Makes me wonder if future llms will be composing nonlinear things and be able to work in non-token-order spaces temporarily, or will have a way to map their output back to linear token order. I know nonlinear thinking is common while writing code though. current llms might be hiding a deficit by having a large and perfect context window.
In some measure of agreeing with you: For other classes of models we know for a fact that there exist problems which can be solved by those architectures and which can't be trained using current techniques. It doesn't feel like a huge stretch that such training-resistent data might exist for diffusion models.
That said, I still see three problems. Notably, the current ancestral chain of inquiry seems to care about the model and not the training process, so the point is moot. Secondarily, in other similar domains (like soft circuits) those organic solutions do seem to emerge, suggesting (but not proving) that the training process _is_ up to par. Lastly, in other related domains, when such a solution doesn't emerge it ordinarily happens because some simpler methodology achieves better results, meaning that even with individual data points suggesting that diffusion solutions don't model that sort of linearity you still need to work a little bit to prove that such an observation actually matters.
That's interesting. Is there research into adding memory or has it been proven that it provides no pragmatic value over any context it outputs?
Shouldn't be hard to train a coding LLM to do this too by doubling the training time: train the LLM both forwards and backwards across the training data.
Is there a top 100 package that does something funny on import?
My guess is that nearly all packages that did this sort of thing were left behind in the 2-to-3 migration, which a lot of us used as the excuse for a clean break.
But I agree that observable side effects are generally pretty rare. And apparently, both libraries are not even in the top 100 packages, depending on how you count. It looks like those spots are all taken by libraries used in uncached, wasteful CI workflows: https://hugovk.github.io/top-pypi-packages/
But still a bummer, to be sure. It's easy enough for me to say it doesn't count when I haven't been affected by it.
This is buried deep in the PyTorch docs and I don’t have the willpower to go find it right now, sorry.
import matplotlib
import PIL
im = PIL.Image.open("foo.png") import PIL.Image
I'd love to see some quantification of errors in q/kdb+ (or hebrew) vs. languages of similar size that are left-to-right.
I'm convinced that's the case. On any major LLM I can carpet bomb Java/Python boilerplate without issue. For Rust, at least last time I checked, it comes up with non-existing traits, more frequent hallucinations and general struggle to use the context effectively. In agent mode it turns into a first fight with the compiler, often ending in credit destroying loops.
And don't get me started when using it for Nix...
So not surprised about something with orders of magnitude smaller public corpus.
There may be emergent abilities that arise in these models purely due to how much information they contain, but I'm unconvinced that their architecture allows them to crystallize actual understanding. E.g. I'm sceptical that there'd be an area in the LLM weights that encodes the logic behind arithmetic and gives rise to the model actually modelling arithmetic as opposed to just probabilistically saying that the text `1+1=` tended to be followed by the letter `2`.
Did your experiment consist of asking an LLM to design a programming language for itself?
That's not really an experiment is it? You basically just used them to create a hypothesis but you never actually proved anything
They're great at writing text and code so the fact that the other LLM was able to use that syntax to presumably write code that worked (which you had no way of proving since you can't actually run that code) doesn't really mean anything
It would be similar to having it respond in a certain JSON format, they are great at that too. Doesn't really translate to a real world codebase
> That's not really an experiment is it? You basically just used them to create a hypothesis but you never actually proved anything
> They're great at writing text and code so the fact that the other LLM was able to use that syntax to presumably write code that worked (which you had no way of proving since you can't actually run that code) doesn't really mean anything
This isn't a published study, it was an experiment. And it influenced how I use LLMs for work, for the better. I'd even call that a successful experiment, now that I better understand the strengths and limitations of LLMs in this field.
How so?
LLMs need all their context within easy reach. An LLM-first (for editing) language still has code comments and docstrings. Identifier names are long, and functions don't really need optional parameters. Strict typing is a must.
for nix, i is nice template engine to start or search. did not tried big nix changes.
From the perspective of a LLM learning from Unicode, this would appear as a delimeter that needs to be inserted on language direction boundaries; but everything else should work the same.
