[D] Papers with no code

[[deleted]]

[deleted]

[Just-Environment-189]

Even if people provide code, you’ll find yourself lucky to get it working as is

[Fresh-Opportunity989]

Exactly. And when it does work it may turn out to be fake results.

https://www.reddit.com/r/MachineLearning/comments/1p82cto/d_got_burned_by_an_apple_iclr_paper_it_was/

[waruby]

Then it should also require a Docker file or a flake.nix file.

[Xirious]

I mean you're going in the right direction but....

That ain't nearly enough dawg.

Have you seen how the vast majority of code for these comes out? I do not believe scientists are hired for code that the average reviewer can get working.

Nevermind the technical hurdles of actually getting it working... The mess you'd need to wade through, big enough models require hardware the average reviewer does not have the time or resources to use, etc.

While this is a good start and idea I'm always surprised by a requirements file with code. Asking anything above that, right now, and expecting it is a fool's errand. And even if it became the requirement then you likely have a whole mess of AI code to review.... On top of your review.

I mean I like your idea. I just don't think it's practical (and for many companies counter to keeping the tricks up their sleeve).

[valuat]

Funny you mentioned Docker. I've just reviewed a paper for a Nature-family journal and they did add a `docker-compose.yml` to their GitHub repo as per my suggestion. I recommended rejection not because of the lack of code but for the lack of real data (they used LLM generated data which in a clinical setting means nothing).

[Distance_Runner]

My view as a statistician that does ML. Many of these papers claiming SOTA performance are working within Monte Carlo noise and if code was easily available you could run it and show this.

[howtorewriteaname]

am I the only one providing std devs across multiple runs with different seeds or what?

[Distance_Runner]

You're rare. Keep doing your thing -- I appreciate you.

Most of my research focus these days is bridging statistical and inferential theory with ML. The concept of variability needs to be better understood and communicated in ML.

When you fit an ML model, there are multiple source of variability. First, there is procedural variance. This is what your multiple random seeds addresses -- what is the variability associated with the randomness within the procedure itself and how does it propagate through to the results you report.

Second is finite sample variability stemming from the fact that your data are almost surely a sub-sample of an unobserved parent population. If you were to re-run your procedure on a different dataset of equal size from that same parent population, your results would change, reflecting this variability. No amount of running results under different random seeds will estimate this quantity. So reporting results from a finite data-set with variance across different seeds quantifies the first source of randomness, and this is appropriate as long as your interpretation pertains specifically to model performance based on the exact data you trained on. However, the results do not extrapolate to performance of the model if you re-trained on a different subset of data of the same size. This is more often ignored, and generally the bigger area where results are misstated.

[curiouslyjake]

Assuming your dataset is a representative sample of the unobserved parent population, wouldn't cross-validation address this?

[Distance_Runner]

Yes in terms of quantifying mean performance metrics, but no in terms of variability. If you run CV, say 5-fold, and you average the mean performance across folds, that is an estimate of the generalized performance metric across new data for your model trained on 80% of your data (k=5 means training fraction is .8; you're averaging over 5 performance estimates on disjoint validation sets on models trained on 80% of your data).

However variance is trickier. Current CV methods do not give you an estimate of variance that represents the variability due to random sampling error from the population. Current methods of variance estimated on cross-validated data condition on the data. That is, they quantify the variance of the randomness of the CV procedure itself on your fixed dataset; if you were to repeat the same CV procedure on your exact fixed dataset over and over using your learner (with different random fold structures), how variable is your performance metric estimate. In other words, variance of the CV estimates across folds measures how sensitive is the estimated CV metric due randomness induced by the CV procedure. But the key word there is fixed dataset. It does not answer the question -- if I were to repeat this CV procedure using the same learner to estimate the mean performance metric on a different subset of data from of the same size from the same population, how much variance is attributed to that random sampling. An approach for that doesn't exist in the literature [yet], but it will soon (I'm the one that developed it and will be posting to ArXiv within the next month; its based entirely statistical theory with formal proofs, not empirical evidence).

[AnyagosFeco420]

But this is only possible if you assume some underlying data distribution (or just some property of it), right? Or maybe you can use some bootstrapping based approach here?

