It proposes a research program using #AI to tackle the longstanding problem of many free parameters of Standard Model.
01.12.2025 17:46 β π 0 π 0 π¬ 0 π 0@hakankj.bsky.social
Swedish software developer (retired), Independent Researcher mostly in in Constraint Programming and Logic Programming, esp. Picat and Prolog. Also interested in probabilistic programming, inductive programming, math, AI. Homepage: http://hakank.org/
It proposes a research program using #AI to tackle the longstanding problem of many free parameters of Standard Model.
01.12.2025 17:46 β π 0 π 0 π¬ 0 π 0Our (Sergei Chekanov and me) physics paper "Discovering the Underlying Analytic Structure Within Standard Model Constants Using Artificial Intelligence" has now been accepted and published in the Particles journal: www.mdpi.com/2571-712X/8/... .
01.12.2025 17:45 β π 0 π 0 π¬ 1 π 0The Picat PPL page includes documentation of the features of Picat PPL as well as over 550 probabilistic programming models. (In total, it's now over 115 000 lines, including comments and output of the model.)
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The dedicated page for Picat PPL is hakank.org/picat/ppl .
and at GitHub: github.com/hakank/hakan... .
Also, the published files can be downloaded from the Zip file: www.hakank.org/picat/ppl/al... . This file will be updated when changes are done.
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It has two features:
- support for doing probabilistic programming modelling using a fairly high level syntax (such as Gamble and WebPPL)
- support of over 40 different probability distributions (or about 80 depending how one count) with exact calculations, upto Picat's precision.
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It is designed for exploring uncertainty, solving probability puzzles, and experimenting with small-scale probabilistic models - rather than performing heavy Bayesian data analysis.
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Picat PPL (hakank.org/picat/ppl) - Probabilistic Programming Light is is a lightweight probabilistic programming framework implemented entirely in Picat (picat-lang.org).
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This is based (and extended) on the findings and data from our first physics paper (preprint): S. V. Chekanov, H. Kjellerstrand: "Discovering the underlying analytic structure within Standard Model constants using artificial intelligence"(arxiv.org/abs/2507.00225 )
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but instead follow a hierarchical structure within a high-dimensional functional space. The found analytic solution depends on only two input parameters, representing the simplest mathematical model that could provide a foundation for developing a future theoretical framework to address the SM.
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We outline the core principles of our method and identify the simplest analytic relationships among SM parameters. Our results suggest that the SM parameters associated with quark and boson masses are not randomly distributed,
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Abstract: This paper presents an approach to reducing the number of fundamental parameters in the Standard Model (SM) using genetic programming, a machine learning technique based on evolutionary algorithms.
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Our second physics paper (preprint) is now available at arXiv: S. V. Chekanov (@svchekanov), H. Kjellerstrand (@hakankj): "Evidence of Relationships Among Fundamental Constants of the Standard Model" (arxiv.org/abs/2509.07713)
Abstract: See next comment.
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Here's the GitHub repo of the source code of the #Picat SAT FlatZinc solver fzn_picat_sat used in the MiniZinc Challenge 2025: github.com/hakank/fzn_p... .
For the result of the #MiniZinc Challenge 2025: www.minizinc.org/challenge/20... . (As mentioned earlier, the Picat SAT solver got two Silver.)
For some examples of how to use the bv module:
* Section "4 Day 17: Chronospatial Computer" of our ICLP'25 paper Picat Through the Lens of Advent of Code (arxiv.org/abs/2507.11731)
* My examples at hakank.org/picat/#bv
GitHub commit of these models: github.com/hakank/hakan...
#Picat version 3.9 has been released: picat-lang.org
Updates: picat-lang.org/updates.txt
It contains improvements of the SAT solver and - which is really cool - a bit vector constraint module which makes it possible to use arbitrary sized domains (> 2**56) in constraint models.
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Results from the #MiniZinc Challenge 2025: minizinc.org/challenge/20...
As earlier years, Google OR-tools got all Gold medals. We (#Picat team) got two Silver (again). Choco: 1 Silver, 1, Bronze; Yuck: Silver; SICStus Prolog, Pumpkin, iZplus, and Atlantis: 1 Bronze.
Congrats to all!
Puzzle creator Jim Bumgardner (aka krazydad, krazydad.com) is "vide coding" a puzzle solver/generator using Constraint Programming (w Google OR-tools): "Lets make minesweeper sudoku part 2" (www.youtube.com/watch?v=aNdT... ).
His other YT videos: www.youtube.com/@jbum
Our paper βDiscovering the underlying analytic structure within Standard Model constants using artificial intelligenceβ by Sergey Chekanov (anl.gov/profile/serg...) and myself (hakank.org) is at arXiv: arxiv.org/abs/2507.00225, in the category hep-ph.
02.07.2025 06:18 β π 1 π 0 π¬ 0 π 0It is a Python program but since it's a beta (about v0.000006), I'll not publish it.
If you're interested, please DM (or mail me).
Earlier today I read about Warp Terminal (warp.dev) and it looked neat. However, it uses a remote AI for the query.
Since I rather want to use my local LLM (via LM Studio), ChatGPT (4o) and I did Terminal Assistant instead. Examples: hakank.org/terminal_ass...
Just for fun, I also asked Google Gemini to do a Deep Research on this problem: docs.google.com/document/d/1...)
and NotebookLM Deep Dive podcast: notebooklm.google.com/notebook/280...
Note that the paper also discusses variants to this puzzle.
Here's my (new) Litmus test of LLMs: The No change problem (from medium.com/puzzle-spher... )
#Picat program: www.hakank.org/picat/no_cha... showing two constraint approaches (and a brute force variant).
LLMs might get one of the two solutions, but often only after a lot of corrections.
And I did another Research with Google AI Studio but restricted it to be only on Einstellung Effect. Here's the generated document: docs.google.com/document/d/1...
And then using Google's NotebookLM to create a neat 2 person discussion about the document: notebooklm.google.com/notebook/ef8...
In March 2023, I tested GPT4 with a couple of traditional ILP (Inductive Logic Programming) problems, and some other problems: hakank.org/gpt/
Yesterday, I checked how GPT 4o would do: chatgpt.com/share/67d479...
In summary: quite much better.
The other day I happen to read about Einstellung effect (en.wikipedia.org/wiki/Einstel...) and thought that it seems to be one of the problems that are in LLMs.
So I asked GPT 4.5 + Deep Research for a report on LLM Cognitive Biases Research. Here's the report: chatgpt.com/share/67d47b...
#Picat version 3.8 is available: picat-lang.org/download.html
picat-lang.org/download.html
Updates: picat-lang.org/updates.txt
E.g. new if/then/else syntax variant, improved error handling, debug flag (-d) at command line, new between/4, and some other bug fixes.
Unfortunately. Pop-11 is a fun language and the Poplog distribution contains a huge number of examples of GOFAI (Good Old Fashion AI) that nobody seems to care about nowadays...
Here's my Poplog page:
www.hakank.org/poplog/
Nope, I learned about it some weeks ago so it's on my TODO list. It does seems fun.
One must love a language that has a built-in function for shortest path (www.uiua.org/docs/path). When I browsed the site some weeks ago it had astar but that's now deprecated (in favor of path).
Are you in APL/J/K?
Thus there are now two "CP" in my life. The real (and for me the first): Constraint Programming, and now Competitive Programming.
I hope that will not confuse neither you nor me.
So today I have toggled the autocomplete off. What a mental relief!
26.01.2025 19:07 β π 1 π 0 π¬ 1 π 0