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chaisan

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投稿

Autoresearch for Integer Factorization

github.com
4 ポイント·投稿者 chaisan·3 か月前·0 コメント

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1 ポイント·投稿者 chaisan·4 か月前·0 コメント

[untitled]

1 ポイント·投稿者 chaisan·4 か月前·0 コメント

[untitled]

1 ポイント·投稿者 chaisan·4 か月前·0 コメント

Autoresearch for SAT Solvers

github.com
167 ポイント·投稿者 chaisan·4 か月前·32 コメント

Autoresearch for SAT Solvers

github.com
3 ポイント·投稿者 chaisan·4 か月前·1 コメント

Monitor your world with one daily report

monitorish.com
1 ポイント·投稿者 chaisan·5 か月前·1 コメント

コメント

chaisan
·3 か月前·議論
we moved our whole org off Vercel after that selfie Rauch put out. rotten company, overpriced product for what it is, sneaky practices. never looked back.
chaisan
·4 か月前·議論
have examples?
chaisan
·4 か月前·議論
nice. for which problem?
chaisan
·4 か月前·議論
yess. loads of space for further exploration here. there is an attempt to keep things as general as possible in the expert.md file, but hard to mitigate overfitting fully. however, changing the seed will not get you much further with all else in the solver constant. unless you try a number of seed that exponentially scales with the size of the problem
chaisan
·4 か月前·議論
sure. in the limit, everything is parameter tuning. with large enough NP-hard problems, the complexity of the search space is big enough that its infeasible to get to a better state by just tuning params in any reasonable amount of time.
chaisan
·4 か月前·議論
wrt. token usage?
chaisan
·4 か月前·議論
somewhat
chaisan
·4 か月前·議論
and it would take an algo change to the solver to jump to the next local optimum
chaisan
·4 か月前·議論
yeh. ofc. but on any problem larger than 40 variables, the gains from random restarts or initializations will quickly plateau
chaisan
·4 か月前·議論
the sum of the weights of the unsatistied clauses. we want to reduce this number
chaisan
·4 か月前·議論
as its from 2024 (MaxSAT was not held in 2025), its quite likely all the solvers are in the training data. so the interesting part here is the instances for which we actually got better costs that what is currently known (in the best-cost.csv) file.
chaisan
·4 か月前·議論
its just comparing the cost of the best solution found to the best known cost we had before. O(N). why optimistic?
chaisan
·4 か月前·議論
nice. EDA indeed one of the top applications of SAT
chaisan
·4 か月前·議論
An autonomous AI agent that teaches itself to become the world's top expert on MaxSAT. Given weighted MaxSAT instances, it learns novel strategies, finds better solutions and iteratively refines its toolbox. No human guidance.
chaisan
·5 年前·議論
Ntropy | https://ntropy.network/ | ML & Backend engineers | SF Bay Area / London, UK / Remote | Full-time

Ntropy is building a data layer to combine financial transactions from across organizations in a scalable and privacy-preserving way.

You will be one of the first hires joining the team of 6 (2 founders + 4 engineers). We are well funded and will be scaling up to 10-12 people over the next 4 months.

See https://jobs.ntropy.network

stack: Python / Rust / lots of GPUs