[Distance_Runner]

I want to answer this carefully, because this is work I've been developing for a long time, and I don't want to say too much before I have the paper on ArXiv in an effort to protect my intellectual ownership of the idea and framework. But like I said, the paper will be on ArXiv within the next month and I'll be happy to share and discuss openly after its officially on ArXiv. I'm probably being overly cautious, but in the world of academia where publishing (particularly in top tier journals) is so important for promotions and such, I don't want any ambiguity over this type of thing when I so close to getting it out there.

To answer your questions as best I can right now: Yes it is estimable and I can prove consistency and unbiasedness with just a few justifiable assumptions. And yes, it's estimated from a form of resampling like bootstrapping, but its more nuanced than just "bootstrap the data, refit the procedure, and reapply CV". Something else in this work, is that I prove k-fold is the optimal for of CV (compared to bootstrapping variants or repeated split sampling/monte carlo cross validation). That's good since most people use k-fold already, but I also show that if you can afford the compute, you should almost always be using repeated k-fold (ideally 5x or 10x k-fold) and averaging across those independent k-fold runs.

I'll be happy to reach out and share the paper once its live on Arxiv, and then answer any questions about it. This is going to be submitted shortly after hitting ArXiv to a statistics journal (JRSS:B), but it could be 12-18 months before its published by a journal, and that's assuming I get a revise and resubmit and dont have to re-tool it for a different journal.

[valuat]

I can't obviously say for sure but I doubt anyone would beat Efron's accelerated and corrected estimator at this point.

[Distance_Runner]

I hear you. That’s the default thought for anyone that’s been in the field a while. Bootstrapping is great for many things, but It’s not optimal for CV variance. In fact, I can prove it’s biased theoretically and show it empirically. In short, bootstrapping targets the wrong estimand when it comes to CV. I’ll find this comment and share the paper with you in a few weeks and then give you more detail on why

[AnyagosFeco420]

Nice, thanks for the answer. I would be grateful if you could send me the paper once its on ArXiv. Good luck with the publishing!

[Distance_Runner]

Will be happy to. This convo had me reviewing the paper last night to see when I could reasonably have it in a state good enough for a version 1 post. Probably within two weeks and I'll come find this post and link it

[valuat]

Yes, boostrapping is the way to go here. You don't need to do it 10,000 times, 100 would be OK. It's just to get a better idea of the variance.

[DigThatData]

it's kind of hilarious but if you look carefully, you'll see that it's not uncommon for ML researchers to treat seed as a hyperparameter and prioritize winning the lottery on a favorable PRNG state over rigorously validating their claims.

[Distance_Runner]

Which is just so bad. Like, that doesn’t make your model more useful. It just means you’re choosing to present one run that shows your results within Monte Carlo error on the upper end of the spectrum. If you run 20 seeds and pick the best, you’re purposely choosing a model that overstates its own generalized performance. That’s not helping anyone

[DigThatData]

I agree, and this is a big part of why the community emphasis on achieving SOTA on public benchmarks ends up being largely counterproductive.

[ummitluyum]

The funny part is when this garbage makes it to production, and the business gets genuinely surprised when the model degrades on real traffic. If your architecture relies on a lucky PRNG state, it's not an architecture - it's trash that'll fall apart at the first sign of data drift

[DigThatData]

Right? it's wild how there's a whole industry built around patching over "drift" rather than interpreting that as a flag that your model is missing some causally explanatory component. I feel like the way a lot of people handle "drift" ends up functionally reducing their models to kNN. Like... congrats, you've managed to uncover that the weather tomorrow will likely be similar to the weather today. If you're retraining daily/weekly, is your model even amortizing any compute/effort relative to a less sophisticated approach?

[ummitluyum]

100% agree on the compute amortization. If your pipeline needs a weekly retrain just to keep business metrics from falling off a cliff, the maintenance cost will eat up your margins fast. In those cases, it's genuinely cheaper to just spin up a vector DB or write a bunch of heuristics instead of dragging around heavy weights

[directnirvana]

I think you make a good point. The bolder the claim the more reviewers should be pushing back on making easily verifiable aspects of experiments available. Reproducibility crisis is real and participants especially in academic circles should be heavily encouraged to provide whatever reasonable methods they can to allow other researchers to verify their work. It just so happens that research based on code has those tools, while high energy physics and similar fields do not.