Everything is written sequentially in the sense that the character that is written first can only be followed by the character that is written next. In this sense writing non-sequentially is logically impossible.
Exercise to the reader to guess how line breaks, text wrapping, and search algorithms worked.
So, in the word “gif”, they would start writing the “f” first and finish writing the “i” first (just before writing the last part of the “f”. For “if”, writing the “f” would start before writing the “i” started and finish after writing the “i” finished.
In traditional printing “writing” can happen simultaneously for an entire page, but colour printing can make things more complex.
Basically, the numbers 1234 and 4321 are identical assuming one is written left to right and the other is right to left. Then it's just a convention which way you are used to reading.
I know nothing of Old (or New) Hebrew unfortunately so I may be completely off base.
1: https://www.thoughtco.com/given-before-new-principle-linguis...
W.r.t. natural languages, TFA clarifies it a bit:
> And it’s not the same as translation to Arabic or Hebrew; direction here refers to the temporal order in which the tokens are produced; even for right-to-left languages, the order in which the tokens get produced remains unchanged; rather, a thin display layer handles the visual presentation.
It's actually not, and unless they in some way run a rule engine on top of their LLM SaaS stuff it seems far fetched to believe it adheres to rule sets in any way.
Local models confuse Python, Elixir, PHP and Bash when I've tried to use them for coding. They seem more stable for JS, but sometimes they slip out of that too.
Seems pretty contrived and desperate to invent transpilers from quasi-Python to other languages to try and find a software development use for LLM SaaS. Warnings about Lisp macros and other code rewrite tools ought to apply here as well. Plus, of course, the loss of 'notation as a tool of thought'.
My question: is there a way to reduce cognitive load in LLMs?, one solution seems to be process the input and output format so that the LLM can use a more common format. I don't know if there is a more general solution.
Edit: Cat attack https://the-decoder.com/cat-attack-on-reasoning-model-shows-...
That's like reading binary for humans. 1s and 0s may be the simplest possible representation of information, but not the one your wet neural network recognizes.
There can be considerable complexity in Lisp abstract syntax.
import numpy as np
def flippedSubtract(a, b): return b - a
flipSubUfunc = np.frompyfunc(flippedSubtract, 2, 1)
def isDivBy11(number): digits = list(map(int, str(number))) discriminant = flipSubUfunc.reduce(digits) return (discriminant % 11) == 0
Though Claude already understands (has already seen?) 0=11|-/d so it's hard to tell for this example
As for the cat attack, my gut feeling is that it has to do with the LLM having been trained/instructed to be kind
My hot take is that Iverson was simply wrong about this. He couldn't be expected to predict code completion and then LLMs both wanting later tokens to depend on earlier tokens. SQL messed it up, too, with "from" not coming first. If APL were developed today, I think left-to-right evaluation would have been preferred. The popularity of dotted function calls in various languages makes it reasonably clear that people like tacking things onto the end and seeing a "pipeline" form from left to right.
The 10 by 10 reshaping of counting to 100
In fact, for many things, you so the math way in numpy as well. But in other things, the dot/object-oriented way is preferred.
APL is just consistent, terse, mathematical notation.
Your insight about APL being reverse-concatenative is very cool.
But it's not a good idea to use regexes in code that you're going to use long term. It's justifiable for simple regexes, and many people go against this advice, but really for anything remotely complex regexes become totally unreadable and extremely bug prone. Complex regexes are a huge code smell and array languages are pretty much one enormous regex.
I wrote something like that in C# once [0] but I'm not getting the impression that there's a lot of demand for that kind of thing.
[0] https://share.unison-lang.org/@unison/website/code/main/late...
https://github.com/oils-for-unix/oils/wiki/Alternative-Regex...
I haven't actually used them because generally the right alternative to a regex is a proper parser.
