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EsoLang-Bench: Evaluating Genuine Reasoning in LLMs via Esoteric Languages
- shablulman 7mo ago[dead]
- deklesen 7mo agoMhh... my hunch is that part of this is that all python keywords are 1 token, I assume. And for those very weird languages, tokenizing might make it harder to reason over those tokens. Would love to see how the benchmarks results change if the esoteric languages are changed a bit to make them have 1-token keywords only.
- chychiu 7mo agoConsidering that brainfuck only has 8 characters and models are scoring at 6.2% I don't think tokenization is the issue
- altruios 7mo agoThe only issue. * Reasoning is hard, reasoning about colors while wearing glasses that obfuscate the real colors... even harder... but not the core issue if your brain not wired correctly to reason. I suspect the way out of this is to separate knowledge from reason: to train reasoning with zero knowledge and zero language... and then to train language on top of a pre-trained-for-reasoning model.
- onoesworkacct 7mo agoLLMs already use mixture of experts models, if you ensure the neurons are all glued together then (i think) you train language and reason simultaneously
- __alexs 7mo agoI had hope we might finally be ushering in a bold new era of programming in Malbolge but apparently that was too optimistic.
- bwestergard 7mo agoI'm shocked to see how poorly these models, which I find useful day to day, do in solving virtually any of the problems in Unlambda. Before looking at the results my guess was that scores would be higher for Unlambda than any of the others, because humans that learn Scheme don't find it all that hard to learn about the lambda calculus and combinatory logic. But the model that did the best, Qwen-235B, got virtually every problem wrong.
- __alexs 7mo agoThey are also weirdly bad at Brainfuck which is basically just a subset of C.
- astrange 7mo agoBF involves a lot of repeated symbols, which is hard for tokenized models. Same problem as r's in strawberry.
- bwestergard 7mo agoInteresting. So why do the models seem to handle deeply nested Lisp expressions just fine?
- kgeist 7mo agoProbably because there's a ton of code that deals with nested parentheses across languages in the training data, and models have learned how to work around tokenization limitations, when it comes to parentheses.
- astrange 7mo agoIt's because the models wouldn't work for coding if they couldn't do nested scopes, so people don't release models unless they work. They can only do it in a limited form though, because transformer models only have limited "memory". I don't think they can fully implement parsing.
- simianwords 7mo agoI bet I can do better by allowing this: the llm can pull documentation of the language from the web to understand how it works. If the llm has “skills” for that language, it will definitely increase accuracy.
- Heer_J 7mo ago[dead]
- orthoxerox 7mo ago> Frontier models score ~90% on Python but only 3.8% on esoteric languages, exposing how current code generation relies on training data memorization rather than genuine programming reasoning. I would probably score about the same, does this prove I also rely on training data memorization rather than genuine programming reasoning? Or does this simply show that esolangs are hard to reason in by design? A more honest approach would use a "real", but relatively unpopular, language. Make them use CoffeeScript or Ada or PL/I or Odin or that other systems programming language that that very opinionated guy is implementing on top of QBE.
- iloveoof 7mo agoTry MUMPS, widely used but little training data online. Probably less than some esolangs
- twoodfin 7mo agoFrontier models have gotten much better at ObjectScript (the InterSystems evolution of MUMPS/M). Palindrome: https://chatgpt.com/s/t_69bc8d8c116c8191a339a33f0fbcc935 https://chatgpt.com/s/t_69bc8d8c116c8191a339a33f0fbcc935 This is a noticeable improvement from a year ago. I wish it would use Return instead of Quit but that’s a stochastic parrot for you.
- wavemode 7mo ago> I would probably score about the same, does this prove I also rely on training data memorization rather than genuine programming reasoning? Setting aside whether this benchmark is meaningful or not - the argument you're making is faulty. There are indeed humans who can write complete programs in Brainfuck and these other esolangs. The fact that you personally can't is not logically relevant.