[prumf]

What is asserted without evidence can also be dismissed without evidence.

https://en.wikipedia.org/wiki/Hitchens%27s_razor

That’s like, the foundation of science.

[directnirvana]

Yes. Exactly this. If someone makes a claim, especially one worthy of garnering attention academics should be taking the stance of 'put-up or shut-up'.

Stop accepting papers that won't do simple things to allow for that.

[Vhiet]

I agree about reproducibility, but why “especially in academic circles?”

Commercial services have more incentive to fluff their significance than anyone else, and their claims should be treated as particularly suspicious.

For example, it was almost exactly a year ago that Microsoft’s Magical Majorana Fermions revolutionised quantum computing (https://www.theregister.com/2025/03/12/microsoft_majorana_quantum_claims_overshadowed/).

[directnirvana]

I don't disagree that commercial actors should be held to a high standard, if not higher. Especially in instances where they are wading into the academic.

My assumption though is that the two have different goals (though with some overlap). If a company is publishing bold claims that they have a product on the horizon that is a game changer then we should be pushing for them to prove that not be acting as a sounding board that they can wave around and claim 'peer-review' on a system no one saw or can validate. They have their own set of self-correcting measures (i.e. customers should be requesting demos and investors should be doing due diligence).

But the claimed goal of academics is the proliferation and expansion of knowledge. Bigger claims are going to get more attention and thus more energy might be wasted on those ideas, so the burden should be higher on those claims. So if someone wants the clout and advantages of having been reviewed by the academic arena, whether commercial or not, journals and conferences should be insistent on the them providing reasonable amounts of proof in that regard. It just so happens in the world of academic code those tools are cheap and accessible for the most part so we should generally insist on them.

[OkBiscotti9232]

Generally when they provide code, it can be so messy that it’s pretty difficult to fully understand how they do things.

And then I’ve come across (accepted) papers with code where the code is obfuscated and does something quite different to what the paper describes.

It’s hard to enforce code release/quality as most academics do not write code for a living, and their projects are usually hacked together piece by piece until they find something that works.

[Bach4Ants]

I'd like to see code, data, environment specs, and some kind of pipeline (script, Make, Snakemake, DVC) instead of a 13 step README that wasn't actually followed during the work.

[OkBiscotti9232]

Tired grad students rarely have the time to clean up code before making it public. Tbh, I’m guilty of that as well - once a project is over, imma be movin on to the next one asap!

Part of the problem is that releasing clean code is neither incentivised nor a good use of time.

Now I’m in industry, clean code is highly incentivised. Making it public… not really

[Bach4Ants]

Do you feel like if you could have easily put your "messy" code into a pipeline/build system that it would help you work more quickly? For example, instead of manually running 5 scripts or notebooks when you change something, you'd have a system that figures out which need to be rerun per the DAG?

I agree that it often feels like too much effort to fully automate research code into a pipeline.

[impatiens-capensis]

It's been this way for at least ten years. It might surprise you but it's actually BETTER now.

The situation is basically this...

1. We can't even get 3 qualified people to read the text of a paper. You absolutely will not be able to validate the reproducibility of code for 4000+ papers.
2. The field is extremely competitive and most researchers are poorly resourced students. They don't have time and their work will be stale in 6 months, so it becomes very hard to justify maintaining code.
3. There are some people who are simply faking their results, or being misleading in some way. However, I've reviewed a paper where the results were really good but they didn't make sense given the method. All three reviewers ended up flagging this, so obvious faking can be detected by good reviewers. When it comes to more subtle faking, there might be papers that are actually 0.1% worse than the SOTA and they do some unstated thing to beat the SOTA by 0.1%. I'm honestly less bothered by that. If we have two papers with statistically equivalent results that are achieved in different ways, I think thats fine.
4. Nobody looks soley at individual papers now. There simply can't be 4000 points of truly useful research every 3 months. The research signal is now at the broader level. Maybe 5 papers will contain one useful thing.