I think it's obvious that Cyrillic isn't any less usable than the Latin alphabet in any objective sense. In fact, I'm using English orthography, which has all kinds of unnecessary usability problems which aren't present in any Cyrillic orthography that I know of. But familiarity is a much stronger factor; even today I can barely sound out words in Russian or Ukrainian, while English text printed in Latin letters is clearer to me than speech.
On theoretical grounds, I suspect that the APL syntax Gabi is calling RL-NOP is less usable for left-to-right readers than at least LR-NOP and maybe even conventional Please Brutally Execute My Dear Aunt Sally operator precedence. But familiarity is such a strong force that this hypothesis is very difficult to test.
The theoretical grounds are that, when reading left to right, a reader must maintain a stack of pending operators and values in their mind, unless they are saved by parentheses. (The Iverson quote disagrees with this, but I think Iverson was wrong.) Maintaining mental stacks is difficult and error-prone; this is the reason for the Tim Peters proverb, "Flat is better than nested."
I suspect that operator precedence might be superior for two reasons:
1. It more often avoids parentheses, which are extra symbols to recognize and correctly pair up in your mind.
2. The meaning of high-precedence subexpressions like `x×b` are almost context-independent—although an exponentiation operator or something like a C struct field selector could still follow `b` and change its meaning, following multiplications, divisions, additions, subtractions, or comparisons will not, and preceding additions, subtractions, or comparisons also will not. I conjecture that this facilitates subconscious pattern recognition.
But the familiarity factor enormously outweighs these theoretical considerations for me.
On the contrary, I find it much more usable for left-to-right readers, because it allows a "top-down" reading of the expressions, instead of a "bottom-up" reading.
When trying to understand an unfamiliar program, for debugging or maintenance, you normally do not want to waste time by reading completely all expressions, which provide irrelevant computation details.
You typically search where some variables are modified and how and why. For this it is frequently enough to look only at the last operations that have been performed before storing a modified value into a variable.
With the Iverson notation, the last operations are always conveniently grouped at the left side of a text line. Thus you read from left to right only as much as necessary to find what you need, then you can skip the rest of the line.
With the school notation, the required information is not grouped at one end of the line, so reading becomes slower.
The opposite of the Iverson notation, which was used in some stack-oriented languages, also groups the information, but in a way that is less usable for left-to-right users.
From natural languages, left-to-right readers expect that a sentence starts with its topic (at the left side), i.e. the most important part, e.g. the last assignment, like in the Iverson notation, instead of ending with its topic, like in the opposite notation.
> "a reader must maintain a stack of pending operators and values in their mind"
I believe that few readers, if any, do this.
The normal case when reading is that you do not want to reproduce in your mind what the computer does, but only to find the information flows between program variables. For this, it is enough to read partial expressions, as explained above.
In the very rare case when you wanted to make a mental calculation identical to that of the computer, you would normally read the expression from right to left.
When writing, the Iverson notation is usually more convenient than the school notation, while writing normally, from left to right. The reason is that for most computations the natural way to find the expression that must be computed is to go backwards, from the desired result towards the available data.
Everybody learns in school the traditional convention for writing mathematical expressions.
It appears that for most people it is difficult or impossible to unlearn later such a convention, even if they encounter a superior convention.
On the other hand, I am among those fewer for which this is not true, so when I have first read the book "A Programming Language" of K. Iverson, on which the later APL language and its successors have been based, I have immediately recognized that the Iverson convention is much better than the school convention, and I have no trouble in using it.
When reading a program written with the Iverson convention, you still read from left to right, but you typically do not read until the end of the line, but only as much of the left part as necessary to understand the purpose of the line. (Because the right operand of any operator is everything that follows it until the end of the line, and the details of that computation may be irrelevant. With school notation, when searching where a variable has been modified and how, you must jump between the beginning of the line and the end of the line, to find the last operations that have generated the stored value, when reading and understanding the complete expression would be a waste of time.)
The original motivation of the Iverson convention, which remains very important, was to give a useful meaning for a sequence of identical non-commutative operators, e.g. subtraction and division. This is particularly desirable when the operators are used in vector reductions.
