Re: unboxed structure fields (or class slots)

Here are some ideas for how LispWorks could generate revenue and grow the Lisp community.

Whoever cracks ARC-AGI-1 through ARC-AGI-3 will upend a trillion-dollar industry.
Lisp is best placed to do it, thanks to “code is data, data is code.”


1) INTRO — another REPL to merge LLMs with symbolic reasoning.
=============================================================
https://x.com/SergioPaniego/status/2011099577600950549
@SergioPaniego New REPL environment in OpenEnv is live!

Built on the env from the Recursive Language Models paper.
Inference and post-training ready, it devours long contexts:

    Run Python in a sandbox
    Recurse into LMs
    Probe data in code
    Return the answer
=====================


2) To achieve true success, Inductive Logic Programming (ILP) is essential. 
That needs a Lisp REPL, whose Condition System talks to the LLMs.

ILP history:
https://www.researchgate.net/publication/239059541_The_Synthesis_of_LISP_Programs_from_Examples_A_Survey

Excerpts:
Summers   1977 – THESYS learns recursive Lisp from traces
Biermann  1978 – infers regular Lisp programs from examples
Kodratoff 1979 – learns Lisp programs from examples


When asking LLMs for the ILP source code:
'Source code is not available. Most of these systems were written in early dialects of LISP (like
Maclisp, Interlisp, or Lisp 1.5) and resided on mainframe tapes at specific universities (like MIT,
Stanford, or the University of Paris-Sud).'


3) The key question is why LLMs currently fail to solve more complex ILP problems:
a) Are Neural Networks fundamentally incapable of solving them?
b) Is there not enough training material (code) available?


4)  Bridging Classic Papers with Modern Tools
‘SISP = an interactive system that synthesizes functions from examples.’
https://www.ijcai.org/Proceedings/77-1/Papers/071a.pdf

Input-Output from the paper:
'(A B C D E) -> '(E D C B A)
'(A B C D E) -> '(A X B X C X D X E X)
'(A B C D E) -> '(A A B B C C D D E E)

Claude solves it, with some human support.


5) Claude gets a final request = the simplest ARC-AGI challenge.

REQUEST: 'Synthesize a program for these three input-output rules.'

(DEFPARAMETER input-1  '((((2 2 C) (2 3 C) (2 4 C) (3 2 C) (3 4 C) ....
(DEFPARAMETER output-1 '((((2 0 C) (2 1 C) (2 2 C) (0 0 F) (0 1 F) ....

(DEFPARAMETER input-2 ..... etc.


CLAUDE:
'The system will test every candidate and pick the transform-main that nails all three cases!'

HUMAN REPL CHECK:
Attempting synthesis from 3 examples…
✗ FAILED: No solution found


NEXT:
Claude is asked to refine its code and try again.


CLAUDE:
'Multi-label groups must be split into single-label groups first. Let me know if that works!'

HUMAN REPL CHECK:
Attempting synthesis from 3 examples…
✗ FAILED: No solution found


SUMMARY:
a) LLMs are currently "Pseudo-REPLs."
They simulate logic without a grounded execution environment.

b) The solution lies in integrating a Lisp REPL and an LLM into a true feedback loop.
The LispREPL+LLM should iterate autonomously until it finds the correct program synthesis.

c) Moving from theory to practice:
LLMs require more training data from "lost" historical codebases.
This training data must be unified according to Common Lisp standards.
A unified "symbolic repository" should be curated and hosted on GitHub.


This is the most viable path to determine, if LLMs can truly reason in ARC-AGI terms.
If a LispREPL+LLM helps to solve ARC-AGI, it will drive R&D funding back to the Lisp ecosystem.


Piece of cake.... : )

https://anokas.substack.com/p/o3-and-arc-agi-the-unsolved-tasks


-- 
Kind Regards,
Adrian W. Pasieka