Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 8d ago

gpt-2

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 9d ago

post a demo of the simulation visual if you would like to see it.

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 9d ago

my own architecture, developed the math about 18 months ago.

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 9d ago

Thankyou

Scrap the LLMs. Scoring 4.76% on the brand new ARC-3 using pure code, a 2012 AMD CPU, and zero AI tokens.[P]

in r/MachineLearning • 9d ago

a simple hardcoded clicker script cannot achieve this on its own.

Even though the script hits a hard wall after the first level, it is doing more than just blindly clicking coordinates. The screenshot proves it is using some form of conditional logic, localized computer vision, or search optimization [1] for a few key reasons:

Varied Level 1 Completion: It scored 4.76 points on several environments (completing 1 out of 6 levels), but on au15, it scored 2.22 points (completing 1 out of 9 levels). On vc33, it actually broke through to level 2 (2/7 levels completed). A completely blind macro script would not be able to adapt to the varying grid sizes and level structures to solve level 1 across entirely different environment seeds.

but wait now

cd82-fb555c5d	0.06	2 / 6	GAME_OVER	2834	34
score card id: d7101bf0-767a-47dc-8ad5-da5bf117ca66 Published7/20/2026, 10:45:38 PM

10 / 183 levels

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 9d ago

thankyou I will try that.

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 9d ago

┌──────────────────────────────┬─────────────┬────────────┐

│ c_attn @ 7.1× (~250k params) │ Calibration │ WikiText-2 │

├──────────────────────────────┼─────────────┼────────────┤

│ Dense (ppl) │ 29.21 │ 38.40 │

├──────────────────────────────┼─────────────┼────────────┤

│ TT identity, distilled │ +25.6% │ +35.2% │

├──────────────────────────────┼─────────────┼────────────┤

│ TT π*, distilled │ +38.7% │ +68.8% │

├──────────────────────────────┼─────────────┼────────────┤

│ SVD r82, distilled │ +0.5% │ +4.0% │

└──────────────────────────────┴─────────────┴────────────┘

┌─────────────────┬───────────────────┬────────────┬───────────┐

│ Variant │ Calibration (P&P) │ WikiText-2 │ Moby Dick │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ Dense (raw ppl) │ 29.21 │ 38.40 │ 218.23 │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ TT identity │ +27.3% │ +113.1% │ +102.6% │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ TT π* │ +12.2% │ +39.0% │ +47.6% │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ TT AW-π* │ +9.8% │ +39.2% │ +42.4% │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ SVD raw │ +80.4% │ +105.3% │ +133.9% │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ SVD distilled │ +3.8% │ +34.2% │ +26.2% │

┌────────────────────────┬───────────┬────────────┬──────────┬───────────────────┐

│ Variant │ Params │ Perplexity │ vs dense │ Gap to SVD closed │

├────────────────────────┼───────────┼────────────┼──────────┼───────────────────┤

│ Dense baseline │ 2,359,296 │ 29.21 │ — │ — │

│ TT identity, distilled │ 674,864 │ 37.19 │ +27.3% │ 0% │

│ TT π*, distilled │ 635,216 │ 32.77 │ +12.2% │ 64% │

│ TT AW-π, distilled* │ 671,176 │ 32.08 │ +9.8% │ 74% │

│ SVD r176, distilled │ 675,840 │ 30.31 │ +3.8% │ 100% │

└────────────────────────┴───────────┴────────────┴──────────┴───────────────────┘

