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There is something deep in this observation. When I reflect on how I write code, sometimes it’s backwards. Sometimes I start with the data and work back through to the outer functions, unnesting as I go. Sometimes I start with the final return and work back to the inputs. I notice sometimes LLMs should work this way, but can’t. So they end up rewriting from the start.

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.

Languages that are difficult for LLM to read & write are also difficult for the general public. These languages have always had poor uptake and never reach critical mass, or are eventually replaced by better languages.

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.

This is something that diffusion based models would capable of. For example diffusion-coder https://arxiv.org/abs/2506.20639 Could be trained on right to left, but it doesn't seem like they did.

Another example of this is Claude placing unnecessary imports when writing Python, because it's hedge-importing modules that it suspects it might need later.

I fully discount the right to left thing.

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.

Humans can't either? I think if this convention had been more usable form of programming, we'd know by now

don't plan on it staying that way. I used to toss wads of my own forth-like language into LLMs to see what kinds of horrible failure modes the latest model would have in parsing and generating such code.

at first they were hilariously bad, then just bad, then kind of okay, and now anthropic's claude4opus reads and writes it just fine.

Interesting. Upshot - right to left eval means you generally must start at the end, or at least hold an expression in working memory - LLMs - not so good at this.

I wonder if diffusion models would be better at this; most start out as sequential token generators and then get finetuned.

Incidentally, I've had the same thing too with Lisps on both o-series and smaller Claude models - always a mismatched paren or two.

R has right assigment `1 -> x` LLMs seem to enjoy it a bit too much.

My curmudgeonly genius Q/Kdb+ programmer of a co-worker, whom claims to be immune to the impact of LLMs, is going to be fucking pissed when he hears about Qython.

"Claude is aware of that, but it struggled to write correct code based on those rules"

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'.

How do they do with lisps?

I think in the long run the sensible way to deal with this kind of monitoring is either shared-IP web endpoints for European ISPs, or per-connection random IPv6 addresses, reallocated continuously.

Basically, to make the IP no longer be PII.

Cognitive load in LLMs: When LLMs are faced with syntactic complexity (Lisp/J parentheses/RL-NOP), distractors (cat facts), or unfamiliar paradigms (right-to-left evaluation), the model’s performance degrades because its "attention bandwidth" is split or overwhelmed. This mirrors human cognitive overload.

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-...

Same reason the same models don't fundamentally understand all languages. They're not trained to. Frankly the design changes to get this to work in training is minimal but this isn't the way English works so expect most of the corporate LLM to struggle because that's where the interest and money is.

Give it time until we have true globally multi lingual models for superior context awareness.

I can write code right-to-left, I simply choose to not do it.

most mainstream models are decoders vs. encoders-decoders, diffusers, etc. and lack reversible causal reasoning, which of course can be counter-intuitive since it doesn’t feel that way when models can regenerate prior content

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

It's not because of the left of right evaluation. If the difference was that simple, most humans, let alone LLMs, wouldn't struggle with picking up q when they come from the common languages.

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

An LLM that's been so heavily trained on an imperative style will likely struggle to solve similar (and often more complex) problems in a standard q manner.

I always thought APL was written in the wrong direction. It writes like a concatenative language that's backwards--you tack things onto the front. NumPy fixes it by making the verbs all dotted function calls, effectively mirroring the order. e.g. in APL you write "10 10 ⍴ ⍳100" but in NumPy you write "np.arange(1, 101).reshape(10, 10)". Even if you don't know either language, you can tell that the APL version is the reverse of the Python version.

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.

Seems like it could easily be training data set size as well.

I'd love to see some quantification of errors in q/kdb+ (or hebrew) vs. languages of similar size that are left-to-right.

Ordering issues can be overcome by allowing the model to think in one direction and then reverse the output once it has created it.

Another quirk inserting random whitespace when generating code. Seem to be tokens for different lengths of whitespace

LLMs are already solving this problem using the "thinking" phase. They don't just one-shot an attempt at the output. The left-to-right narrative thinking process edits multiple drafts of the code they eventually output.

curiously enough,

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

https://github.com/kparc/kcc

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)

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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

This is, in part, one of the reasons why I am interested in the emerging diffusion based text generation models.

I read the other day here that the new Apple AI can write out-of-order. Maybe it can do this.