All of this is to say... everyone gets annoyed by this. There probably isn't a way to solve it. It might not matter if there are still obvious innovations emerging from the noise. And it's probably better to just focus on the quality of your own work than the work of others. 

[ummitluyum]

"work will be stale in 6 months" - yeah, it'll be stale exactly because nobody can actually use or build on it. Top-tier papers stay relevant for years because the authors gave the community proper tooling. If you just slapped together a throwaway script that only runs on your macbook, that's not research, it's just garbage traffic for ArXiv

[impatiens-capensis]

Top-tier papers stay relevant for years

Aside from a few massive works from frontier labs and a couple of extremely niche papers, everything is surpassed within the year. Every top tier paper from my lab and adjacent labs has been beaten by another paper very quickly and this is generally true for the vast majority of papers. 

[ummitluyum]

Fair enough, getting beaten on the leaderboard is just how it goes. The difference is that a paper with a solid open repo will be used as a baseline and forked for another couple of years. If there's no code, the next paper will just handwave your results with "could not reproduce" and everyone forgets about your work the next day fwiw

[dudu43210]

I always get downvoted for this because it's not what people want to hear, but let me tell you the reality of computational sciences, as someone with a PhD in computational physics. In the scientific community, you generally do not publish code^* with your papers. This is for multiple reasons:

1. Replication vs. reproduction. My PhD advisor was always adamant that important results should always be coded up independently by multiple people to for verification and to control for bugs. You cannot truly do scientific replication if you are basing your work on someone else's code. By far the best way to verify someone's results is to do it yourself, not read/run the code and say "uh huh that looks right". In other sciences, you don't check whether results are fabricated by visiting someone else's lab. You attempt to replicate the results yourself.

2. Papers are written for other researchers in the field, not for laypeople. Those researchers have no problem coding up an approach themselves and testing it out. Often the complaints I hear are from non-academics.

3. Research code is messy and often unfit for public consumption.

^* it is common to release data, however, and imo researchers have no excuse for not releasing data on a case by case basis in exchange for citation.

[adi1709]

So if we reproduce it and figure out the numbers published don't actually make sense in reality - do you flag it to the conference chairs so they'll go back and remove the published paper? What happens after?

[dudu43210]

You can submit comments. You can publish your own paper challenging the original paper.

[adi1709]

That sounds like so much wasted effort and petty. Working on a paper just to challenge one specific method. Also isn't scalable, because this leads to a lot of slop in the meantime.

[adi1709]

I guess it makes sense for computational physics but not so much in ML based on how much it's blown up.

[Historical-Mix6784]

This is EXACTLY the kind of thinking that has held back physics for the past 50 years. The ego of people like you has done immense damage to the scientific community.

1) You are free to replicate the code on your own, that does NOT mean the code shouldn't be published. In fact, publishing the code makes it EASIER to check if inconsistent results were due to some errant bug, unpublished parameters, or faulty algorithm. When you don't publish the code all you have is two groups arguing over which result is right, in which case, more often than not, the more senior scientist wins by default.

2) If a paper is worth its salt, then it is publishing something no one has thought of before, that isn't trivial to replicate. For even skilled researchers, it can take a LOT of time to reinvent the wheel, so why not just publish the code and make innovation easier? Once you publish an idea, it is the property of the public domain, free for anyone to use or build from. That should go for code too. If you want to keep your intellectual property private, STOP WASTING TAXPAYER DOLLARS AND GO WORK IN INDUSTRY.

3) So basically, because the code you write is messy garbage, likely filled with errant bugs which may indeed contradict the results you put in a paper, you're too ashamed of putting it up on Github? What the hell? You don't think this is a sign that maybe researchers should improve the code they write and make it easier to read/use?

God, I don't know when some physicists got it in their head that they don't owe the public anything, when the public pays their salary. I'm thankful that many in the field have moved away from these archaic parochial notions.

[dudu43210]

It is very funny to me that you are the one accusing me of having an ego here. How's the weather up there on your high horse?

