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fuglede_

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welter.fuglede.dk 1mo ago

Show HN: Welter's game, a little Nim-like combinatorial game

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twitter.com 1mo ago

Terence Tao's promotional video for OpenAI

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www.youtube.com 1mo ago

The Unsolved Lollipop Problem [video]

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www.youtube.com 2mo ago

Terry Tao – New Mathematical Workflows – Future of Mathematics Symposium

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2pts0
davidbessis.substack.com 2mo ago

The Fall of the Theorem Economy

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3pts1
www.nvidia.com 3mo ago

Nvidia Ising – Open AI Models for Quantum Computing

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1pts0
arxiv.org 3mo ago

Exponential quantum advantage in processing classical data

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itsid.cloud 3mo ago

Itsid – LLM with perfect input reproduction for e.g. license removal

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2pts0
arxiv.org 5mo ago

Reducing the cost of breaking RSA-2048 to 100000 physical qubits

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3pts0
scottaaronson.blog 8mo ago

Quantum computing: too much to handle

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8pts0
arxiv.org 8mo ago

Helios: A 98-qubit trapped-ion quantum computer

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1pts0
swapple.fuglede.dk 9mo ago

Show HN: Swapple, a little daily puzzle on linear reversible circuit synthesis

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72pts34
arxiv.org 10mo ago

Unconditional separation between quantum and classical information

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2pts0
scottaaronson.blog 10mo ago

Quantum Information Supremacy

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3pts0
old.reddit.com 1y ago

Advent of Code: Discussion on LLM Cheaters

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2pts0
arxiv.org 1y ago

Exponential Separation Between Quantum and Quantum-Inspired Algorithms for ML

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1pts0
www.youtube.com 1y ago

The Man Who Found the World's Biggest Prime - Numberphile [video]

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2pts0
www.mersenne.org 1y ago

GIMPS Discovers Largest Known Prime Number: 2^136279841 – 1

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30pts11
www.mersenne.org 1y ago

Gimps Discovers Largest Known Prime Number: 2¹³⁶²⁷⁹⁸⁴¹ − 1

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3pts1
fuglede.github.io 2y ago

Llama.ttf: A font which is also an LLM

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608pts154
arxiv.org 2y ago

Sublinear Time Shortest Path in Expander Graphs

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github.com 3y ago

SciPy 1.9.0

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github.com 4y ago

HiGHS: High performance open source MILP and QP solver

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github.com 4y ago

Highs-JS: High Performance MILP and QP Solver in 2MB of WASM

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github.com 5y ago

CondaNSIS: Windows installers for conda-powered Python applications

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2pts0

Right; see Lakatos. In its roughest form, you study the structure of whatever counterexamples you find, add those as (negated) preconditions to your proposition, rinse and repeat until you have a true statement. If the proposition remains useful, you now have a new definition.

There are ways to make it work, but it requires using it only supplementary and with strict discipline. And it will look different for a 6-year old than a 13-year old.

But think of this trick we use about turning a tricky research paper in maths/science into something more tangible by making an LLM whip up an interactive version. That works at every level of education, and it means that you can completely tailor a piece of educational material to the kid

Tiny example: one kid was introduced to fractions and found it abstract that it was both about partitioning stuff and about numbers on a number line. So while we were practicing, I had an LLM make https://fuglede.codeberg.page/broeklegeplads/ to make it more hands-on.

Obviously for the small kids, this has to be an experience guided by teachers and parents, but for bright older kids with sufficient discipline that ought to be a useful trick for enhancing education.

Of course when we were kids, we would just write such educational programs ourselves and get the same effect /and/ learn to program (before getting banned from the computer room for putting spooky /binaries/ on the computers anyway), so maybe that's better for older kids. And maybe these kids will never have to do any maths or programming because the AI overlords have taken over when they grow up.

I've reached for this solution many times too; it's certainly convenient.

What I don't like is that all the resulting pages look the same. It's so easy to tell when a page is authored by Claude, and by now, I get the same “ugh another one” feeling as Dall-E generated images would give when they were completely overdone.

Maybe it's just be, but if not, maybe HTML generated outputs will also end up being dialed down from where they're at now.