- Groxx 7mo agoparticularly if you'd already read approximately all written material in existence about those languages. many humans are capable of learning a language from the documentation.
- rubyn00bie 7mo agoI am not surprised by this, and am glad to see a test like this. One thing that keeps popping up for me when using LLMs is the lack of actual understanding. I write Elixir primarily and I can say without a doubt, that none of the frontier models understand concurrency in OTP/Beam. They look like they do, but they’ll often resort to weird code that doesn’t understand how “actors” work. It’s an imitation of understanding that is averaging all the concurrency code it has seen in training. With the end result being huge amount of noise, when those averages aren’t enough, guarding against things that won’t happen, because they can’t… or they actively introduce race conditions because they don’t understand how message passing works. Current frontier models are really good at generating boiler plate, and really good at summarizing, but really lack the ability to actually comprehend and reason about what’s going on. I think this sort of test really highlights that. And is a nice reminder that, the LLMs, are only as good as their training data. When an LLM or some other kind of model does start to score well on tests like this, I’d expect to see better them discovering new results, solutions, and approaches to questions/problems. Compared to how they work now, where they generally only seem to uncover answers that have been obfuscated but are present.
- groar 7mo agoI guess if you tell codex to build a transpiler from a subset of python to brainfuck, then solve in that subset of python, it would work much better. Would that be cheating?
- msully4321 7mo agoDoing something like that is basically the only way to write unlambda: you start with a lambda calculus (or scheme or whatever) and reduce the lambdas away mechanically. (This is in the unlambda docs!)
- monster_truck 7mo agoI have encountered the opposite of this. All of the latest pro tier models are still fighting for their lives to use powershell correctly, really basic things like quotes, escaping, heredocs. Doesn't matter what I put in agents.md or instruct it to do. You just have to accept the token tax of it stomping on rakes until it figures it out itself and then keep using that session. It's bad enough that I've considered writing some sort of cursed bash->posh translation layer Yet it has no issues at all implementing and then writing slopjective-c 3.0
- noahbp 7mo agoOpus 4.6 has gotten pretty good at writing Powershell. It’s the first model where I didn’t have to ask, repeatedly, that it use Powershell 5, and never use emojis or other invalid characters, like Gemini and those non-ASCII spaces.
- gverrilla 7mo ago"Genuine Reasoning"
- sathish316 7mo agoDoes this imply LLMs will not work well on novel reasoning problems?
- wmf 7mo agoARC-AGI is already testing that.
- danpalmer 7mo agoYep that's the implication. Anecdotally this is obvious to me. I'm using LLMs to write Java and C++, and then can churn out generic plumbing with no issues, but novel code for a novel implementation of a novel idea, they have no idea what they're doing. I'm getting good productivity gains, but it requires a lot of hand holding because AI does not know what it's doing. On far less novel problems I get far better results.
- robot-wrangler 7mo ago> Frontier models score ~90% on Python but only 3.8% on esoteric languages, exposing how current code generation relies on training data memorization rather than genuine programming reasoning. Finally! This is a really obvious test-case that I've wondered about myself, and have seen many casual skeptics and cautiously optimistic people independently raising for several years now. When megacorp is not crowing about such a test, the silence is deafening, and it was practically guaranteed that they tested, didn't like the results, and didn't publish. I'm still surprised it took this long for academics to try it, and skimming cites, I don't see anything similar. Anyone know if this is the first paper to try this kind of thing, or just the first paper to put together a especially good suite of reusable benchies? If this benchmark becomes popular, then presumably to avoid such embarrassments synthetic data is eventually added to training sets to make sure even esolangs are somewhat more in-distro, and then we gradually run out of esolangs to do honest testing with. SAT is a whole different animal admittedly, but comparable honest tests might involve just forcing models to use randomly generated but easily checked EBNF grammar? I don't have a quick link to the relevant papers, but afaik benchmarks of strict adherence to non-simple JSON schemas is also still pretty bad, and we're just working around it with lots of retries/tokens. "But look how well it works for 10k lines of kubernetes manifests!" Well yeah, maybe, but it barely needs to really follow a schema since that is more stuff that's in the training set..
