TMBR: October 2025: Difference between revisions

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[[Piecewise Affine Function|Piecewise Affine Functions]] (PAF) were explored as a generalization of the [[BMO1]] rules:
[[Piecewise Affine Function|Piecewise Affine Functions]] (PAF) were explored as a generalization of the [[BMO1]] rules:


* @Bard proved that 3 dimension PAF are Turing complete: [https://discord.com/channels/960643023006490684/1239205785913790465/1420457986564030641]
* @Bard proved that 3 dimension PAF are Turing complete.<sup>[https://discord.com/channels/960643023006490684/1239205785913790465/1420457986564030641]</sup>
* @star proved that 2 dimension PAF are Turing complete: [https://discord.com/channels/960643023006490684/1239205785913790465/1421271424588451915]
* @star proved that 2 dimension PAF are Turing complete.<sup>[https://discord.com/channels/960643023006490684/1239205785913790465/1421271424588451915]</sup>
* Shawn Ligocki wrote up a proof sketch that 2-region PAF are Turing complete: [https://discord.com/channels/960643023006490684/1239205785913790465/1422772752980639866 <nowiki>[3]</nowiki>]
* Shawn Ligocki wrote up a proof sketch that 2-region PAF are Turing complete.<sup>[https://discord.com/channels/960643023006490684/1239205785913790465/1422772752980639866]</sup>
* It was discovered that Amir Ben-Amram had already proven that 2-dim and 2-region PAF were Turing complete in 2015.
* It was discovered that Amir Ben-Amram had already proven that 2-dim and 2-region PAF were Turing complete in 2015.
* BMO1 is a 2-dim, 2-region PAF so this provides some sense for the difficulty of the problem.
* BMO1 is a 2-dim, 2-region PAF so this provides some sense for the difficulty of the problem.
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== Deciders ==
== Deciders ==
* Inductive deciders
* Inductive deciders
** -d rewrote quick_sim.py in C++, achieving a 6-10x faster runtime<sup>[https://discord.com/channels/960643023006490684/1226543091264126976/1432118726492291173 1][https://discord.com/channels/960643023006490684/1226543091264126976/1433247936942440498 2]</sup>.
** -d rewrote quick_sim.py in C++, achieving a 6-10x faster runtime.<sup>[https://discord.com/channels/960643023006490684/1226543091264126976/1432118726492291173][https://discord.com/channels/960643023006490684/1226543091264126976/1433247936942440498]</sup>
** Katelyn Douchette is working on an automated inductive decider<sup>[https://discord.com/channels/960643023006490684/1369339127652159509/1419016459560161280 1][https://discord.com/channels/960643023006490684/1095740122139480195/1427714010697961534 2].</sup> (see [[Inductive Proof System|inductive proofs]])
** Katelyn Douchette is working on an automated inductive decider.<sup>[https://discord.com/channels/960643023006490684/1369339127652159509/1419016459560161280][https://discord.com/channels/960643023006490684/1095740122139480195/1427714010697961534]</sup> (see [[Inductive Proof System|inductive proofs]])


== Misc ==
== Misc ==
* @coda shared a mechanical implementation of a Turing Machine<sup>[https://discord.com/channels/960643023006490684/1362008236118511758/1425894649280598066 1]</sup>, [[Antihydra]].
* @coda shared a mechanical implementation of a Turing Machine, [[Antihydra]].<sup>[https://discord.com/channels/960643023006490684/1362008236118511758/1425894649280598066]</sup>
* @Bricks shared a method to measure susceptibility to block-analysis<sup>[https://discord.com/channels/960643023006490684/1239205785913790465/1430227817957953638 1]</sup>, which resulted in the following [https://docs.google.com/spreadsheets/d/1j00LBxxp9W7uz1wZdMIvDCZ56eReuH0IGO9Z8-yybcQ/edit?usp=sharing spreadsheet of machines] which should be easiest to solve using [[Block Analysis|Block-Analysis.]]
* @Bricks shared a method to estimate susceptibility to [[Block Analysis]] and a [https://docs.google.com/spreadsheets/d/1j00LBxxp9W7uz1wZdMIvDCZ56eReuH0IGO9Z8-yybcQ/edit?usp=sharing spreadsheet] of [[BB(6)]] holdouts quantified by it.<sup>[https://discord.com/channels/960643023006490684/1239205785913790465/1430227817957953638][https://discord.com/channels/960643023006490684/1239205785913790465/1430651610102632579]</sup>


== Holdouts ==
== Holdouts ==

Revision as of 18:42, 2 November 2025

Prev: September 2025 This Month in Beaver Research Next: November 2025
A brave busy beaver confronts the dreaded Antihydra. Copyright Nico Roper. Commissioned for Why Busy Beaver Hunters Fear the Antihydra.

This edition of TMBR is in progress and has not yet been released. Please add any notes you think may be relevant (including in the form a of a TODO with a link to any relevant Discord discussion).

TODO: BB(3x3) month

Wily Coyote, a BB(3,3) holdout

TODO: BB(2x5) month next month (?)

Blog Posts

Champions

Polygon identified a new BB(4,3) champion with a score of over 1044 (1RB1RD1LC_2LB1RB1LC_1RZ1LA1LD_0RB2RA2RD (bbch)). This TM was first proven to halt by Pavel Kropitz in May 2024, but its runtime was not known at the time.

Theory

Piecewise Affine Functions (PAF) were explored as a generalization of the BMO1 rules:

  • @Bard proved that 3 dimension PAF are Turing complete.[1]
  • @star proved that 2 dimension PAF are Turing complete.[2]
  • Shawn Ligocki wrote up a proof sketch that 2-region PAF are Turing complete.[3]
  • It was discovered that Amir Ben-Amram had already proven that 2-dim and 2-region PAF were Turing complete in 2015.
  • BMO1 is a 2-dim, 2-region PAF so this provides some sense for the difficulty of the problem.
  • This introduces a new type of Cryptids separate from previous Collatz-like ones.

Deciders

  • Inductive deciders
    • -d rewrote quick_sim.py in C++, achieving a 6-10x faster runtime.[4][5]
    • Katelyn Douchette is working on an automated inductive decider.[6][7] (see inductive proofs)

Misc

Holdouts

BB(6) Holdouts count decrease overtime.
BB(6) Holdouts count decrease overtime.
BB Holdout Reduction by Domain
Domain New Holdout Count Previous Holdout Count Holdout Reduction % Reduction
BB(6) 1618 1691 73 4.3%
BB(7) 20,405,295 22,801,601 2,396,306 10.5%
BB(4,3) 9,401,447 460,916,384 451,514,937 98.0%
BB(3,4) 15,136,283 434,787,751 419,651,468 96.6%
BB(2,6) 870,085 873,469 3384 0.4%
Details