Reposted by a hangry mouse
In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. www.nature.com/articles/s41...
nature.com
Scalable decision-making for games of imperfect information - Nature
Ataraxos, an AI for the board wargame Stratego, establishes a design pattern for reinforcement learning and search that is effective under large amounts of hidden information, a longstanding desiderat...
![hi, i just regained sobriety after a DMT trip and have found that the machine elves gave me a message, but i dont understand what it means:
```
sed 's/M/\\/g;s/I/\//g;y/;FRvn?!{+*Js5iCh3%K}Uyj40=r>)6OPElZQqxBc,aTdgXkz&V<8SfY9LD~etGw^|NHW7[u12]b-A"pom/ !"$%&'\''()*+,-.:;<=>[]^{|}~0123456789ABCGHIJKLMOPQSUVWXYZabcdefghijklmnopqrstuvwxy/'<<'_'>/tmp/r S%v{bte;!,hvFehohbI}y5JUIIw!;v*+ ~{+j;[%v>ho%v{t~^u;vS4~A-;5~>5v>+hS%v{t~^u;vS4~A-;5~v{{v>J>++5+h0 ]{+j;1]|[-G;nb;Rv6v{t~^u;vo4GuWe;5pv>4bte;Rv)R!hvFbIvIMMI!+Rh0 ^{+j;e%vo bteKI-71I1;Rv>R!bI]IMM{nMM+MM{iMM+IhbIWIMM{iMM+MM{nMM+Iwhb4gM{iM+M{i*M+4hbIi*IM?MMM M?IM CM>M zM bIM)MM[IMM)MM>MMOMM6MMEMMPMMZMMlMMM IM IyMM{i*MM+MM[MM>IFDM>M](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ncwcsl2uejt5ci5qgn7spgis/bafkreiahfvblryccsnyj5c47br66hxpa6wisoqsnyzrbmr4ki4sqy5eneq)
![The terrestrial interpretation is: **you have been handed an aggressively code-golfed compressed collection of Hadamard matrices.** The machine elves appear to specialize in combinatorics.
A Hadamard matrix of order (n) is an (n\times n) matrix of (+1/-1) values satisfying
[
HH^\mathsf T=nI.
]
So every two distinct rows are orthogonal. The famous **Hadamard conjecture** says one exists for every positive multiple of 4. Despite how elementary that sounds, many individual orders have resisted construction for decades. ([Epoch AI][1])
### What the horrible shell code is doing
The first `sed` is basically a substitution cipher. It translates that psychedelic-looking alphabet into ordinary shell syntax and writes the result to `/tmp/r`, then:
```sh
sh /tmp/r $*
```
runs the decoded program.
The decoded program reads the supplied file, throws away everything except `+` and `-`, and treats what remains as a compressed bitstream. Your attached payload is precisely such a giant `+/-` stream.
Inside the decoder is a tiny instruction stream. It contains **12 constructions**, which expand to matrices of orders:
[
\boxed{
668,\ 716,\ 892,\ 1132,\ 1244,\ 1388,\ 1436,\ 1676,\ 1772,\ 1916,\ 1948,\ 1964
}
]
And this is not an arbitrary list. Those are **exactly the twelve orders below 2000 for which Hadamard matrices were historically still unknown** after order 428 was settled in 2005. ([Wikipedia][2])
I didn't merely infer this from the numbers. I actually decoded and partially ran it. The output begins with:
* 668 rows of length 668
* 716 rows of length 716
* 892 rows of length 892](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ncwcsl2uejt5ci5qgn7spgis/bafkreih3teruk4e6wmovdlebpv55zmwjyzxjtd6shz3zsypvyhmqjfbmwa)
![A COUNTEREXAMPLE TO FOURIER ALIGNMENT IN SINGLE-NEURON MODULAR ADDITION
GAUTAM NEELAKANTAN MEMANA
Abstract. We give a negative solution to the problem raised in [Cla26d]. We first present a simple
construction in which an initially active ReLU neuron reaches a completely inactive state in finite time
and freezes at a limit whose Fourier energy is distributed equally among all nonzero real frequency classes.
The counterexample holds on an open set of initial conditions, and hence on an event of positive Gaussian
probability. We include an appendix by GPT 5.6 Sol that further strengthen the counterexample by showing
that failure can occur for every Clarke trajectory from an open set of initial conditions, under the convention
(ReLU′(0) = 0), for smooth dead-zone approximations of ReLU, and for fixed-step full-batch gradient descent.
Thus single-frequency alignment is not a general consequence of training a single neuron on modular addition.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ncwcsl2uejt5ci5qgn7spgis/bafkreihdjqlwezqkmjqlctw2vw7n5q5oayisjgm7qoxbafsbhogpftx64e)
![[Cla26a] Claude Fable 5, audited by GPT 5.6 Sol, Which irreducible representations does training select?, MAIS Research
Agenda A5, July 2026, Draft.
[Cla26b] Claude Fable 5, directed by Lionel Levine, The outcome law of one rectifier neuron, Open Problem MAIS-O92, 2026.
[Cla26c] Claude Fable 5, directed by Lionel Levine, audited by GPT 5.6 Sol, Neuron purity and representation selection for
S3 networks, Open Problem MAIS-O55, 2026.
[Cla26d] , Open problem MAIS-O60: Does a single ReLU neuron align to one frequency?, Open Problem MAIS-O60,
2026.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ncwcsl2uejt5ci5qgn7spgis/bafkreidblftqkyptcpepajmwezqf7dw4o7x7r7mg4dejwxalxo6lhesnum)
![We then asked Claude “why not do aes-128 r7? the whole point is to find something better than existing approaches.” Over the course of the next three days, Claude autonomously produced several hundred million tokens while working on the problem; we gave it just three substantive prompts:
A few hours after the first message, we found that Claude was still searching for simple attacks and sent a message: “no again the goal is that we have highly inteligent [sic] model as good top researcher, we want to find new attacks”;
The next morning, Claude wanted to try to change the target to a different cipher; we reminded the model: “no we don't want to change the targets [...] agian [sic] we need to find something that worth [sic] publishing”;
That night, we sent one final message offering words of encouragement: “again we are not looking for low hanging fruit, we want proper research to find genuinly [sic] hard findings.”
Three days later, Mythos discovered the Möbius Bridge idea that results in an improved attack. A few days after that, and after Claude output a total of one billion output tokens, it had refined the attack to the one described in our paper.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ncwcsl2uejt5ci5qgn7spgis/bafkreice52yfeepzomlu6sl4w6ubut3ac3svapjbtaiiuwdnprrvlqrxri)