- GorbachevyChase 7mo agoI don’t have much confidence n the premise. Where was the human control? I think most Python programmers when tasked with “now do it in brainfuck” would fail. There is not much meaningful overlap in how to express intent and solutions to problems. The ridiculous syntax is the joke. But more importantly, I don’t have to solve any problems with languages that are elaborate practical jokes, so I’m not worried about the implications of an LLMs ability to be useful.
- dvt 7mo ago> I don’t have to solve any problems with languages that are elaborate practical jokes This is just being needlessly dismissive. Esolangs are (and have been) an area of active CS research for decades. I know I'm a bit of an esolang nerd, and while some are jokes, most focus on specific paradigms (e.g. Piet is visual, bf is a Turing tarpit, etc.). > I think most Python programmers when tasked with “now do it in brainfuck” would fail. This is untrue. Given internet-level awareness and infinite time, virtually all developers should be able to go from Python to brainfuck (trivially, I might add.) Did you even look at the test sets? It's all pretty basic stuff (palindromes, array traversal, etc.—we aren't using pandas here). I mean, sure, it would take forever and be mega annoying, but manipulating a head and some tape is hardly difficult.
- paraschopra 7mo ago(founder of Lossfunk, the lab behind this research.) Esolang-Bench went viral on X. A lot of discussion ensued; addressing some of the common points that came up. Addressing a few questions about our Esolang-Bench. Hope it helps. a) Why do it? Does it measure anything useful? It was a curiosity-driven project. We're interested in how humans exhibit sample-efficiency in learning and OOD generalization. So we simply asked: if models can zero/few shot correct answers for simple programming problems in Python, can they do the same in esoteric languages as well? The benchmark is what it is. Different people can interpret its usefulness differently, and we encourage that. b) But humans can't also write esoteric languages well. It's an unfair comparison. Primarily, we're interested in measuring LLM capabilities. With the talk of ASI, it is supposed that their capabilities will soon be super-human. So, our primary motivation wasn't to compare to humans but to check what they can do this by-construction difficult benchmark. However, we do believe that humans are able to teach themselves a new domain by transferring their old skills. So this benchmark was to set a starting point to explore how AI systems can do the same as well (which is what we're exploring now) c) But Claude Code crushes it. You limited models artificially. Yes, we tested models in zero and few shot capabilities. And in the agentic loop we describe in the paper, we limit the number of iterations. As we wrote above, we wanted to understand their performance from a comparative point of view (say on highly represented languages like Python) and that's by the benchmark by design is like this. After the paper was finalized, we experimented with agentic systems where we gave models tools like bash and allowed unlimited iterations (but limited submission attempts). They indeed perform much better. The question that's relevant is what makes these models perform so well when you give them tools and iterations v/s when you don't. Are they reasoning / learning like humans or is it something else? d) So, are LLMs hyped? Or is our study clickbait? The paper, code and benchmark are all open source. We encourage whoever is interested to read it, and make up their own minds. (We couldn't help notice that the same set of results were interpreted wildly differently within the community. A debate between opposing camps of LLMs ensued. Perhaps that's a good thing?)
- chromaton 7mo agoI did something very similar last year, but with programming languages that were REALLY out of distribution; they were generated specifically for the benchmark. I call it TiānshūBench (天书Bench): https://jeepytea.github.io/general/introduction/2025/05/29/tianshubenchintro.html https://jeepytea.github.io/general/introduction/2025/05/29/t... Some models were OK at solving very simple problems, but nearly all of them would, for example, hallucinate control structures that did not exist in the target language.