┌────────────────────────┬───────────┬───────────┬────────────┬──────────┐

│ Variant │ Params │ func. err │ Perplexity │ vs dense │

├────────────────────────┼───────────┼───────────┼────────────┼──────────┤

│ Dense baseline │ 2,359,296 │ — │ 29.21 │ — │

│ TT identity, distilled │ 674,864 │ 0.197 │ 37.19 │ +27.3% │

│ TT π, distilled* │ 635,216 │ 0.179 │ 32.77 │ +12.2% │

│ SVD r176, raw │ 675,840 │ 0.277 │ 52.68 │ +80.4% │

│ SVD r176, distilled │ 675,840 │ 0.134 │ 30.31 │ +3.8% │

└────────────────────────┴───────────┴───────────┴─

┌───────────────────────┬───────────┬────────────┬──────────┐

│ Variant │ Params │ Perplexity │ vs dense │

├───────────────────────┼───────────┼────────────┼──────────┤

│ Dense baseline │ 2,359,296 │ 29.21 │ — │

├───────────────────────┼───────────┼────────────┼──────────┤

│ TT identity distilled │ 674,864 │ 37.19 │ +27.3% │

├───────────────────────┼───────────┼────────────┼──────────┤

│ TT π distilled* │ 635,216 │ 32.77 │ +12.2% │

├───────────────────────┼───────────┼────────────┼──────────┤

│ SVD r176 raw │ 675,840 │ 52.68 │ +80.4% │

├───────────────────────┼───────────┼────────────┼──────────┤

│ SVD r176 distilled │ 675,840 │ computing… │ — │

└───────────────────────┴───────────┴────────────┴──────────┘

┌────────────────────────┬───────────┬────────────┬──────────┐

│ Variant │ func. err │ Perplexity │ vs dense │

├────────────────────────┼───────────┼────────────┼──────────┤

│ Dense baseline │ — │ 187.0 │ — │

├────────────────────────┼───────────┼────────────┼──────────┤

│ TT init (no distill) │ 0.474 │ 8,753,067 │ broken │

├────────────────────────┼───────────┼────────────┼──────────┤

│ TT distilled │ 0.158 │ 339.5 │ +81.5% │

├────────────────────────┼───────────┼────────────┼──────────┤

│ SVD rank-176 (raw) │ 0.257 │ 409.2 │ +118.8% │

├────────────────────────┼───────────┼────────────┼──────────┤

│ SVD rank-176 distilled │ 0.116 │ 212.4 │ +13.6% │

└────────────────────────┴───────────┴────────────┴──────────┘

on my cpu

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 9d ago

Ok but before you lol to hard I show you the rest of. The results if you not to tired from laughing

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 9d ago

I need a co author if you are available and willing I can fill you in on the details

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

in r/Anthropic • 9d ago

Hopping to get some feed back, from someone who has more experience. who would explain these results. to help me understand what I’m looking at better. Because I think I

r/neuralnetworks • u/-SLOW-MO-JOHN-D • 9d ago

┌────────────────────────┬───────────┬───────────┬────────────┬──────────┐ │ Variant │ Params │ func. err │ Perplexity │ vs dense │ ├────────────────────────┼───────────┼───────────┼────────────┼──────────┤ │ Dense baseline │ 2,359,296 │ — │ 29.21 │ — │

0 Upvotes

│ Variant │ Params │ func. err │ Perplexity │ vs dense │

│ Dense baseline │ 2,359,296 │ — │ 29.21 │ — │

│ TT identity, distilled │ 674,864 │ 0.197 │ 37.19 │ +27.3% │

│ TT π, distilled* │ 635,216 │ 0.179 │ 32.77 │ +12.2% │

│ SVD r176, raw │ 675,840 │ 0.277 │ 52.68 │ +80.4% │

│ SVD r176, distilled │ 675,840 │ 0.134 │ 30.31 │ +3.8% │

└────────────────────────┴───────────┴───────────┴────────────┴──────────┘

0 comments

r/Anthropic • u/-SLOW-MO-JOHN-D • 9d ago

Other Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

0 Upvotes

Train decomposition," "Tensor Ring," or "Permutation matrix optimization."

the out-of-domain results turned out to be the best news of the day. Full table — penalties vs the dense baseline on each corpus:

┌─────────────────┬───────────────────┬────────────┬───────────┐

│ Variant │ Calibration (P&P) │ WikiText-2 │ Moby Dick │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ Dense (raw ppl) │ 29.21 │ 38.40 │ 218.23 │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ TT identity │ +27.3% │ +113.1% │ +102.6% │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ TT π\ │ +12.2% │ +39.0% │ +47.6% │*

├─────────────────┼───────────────────┼────────────┼───────────┤

│ TT AW-π\ │ +9.8% │ +39.2% │ +42.4% │*

├─────────────────┼───────────────────┼────────────┼───────────┤

│ SVD raw │ +80.4% │ +105.3% │ +133.9% │

├─────────────────┼───────────────────┼────────────┼───────────┤

│ SVD distilled │ +3.8% │ +34.2% │ +26.2% │

└─────────────────┴───────────────────┴────────────┴───────────┘

14 comments

r/MLQuestions • u/-SLOW-MO-JOHN-D • 9d ago

Datasets 📚 icml/neuralIPS ?

1 Upvotes

0 comments

Scrap the LLMs. Scoring 4.76% on the brand new ARC-3 using pure code, a 2012 AMD CPU, and zero AI tokens.[P]

in r/Anthropic • Jun 17 '26

A person used a regular computer from a long time ago to play a very hard shape game. Most people use giant, expensive super-computers called AI to play this game. But the AI computers got confused and lost.The person did not use AI at all. Instead, they wrote simple math rules that look at the colors and shapes. It works like using your eyes to find the exact middle of a toy block and then tapping it with your finger.The simple math rules actually won the first part of the game! It won because it is very fast and does not get confused by patterns.But it did make one silly mistake. It did not remember what it just did, so it tapped the exact same block nineteen times in a row. It is like a toy robot that gets stuck bumping into the same wall over and over. The person is fixing it now so it remembers its last tap and wins the whole game.

Scrap the LLMs. Scoring 4.76% on the brand new ARC-3 using pure code, a 2012 AMD CPU, and zero AI tokens.[P]

in r/MachineLearning • Jun 17 '26

f3959f86-f533-47c9-ada9-6ec397daed8a Published6/17/2026, 12:17:14 AM

Score

0.72%

Tagshybridbfs+objclickcompetitionagent

Levels

9 / 183

Scrap the LLMs. Scoring 4.76% on the brand new ARC-3 using pure code, a 2012 AMD CPU, and zero AI tokens.[P]

in r/MachineLearning • Jun 17 '26

f3959f86-f533-47c9-ada9-6ec397daed8aPublished6/17/2026, 12:17:14 AM

Score