(With school notation, a0 - a1 - a2 - ... - an is seldom a useful expression, but with the Iverson convention it becomes alternate sum, which is needed very frequently. Similarly for division.)
1. Function application should be left to right, e.g. `sqrt 4`
2. Precedence order should be very simple. In k, everything has the same precedence order (with the exceptions of brackets)
1 + 2 forces you to have this right to left convention, annoyingly.
Fwiw, I think 2 is great and I would rather give up 1 than 2. However, writing function application as `my_fun arg` is a very strong convention.
I wonder if diffusion models would be better at this; most start out as sequential token generators and then get finetuned.
1|2*3>>4+5
in C and k, this expression should hopefully evaluate to 1, but this is just a lucky coincidence: reading and writing these two expressions are wildly different in complexity in those two languages. if you're not sure what i mean, ask your local LLM to explain why that is, but make sure you're sitting down. what you'll discover is that what you think you "simply chose to do" is not what you're actually doing.
while it is true that you can write code anyway you deem fit, i'm afraid you're a bit confused about the actual direction you're forced to think you chose to write it.
but once you're there, it suddenly gets a lot less complicated, and - miraculosly - doesn't cancel out or mess up your previous beliefs and habits.
k/q, of apl heritage, are beautiful - first and foremost because they're simple to write and simple to read.
There is not enough q/kdb full source code "out there" that would have made it into the LLM training data. It tends to be used in secretive environments and can be very site specific in convention. I bet a purpose built small fine tune with real + synthetic data would be enough to get something generating better Q code.
Basically, to make the IP no longer be PII.
some hacks for time / position/ space flipping the models:
- test spate of diffusion models emerging. pro is faster, con is smaller context, ymmv is if trained on that language &/or context large enough to ICL lang booster info
- exploit known LTL tricks that may work there’s bunch of these
- e.g., tell model to gen drafts in some sort RPN variant of lang, if tests tell it to simulate creating such a fork of this and then gen clean standard form at end
- have it be explicit about leapfrogging recall and reasoning, eg be excessively verbose with comments can regex strip later
- have it build a stack / combo of the RPN & COT & bootstrapping its own ICL
- exploit causal markers - think tags that can splinter time - this can really boost any of the above methods - eg give each instance of things disjoint time tags, A1 vs K37 for numbered instances of things that share a given space - like a time GUID
- use orthogonal groups of such tags to splinter time and space recall and reasoning in model, to include seemingly naive things like pass 1 etc
- our recent arXiv paper on HDRAM / hypertokens pushes causal markers to classic-quantum holographic extreme and was built for this, next version will be more accessible
- the motivators are simple - models fork on prefix-free modulo embedding noise, so the more you make prefix-free, the better the performance, there’s some massive caveats on how to do this perfectly which is exactly our precise work - think 2x to 10x gain on model and similar on reasoning, again ymmv as we update preprint, post second paper that makes baseline better, prep git release etc to make it tons easier to get better recall and exploit same to get better reasoning by making it possible for any model to do the equivalent of arbitrary RPN
- our future state is exactly this a prompt compiler for exactly this use case - explainable time-independent computation in any model
at first they were hilariously bad, then just bad, then kind of okay, and now anthropic's claude4opus reads and writes it just fine.
IIRC, none of the gpt3 models did well with forth-like syntax. gpt4 generally did okay with it but could still get itself confused. claude4opus doesn't seem to have any trouble with it at all, and is happy to pick up the structures contextually, without explicit documentation of any sort.
another of my languages uses some parse transforming 'syntactic operators' that earlier models could never quite fully 'get', even with explanation. likely because at least one of them has no similar operator in popular languages. claude4opus, however, seems to infer them decently enough, and a single transform example is sufficient for it to generalize that understanding to the rest of the code it sees.
so far, claude has proved to be quite an impressive set of weights.
Usually when someone solves problems with q, they don't use the way one would for Python/Java/C/C++/C#/etc.