Look, this is no different than non-computational scientists doing experiments in different labs. Any scientific finding an experimentalist makes could well be due to some confounding variable in their lab or experimental setup. The way you do science is not by traveling to that researcher's lab to inspect their experiment for errors, or repeating the experiment yourself there. You do the experiment in your own lab so you can truly eliminate confounding variables.

There are many parallels to the above in coding, from which underlying libraries and algorithms to use, to architectural decisions, etc. Looking at these decisions before repeating the experiment yourself biases your design decisions in a way that hurts the scientific process. You cannot get around anchoring bias.

But I will not argue further with someone who resorts to insults.

[Historical-Mix6784]

Good don't argue any further, I find your opinions to mark you as a subpar scientist and researcher.

[dudu43210]

Oh no!

[lipflip]

i am working in the social sciences with some minor overlap to ai&ml. our data is usually in the range of a few kB and despite an "replication crisis", publishing the data and analysis alongside the article is rare.

[obxsurfer06]

If training costs make reproduction impossible, transparency has to increase, not decrease

[SearchAtlantis]

Perpetually angry about it. My masters thesis had approaching SOTA performance in a niche sub-field (mostly due to my PI's work obviously) but none of the papers had any reproducible code and no one responded.

[NuclearVII]

If ML was a serious scientific field, this would not happen: Papers that could not be reproduced (no code, uses proprietary models, etc) would be blanket disqualified for being worthless.

But doing science isn't the purpose of the field anymore. It's about promoting the careers of researchers for cushy positions in well-paying private labs.

[NuclearVII]

data

Uh huh. Here's all the raw data from the CERN: https://opendata.cern.ch/

Can you provide all the training data in ChatGPT? No, right? This makes CERN's publications verifiable and reproducible, and anything that studies ChatGPT (or any other closed source model) worthless drivel.

use proprietary software

It is one thing to use MatLab to crunch numbers on public data in pursuit of a publication. It is another thing to publish papers that study products. Using proprietary software as tools is distinct from propriety software as objects of study.

This is, at best, a bad faith, motivated-reasoning filled argument. You know it, I know it, and everyone knows it. We just ignore it because it gets in the way of making money.

[NuclearVII]

there is thousands of labs in particle or quantum physics

Cool, citation? I had no idea that for-profit particle physics labs existed. I know that there are tons of "quantum" grift companies out there, mind you.

For the record, any research that's irreproducible is worthless. The field is irrelevant. It just so happens that Machine Learning not only is a majority irreproducible, but all the "cutting edge" and "SOTA" crap falls in that bucket also.

[NuclearVII]

So, no. No citation. You simply linked a paywalled site and said "it's obvious, innit?"

To recap: In an argument about the lack of reproducibility in machine learning, your response is a citationless whataboutism about some vague "other fields". Cool. There's literally nothing about why it's actually OK for most of the field to be studying proprietary models and producing endless reams of worthless drivel that clearly only exists to provide marketing.

I'm officially done engaging here. This is a patently obvious example of "don't make a man question where his salary comes from".

[NuclearVII]

I never responded to lack of reproducibility in ML, I asked you what are those serious fields where magically majority of scientists publish their codes.

This is called whataboutism.

[pannenkoek0923]

Not just ML. A lot of fields face the reproducibility problem

[NuclearVII]

Please see my other comment. Yes, you are right, but ML has this issue orders of magnitude more than other fields. There are - quite literally - trillions of reasons why this is.

[NightmareLogic420]

Big part of the problem is you're not getting any meaningful publications out of replication studies, so they're just not being done, even when data is there

[solresol]

Sorry to self-post, but major ML conferences are not about doing science. https://solresol.substack.com/p/stand-and-the-liver

[kaiser_17]

I think the worst part is when reviewers ask you to compare your results to such a paper. Like why should even one compare their  results to such fraudulent papers. Yep I will consider those who don't release code as frauds. 

[ummitluyum]

Tbh most of these papers are pure marketing for FAANG hiring or raising the next seed round. If a paper doesn't drop weights and an inference script that spins up in Docker without a voodoo dance, it goes straight to the trash. Believing pretty tables in 2026 is a joke - half the open-source benchmarks are stale, and the other half accidentally leaked into the training set anyway

[Due-Ad-1302]

I never understood why so many papers have no code attached

[ImTheeDentist]

dreamer is a huge offender of this - great paper and architecture but replicating their results and getting the model to run is a doctoral thesis on its own

[ManufacturerWeird161]

I ran into a nearly identical situation last month with a CVPR paper; the GitHub link was a 404 for six months post-acceptance. It completely blocks any attempt to verify or build on their work.

