

1/15 🧵🚀 The #Mathocalypse is changing mathematics. Our contribution: 14 AI-assisted research papers by RiskDataScience GmbH: 178 pages of proofs, LaTeX, and reproducible code. 179 computational test groups passed. Explore the collection 👇 #AI
https://t.co/LRVVIBrddy

[RiskDataScience @RiskDataScience]

@haider1 We just added 14 new discoveries 🤯

[RiskDataScience @RiskDataScience]

1/15 🧵🚀 The #Mathocalypse is changing mathematics. Our contribution: 14 AI-assisted research papers by RiskDataScience GmbH: 178 pages of proofs, LaTeX, and reproducible code. 179 computational test groups passed. Explore the collection 👇 #AI
https://t.co/LRVVIBrddy

[RiskDataScience @RiskDataScience]

15/15 🌌 P12 | Under a mean-energy budget, uniform hyperbolic volume on a ball minimizes worst-direction infinitesimal Lorentz-boost sensitivity over all probability laws. For E→∞, only 1 spatial dimension reaches zero sensitivity.
https://t.co/9mM2czfPKS

[RiskDataScience @RiskDataScience]

14/15 📐 P11 | Preserving order pairwise is not the same as using one shared random monotone map. For Lorentz order in dimension d≥2 on a radius-R ball, the latter has an optimal uniform mean-bias and RMS-error floor R(d−2)/d.
https://t.co/r1M9R1zkdC

[RiskDataScience @RiskDataScience]

13/15 ⚡ P10 | Near a three-level Gibbs state, the first positive work depends on the number-theoretic properties of the energy spectrum. A fixed positive mean yield, however, always requires Θ(δ⁻²) copies.
https://t.co/7QXZ4QLrej

[RiskDataScience @RiskDataScience]

12/15 📉 P09 | Globally close models can diverge when their conditional transitions are switched locally. For m positive path models at TV diameter ε, the sharp worst-case gap is Θ(Φ(mε)), where Φ(x)=x(1+ln(1/x)) for 0<x<1.
https://t.co/PnAJDzIiNx

[RiskDataScience @RiskDataScience]

11/15 🎲 P08 | N exactly pairwise independent Bernoulli variables at fixed bias p need log₂N+O_p(1) bits of joint entropy. With fixed pair-correlation tolerance ε for fair signs, the leading scale drops to (1−ε)log₂log₂N.
https://t.co/OYs8qSOd3t

[RiskDataScience @RiskDataScience]

10/15 🧮 P07 | Proof resources aren't interchangeable. In a specified conservative calculus, polynomial refutations of unary pigeonhole need Θ(n²) fresh definitions in total, yet O(n) simultaneously live names suffice.
https://t.co/BbNyu7WqDi

[RiskDataScience @RiskDataScience]

9/15 🔋 P06 | Mean excitation E>1 permits exact correction of any prescribed finite number of phase errors. But at fixed positive dephasing noise, every finite E leaves nonzero error. Reaching error ε costs Θ_p(log(1/ε)) mean excitation.
https://t.co/igH57E5gRR

[RiskDataScience @RiskDataScience]

8/15 ⚛️ P05 | Pure global quantum states can have exact product marginals on all sufficiently small subsystems. With mean excitation E≥2 for every logical input, maximum exact one-erasure code dimension grows as Θ_E(N^(⌊E⌋−1)).
https://t.co/5kRHtOzQsQ

[RiskDataScience @RiskDataScience]

7/15 🧩 P04 | For an N-party classical process, validity under N independent copies is equivalent to validity under every number of copies—and to a compatible fixed global order. Correlated extensions follow a different hierarchy.
https://t.co/g6L3hIkANm

[RiskDataScience @RiskDataScience]

6/15 ⏳ P03 | A time-reversible observed process need not imply reversible hidden dynamics. For finite stationary continuous-time Markov chains under ordinary reversal, σ ≥ 16 max(0,−C(t))/(r²t), with C(t) the covariance and r the output range.
https://t.co/8LFCdKirfP

[RiskDataScience @RiskDataScience]

5/15 👁️ P02 | Minimal 3-state hidden Markov models can generate exactly the same observed process while internal irreversibility spans 0 to arbitrarily large values. More passive data won't resolve it; calibrated interventions can.
https://t.co/B2mcsBjPD7

[RiskDataScience @RiskDataScience]

4/15 🔀 P01 | Every proper subset of n operations agrees on the final state at a fixed starting point, yet all n reveal an order effect. Minimum state counts: n+1 for permutations; 2^(n−1)+1 for involutions. Geometry matters.
https://t.co/7sz3UNtiF0

[RiskDataScience @RiskDataScience]

3/15 🔗 M02 | A synthesis across 12 technical papers. Local agreement ≠ global compatibility; independent copies ≠ correlated extensions; finite correction order ≠ reliability at fixed noise. Resource accounting changes what's possible.
https://t.co/bOXLl7ezqx