- amluto 7mo agoA few months ago I created a little API to help with an obnoxious case in FFI [0] in an extremely esoteric language known as Python. It was straightforward, it had a fully typed signature, and I fully documented it. (And the entire implementation was only 50 lines or so of intentionally very straightforward code.) The LLM (Codex 5.2 IIRC) could not manage to call the function with the right arguments even after multiple rounds of prompting. Sometimes I think LLMs are unbelievably, amazingly good at things. And sometimes I’m deeply suspicious that they really not very smart, and this was an example of the latter. [0] Python calling to C, passing a callback function pointer and a void *opaque that C will pass back to the callback. Short of writing an extension module, this is pretty much forced to go through an inherently nasty JIT codegen process in libffi, which is sort of tolerable, but you really don’t want to redo it for each object that gets opacified to void*. Codex passed a lambda, which did the nasty JIT thing every time. I wrote a little shim using weakref. Apparently no one has done this before, so Codex wasn’t trained on it, and it couldn’t make itself call the function. Maybe I should post it to PyPI.
- deleted 7mo ago[deleted]
- sinuhe69 7mo agoI don’t understand why the authors haven’t written the problems instead of putting up a video? I rather read then watch (who knows how long) videos. Videos it’s a much worse format for information density and took much longer to make properly and up/download. I mostly skip the videos whenever I can.
- mastermage 7mo agoI mean if you know how GPTs work its realy logical. This also basically means that for anything boundary pushing, new features etc. AI is basically useless. I would even go as far as conjecturing that this means self improving models as some AI proponents proclaim are around the corner. Are as far away as they had always been.
- QubridAI 7mo ago[dead]
- 22122 7mo agoyeah, computers are better at search, humans are better at reasoning, the dichotomy continues on.
- maximge 7mo agoCool test, tried to solve problems from B.3 Complete Problem Examples. https://arxiv.org/html/2603.09678v1#A1 https://arxiv.org/html/2603.09678v1#A1 Opus 4.6 Extended, solved all of them. https://claude.ai/public/artifacts/aeb98066-f7a9-455b-9550-6fe0780dd540 https://claude.ai/public/artifacts/aeb98066-f7a9-455b-9550-6... https://claude.ai/public/artifacts/b0fcd13f-d222-4b65-bdcf-fa98322ef64b https://claude.ai/public/artifacts/b0fcd13f-d222-4b65-bdcf-f... https://claude.ai/public/artifacts/304650fb-afbf-4a08-9f6b-59f1ad4dd7cb https://claude.ai/public/artifacts/304650fb-afbf-4a08-9f6b-5... https://claude.ai/public/artifacts/d00b898c-2265-4a34-a910-952d5a24dbb1 https://claude.ai/public/artifacts/d00b898c-2265-4a34-a910-9... Only once there was an incorrect answer, on Hard: H01: Balanced Parentheses. On the second attempt, it was solved. The Josephus Problem turned out to be really easy (meaning solved in short term). Possibly the model got trained on the earlier tasks since I did everything in one chat. As a prompt, I provided the problem statement, except for the first task where I added this description of the language: Syntax: Character Instruction Performed > Increment the data pointer by one (to point to the next cell to the right). < Decrement the data pointer by one (to point to the next cell to the left). Undefined if at 0. + Increment the byte at the data pointer by one modulo 256. - Decrement the byte at the data pointer by one modulo 256. . Output the byte at the data pointer. , Accept one byte of input, storing its value in the byte at the data pointer.[b]
- msully4321 7mo agoI took a look at the test bench, and the Unlambda interpreter used supports like a third of unlambda, not including input. Since 79/80 of the tests require input, it isn't really possible to solve them. The best anything did on them was 1.2%, which is 1 test. I reported it https://github.com/Lossfunk/EsolangBench/issues/1 https://github.com/Lossfunk/EsolangBench/issues/1 but haven't heard back yet
- Ekil 7mo agoI don't think human can do the test without read the docs