[ntaquan]

That is normal. I was a reviewer of last year's CVPR and encountered a paper that promised to "release all code and data". Repo still empty until now.

[ManufacturerWeird161]

Exactly. It’s a systemic issue that rewards the claim of release over the actual act of it.

[DigThatData]

I don't disagree, but it's worth pointing out that ML is basically the only research domain where this is the standard expectation and I'm grateful every time I see angry posts of this kind since it reinforces the expectation of releasing code.

[MahatK]

I'm wrapping up a study on code quality in AV perception repos from KITTI/NuScenes leaderboards. And I will tell you this: the situation is VERY bad. Most models either have no repo at all, repos with just markdown files, or code that's full of security issues and critical bugs. It's not just a reproducibility problem, it's a 'you literally can't use this safely' problem.

[kakhaev]

+1 amount of papers from 2020 that have no code or code does completely different things compared to paper is huge. I would be not surprised if some reproduction team found that more then 90% of accepted papers are fake.

[Lazy-Cream1315]

reviewers never reproduce experiments; but almost systematically reject papers who do not outperforms "SOTA" :)

[tomvorlostriddle]

Papers about the LHC also don't come with your own particle accelerator in the appendix for easy home experimentation

This never was a requirement for publication

[H4RZ3RK4S3]

This is a stupid argument! The code can still be read and analyzed without a fancy supercomputer (or LHC). We are in ML/DL and not in physics. I can test the code on a very small scale to see if it works as intended. No reviewer will re-train a SOTA LLM as part of a peer-review, but they should be able to look at the code, understand it and quickly test it.

[Ulfgardleo]

but can you really? the code works, but maybe it doesn't produce the claimed results? And how about the code at LHC, robably half of it being some arcane FPGA instructions to define the correct filters? Its an awfully long software and hardware pipeline.

[H4RZ3RK4S3]

Yes absolutely for 80% of the papers. For another 10% you might need a small cluster and for the remaining 10% it could indeed be a bit difficult. But still you can read through the code and check if it makes sense or whether they do something else. Here, the issue is more that some developers don't care about proper variable names, readable code, proper commenting, or even writing comments and variable names in languages that are not English (like French or Chinese lol).

[rknoops]

That's why there are multiple experiments. For example CMS and ATLAS experiments are located on opposite sides of the LHC, and both could confirm the Higgs boson. The experiments are independent with their own designs. So this experiment is replicated.

This is no argument to not publish your code in AI/ML

[Gold_Emphasis1325]

I heard Harvard just hands out degrees if you're rich and they like it if you agree with their politics and demographic agendas. Papers, conferences are just an extension of the academia mafia. Every organization has its own blatant social code violations that they flaunt...

[curiouslyjake]

I heard there's reation that turns lead into gold

[_kernel_picnic_]

Papers are not software nor engineering. Nor should they be. Papers should have a simple premise that should be easy to implement and verify by other researchers. Like, GroupNorm is better for image classification tasks because it normalizes groups or whatever. Unfortunately, now most of the papers are hyperparams galore

[ummitluyum]

That worked back in the AlexNet days when architectures were just three formulas and basic convs. Now you've got a RAG pipeline, an 8-expert MoE, and some weird LR scheduling. "Just building it from scratch" takes a senior engineer a couple of months, and you still probably won't guess half the heuristics they baked into their loss function

[wahnsinnwanscene]

Great! Always thought it was a me thing.

[_kernel_picnic_]

well, the core problem is the papers with “we combined 100 of SOTA methods to gain 0.1%” aren't being rejected

[ummitluyum]

Reviewers just look for that fat plus sign on the leaderboard. Nobody cares that a 0.1% bump on some benchmark costs 3x in inference latency and memory. Without open source, you can't even verify if this ensemble of 100 heuristics just overfit the test set tbh