• Semantic Indexing: Scaling Proofs as Programs

    From Mild Shock@janburse@fastmail.fm to comp.lang.prolog on Wed Jul 16 11:54:47 2025
    From Newsgroup: comp.lang.prolog

    Hi,

    Maybe they should double check what
    modern compilers do or what modern IDEs
    to in object orient programming languages.

    How it started:

    How to Make Ad Hoc Proof Automation Less Ad Hoc https://people.mpi-sws.org/~beta/lessadhoc/

    How its going:

    Optimizing Canonical Structures https://inria.hal.science/hal-05148851v1/document

    The original paper termed canonical structures,
    it has a nice visa à visa. “Type Class” Programming
    versus “Logic” Programming,

    giving it a less functional programming
    spin. But hell wasn't there Prolog++ already
    in the past?

    The newest paper shows new style of research,
    citing garbage tickets from TurdPit inside
    a paper, and just listing some further

    untested and shallow research Turds. Kind
    of institutionalize Trial & Error. They could
    equally well shoot their own Foot and

    then jump in circles.

    Bye
    --- Synchronet 3.21a-Linux NewsLink 1.2
  • From Mild Shock@janburse@fastmail.fm to comp.lang.prolog on Wed Jul 16 12:08:22 2025
    From Newsgroup: comp.lang.prolog

    Hi,

    Maybe somebody should tell Macron that there is
    a Stargate project now. Instead using TurdPit for
    human Trial & Error of chipmunks, Lean prover from
    Microsoft has some interesting projects actually:

    LeanDojo (Stanford / Meta AI collaborators)
    Turns L∃∀n into a reinforcement learning environment for LLMs.
    Train AI to interact with the L∃∀n prover like a human. https://github.com/lean-dojo/LeanDojo

    ProverBot9001
    A transformer-based model that learns to generate
    L∃∀n proofs. Focused on learning from L∃∀n's mathlib
    and applying fine-tuned techniques.
    Uses encoder-decoder LLMs and proof search.

    Autoformalization of Math
    Stanford, OpenAI, and DeepMind folks have tried
    autoformalizing math text (LaTeX or natural language)
    into L∃∀n. Ongoing effort to turn the “arxiv math paper” →
    “Lean formal proof” into a pipeline.

    GPT Agents for Lean
    Some work on tool-augmented LLMs that run L∃∀n, read
    the output, and continue the proof.
    Think: GPT as an agent in a loop with L∃∀n — tries
    a tactic, checks result, adapts.
    Experimental, but shows promise.

    Mathlib + tactic-state datasets
    Hugely valuable structured data from mathlib for
    training and evaluating AI models. Models can learn
    by seeing before-and-after proof states. Some teams
    are working on LLMs that can select the next tactic
    by learning from these states.

    Have Fun!

    Bye

    Mild Shock schrieb:
    Hi,

    Maybe they should double check what
    modern compilers do or what modern IDEs
    to in object orient programming languages.

    How it started:

    How to Make Ad Hoc Proof Automation Less Ad Hoc https://people.mpi-sws.org/~beta/lessadhoc/

    How its going:

    Optimizing Canonical Structures https://inria.hal.science/hal-05148851v1/document

    The original paper termed canonical structures,
    it has a nice visa à visa. “Type Class” Programming
    versus “Logic” Programming,

    giving it a less functional programming
    spin. But hell wasn't there Prolog++ already
    in the past?

    The newest paper shows new style of research,
    citing garbage tickets from TurdPit inside
    a paper, and just listing some further

    untested and shallow research Turds. Kind
    of institutionalize Trial & Error. They could
    equally well shoot their own Foot and

    then jump in circles.

    Bye

    --- Synchronet 3.21a-Linux NewsLink 1.2