But maybe not, since it's obviously a useful thing to be able to do. I wonder if there's a way out. To be able to introduce some natural entropy so not everything ends up looking the same. I guess not, since we're using machines whose natural mode of behavior is regression toward the mean. And maybe having it be harder to tell the author wouldn't even be desirable anyway.

It would be very helpful to know in understanding the capabilities of the models; and in getting intuition about where they are best applicable.

If the reason it was able to output the proof is that it happened to be included in an in-house university report written in Georgian, then that would make it less useful for research than if it's new entirely.

The XOR swap trick also features in the compilation/synthesis of quantum algorithms, where the XOR instruction (in the form of a CNOT gate) is fundamental in many architectures, and where native swapping need not be available.

One extension that I ran into, and which I think forms a nice problem is the following:

Just like the XOR swap trick can be used to swap to variables (and let's just say that they're bools), it can be extended to implement any permutation of the variables: suppose that the permutation is written as a composition of n transpositions (i.e., swaps of pairs), and that is the minimal number of transpositions that let's you do that. Each transposition can be implemented by 3 XORs, by the XOR swap trick for pairs, and so the full permutation can be implemented by 3n XORs. Now here's the question: Is it possible to come up with a way of doing it with less than 3n, or can we find a permutation that has a shortcut through XOR-land (not allowing any other kinds of instructions)? In other words, is XOR-swapping XOR-optimal?

I'm not going to spoil it, but only last year a paper was published in the quantum information literature that contains an answer [0]. I ended up making a little game where you get to play around with XOR-optimizing not only permutations, but general linear reversible circuits. [1]

[0] https://link.springer.com/article/10.1007/s11128-025-04831-5

[1] https://swapple.fuglede.dk

There's some good discussion here: https://old.reddit.com/r/adventofcode/comments/1h9cub8/discu...

But yes, you would have people openly share repositories for automatically ingesting the puzzle text, solving the puzzles, and submitting the results the moment the puzzles opened, leading to inhuman solution times. So, despite the rules stating that you can't do that, the result is that whenever the puzzles were easy enough for an LLM to solve them with high probability – and most of them are – the leaderboards would be overrun with such solutions.

In 2023, the LLMs would still struggle enough that the overall leaderboard (taking all 25 ⋅ 2 puzzles into account) would still be dominated by ordinary people (many of them recording their solutions), in 2024 that was no longer the case. Personally I would go from being able to top 100 regularly, to almost never being able to. I'm going to miss the thrill, and think it's a bit saddening that we can't have nice things, but also ultimately think that getting rid of it is the best option.

That's nifty! There's a lot of symmetry that can help to boil it down. For example, you actually only need row moves, and any solution with column moves can canonically be turned into one with row moves; post-composing with Ci→j is pre-composing with Rj→i.

One can think of the set of all possible board configurations as the vertices as a graph, with edges indicating how to move between configurations. Then your 1536 solutions are the 1536 distinct shortest paths between the starting and target configuration.

Then, you can also choose to consider not just board configurations, but board configurations up to simultaneous permutation of rows and columns; that will also reduce the number of unique solutions.

Thanks for sharing your thoughts!

I know of some work on trying out various heuristics for A*; Section 5 of https://arxiv.org/pdf/2201.06508 gives some examples of what works and what doesn't. I don't think D* Lite specifically has ever featured. There's plenty of room for trying to come up with other heuristics, and just for other takes in general.

But for bigger boards you won't be able to materialize the whole graph.

If we restrict to boards corresponding to solvable puzzles, the number of vertices is https://oeis.org/A002884 (1, 1, 6, 168, 20160, 9999360, 20158709760, …) and indeed grows quickly. It's possible to manipulate the 7×7 case (https://arxiv.org/abs/2503.01467, shameless plug) but anything bigger than that seems hard.

One can ask, for example, how many moves are needed for the hardest n×n Swapple. For n = 1, …, 7 the answers are 0, 3, 6, 9, 12, 15, 18 respectively, but we don't know what the answer is for n = 8.

Heh, nice catch, I think you'll find that with a bit of work, you can make row reduction/Gaussian elimination work here as well. But that the resulting sequences of operations can get very long! One thing I personally like about the puzzle is that once you've played it for a few days, you start gaining some intuition about sequences of moves that are useful, but coming up with a good general algorithm (that also works for larger than 4x4 boards) is still a challenge.