This is probably a poor example, if I asked someone to write a function to create an nxn identity matrix for a given number the non-q solution would probably involve some kind of nested loop that checks if i==j and assigns 1, otherwise assigns 0.
In q you'd still check equivalence, but instead of looping, you generate a list of numbers as long as the given dimension and then compare each item of the list to itself:
{x=/:x:til x}3
I think this form might be a bit easier: {(x,x)#(x*x)#1,x#0}
Give it time until we have true globally multi lingual models for superior context awareness.
as for LLM copilots and the quality of their lisp: why you'd expect them to excel in lisp better than they lisp in excel, pardon the pun?
this thread made me revisit some past conversations with people like atw, nsl and aab with regard to possible ways to expose humans to the way rivers flow in k/q/apl land. the choices are limited, and decision takes some agony:
a) if you don't want your audience to close the tab right away, you'd say "a k expression is written, read and evaluated strictly right to left unless the precedence is explicitly overridden by parens, and this works better than you think, no worries, you'll come around. by the way, parens are evil, avoid them if you can".
b) if your intent is to retain a sharper crowd who went to yale or something, you'd say "a k expression is to be understood right of left", and throw them a freebie in form of a prompt for their local LLM in order to get lit. the magic sequence is just "f g h x leibniz".
for my own selfish reasons, i always chose the former, and it seems to perform better than the latter, proof:
https://github.com/kparc/ksimple
still, neither approach is anywhere near the chances of successfuly explaining which way to write python code to a 5yo kid, especially its precedence rules, which are much more intuitive (lol).
to explain the same thing to an LLM is not much different, really. all you need to do is to depress your 0yo kid with an obscene amount of _quality_ python code, of which there is no shortage. obviously, the more python code is fed to LLMs, the more humans will paste more LLM-generated python code, to be fed back to LLMs, ad lemniscate.
(and don't mind the future tense, we are already there)
============
so this is why LLMs can't write k/q/apl. first, they haven't seen enough of it. second, they are helpless to understand the meaning of a quote which was once chosen to helm a book known as SICP, not to mention countless human counterparts who came across it earlier, to the same effect:
"I think that it's extraordinarily important that we in computer science keep fun in computing. When it started out it was an awful lot of fun. Of course the paying customers got shafted every now and then and after a while we began to take their complaints seriously. We began to feel as if we really were responsible for the successful error-free perfect use of these machines. I don’t think we are. I think we're responsible for stretching them setting them off in new directions and keeping fun in the house. I hope the field of computer science never loses its sense of fun. Above all I hope we don’t become missionaries. Don't feel as if you're Bible salesmen. The world has too many of those already. What you know about computing other people will learn. Don’t feel as if the key to successful computing is only in your hands. What's in your hands I think and hope is intelligence: the ability to see the machine as more than when you were first led up to it that you can make it more."
― Alan J. Perlis
Language designers would be smart to recognize this fact and favor making their languages more LLM friendly. This should also make them more human friendly.
Most techies (generalizing here) start with a reasonably clear spec that needs to be implemented and they can focus on how to architect the code.
Research - whether science, finance or design - is much more iterative and freeform. Your objective is often very fuzzy. You might have a vague idea what you want, but having to think about code structure is annoying and orthogonal to your actual purpose.
This is why languages like Ruby work well for certain purposes. They allow the person to prototype extremely rapidly and iterate on the idea. It will eventually reach a breaking point where global state starts being an impediment, but an experienced dev will have started refactoring stuff earlier than that as various parts of the implementation becomes stable.
That says nothing about the language at all, actually. Just that it's small and easily confused for something more idiomatic to a newbie.
Methinks if you want job-security in a post—LLM-zero-shot-app-generator world, get into Lisp or Haskell; People that know only Node+React from YouTube learn-2-code tutorials are going to be slaughtered.
I just had an idea: an app/GUI/backend framework for Lisp or Haskell (with an S-expression lib) where everything is structurally inverted so it must be manually run through foldr - behold: an LLM-resistant (if not LLM-proof?) dev environment!