D-Machine

I fear you are right here, and that the problem is far more dire than much of academia realizes. I know enough highly intelligent people (some even with family / spouses in academia, surprisingly) that are otherwise very e.g. left / liberal / progressive and open, that are still basically saying academia needs to be gutted / burned down.

I've no idea what the actual stats are on faith in academia overall today, but I don't think it is looking good.

I think people in this post are using arXiv as sort of metonymy / stand-in for OA here, but, yes.

Ah, look, another smug sneer that ignores the evidence I presented, and makes another circular argument (i.e. that because academics look at rep, this is justified, even though I provided evidence disputing this).

I know what journals are better / not. But reputation only is helpful in letting you ignore trash journals, once you are out of trash land, rep is just not a very meaningful factor, and you have to focus on methodology and substance.

Do you not notice the circularity of your reasoning here?

Also I didn't say incompetent, I said "not very". More competent researchers make journal rep only a very small factor, and it is not via the "high rep = more trustworthy" direction (which is the bad heuristic), it is "pay-to-publish journals = not trustworthy" (better heuristic).

Once you have ruled out a publication being in a trash journal, reputation is only a very minor factor in consideration, and methodological and substantive issues are what matter.

> The naive public does not believe anything in particular about peer review

You'd need to provide evidence or an argument for this. The media reports on things in part based on journal prestige, and likely when questioned, people will say they can trust such things because good scientists have looked at the work and say it is good. This would be an implicit belief that peer review is generally working well, even if they don't use the term "peer review".

> You seem stuck somewhere in the middle, caring deeply about a system you don’t seem to fully understand.

Extremely presumptuous, as I work in this system, and have provided plenty of evidence for my claims. You've provided only sneers.

Oh, I agree this is all super complex and delicate. If I had more time, I'd love to write a more nuanced, many-thousands-of-words blog post going into which journals and fields actually have good peer review and can be more / less trusted.

I just wanted to make a strong rhetorical case by highlighting some things that might be surprising to people making more naive defenses of journals via peer-review-based arguments.

> Because a lot of people are deeply invested in the present system perhaps?

I mean, right, yes, of course. Much of the downvotes are cognitive dissonance, obviously. I suppose I meant the question rhetorically.

> Your argument depends on worse peer review at top journals - but fundamentally, you fail to show how doing any peer review is strictly worse than doing no peer review.

No, it doesn't. The argument is that peer review is incompetent gatekeeping in general, and so slows things down and makes thing expensive. Also, I am countering the argument "we need journals because journals do peer review" by arguing "peer review by journals isn't clearly actually good", I am not saying "peer review in general is unneeded", as I support review by the entire scientific community, rather than journal gatekeepers.

> you fail to show how doing any peer review is strictly worse than doing no peer review

I wasn't trying to show that. I have provided plenty of arguments to show why killing journal-based peer review could definitely speed things up and so potentially make things better. I want actual organic review by the community, not by tiny groups of gatekeepers.

Because there isn't such a relation. It's a thing people believe when they don't have actual experience with peer review. If anything, predatory journals and low-quality pubs can charge more, since publication is more guaranteed (and researchers reaching for these pay-to-publish journals are more desperate).

> The first thing an academic does is check where a paper is published, before even reading it. It's a crutch

IMO, academics that do this are not very competent, because we have plenty of research suggesting that higher-profile journals are in fact less trustworthy in many ways, or that there is no correlation at all between reputation and quality (see my other post here in this thread).

Yes, some trash journals publish all trash, but, beyond that, competent researchers scan the abstract, look at sample sizes and basic stats, and if those check out, you skip to the methods and look for red flags there. Also, most early publications will be on an arXiv-like place anyway so you can't look to reputation yet.

Likewise, serious analytic reviews like meta-analyses don't factor in e.g. impact factor or paper citations, since that would be nonsense. They focus on methodology and stats.

I really think we ought to shame academics that are filtering papers based on journal alone, it is almost always the wrong way to make a quick judgement.

> How does that have anything to do with peer review?

I already addressed this. People know peer review can be bad, but some think "good journals" still do good peer review. This is not so clear.

> In what world does the arxiv system moderate this discrepancy?

Open systems allow the scientific community to figure out ways to properly assess research quality and value more cheaply, and without passing through (often arbitrary and random) small numbers of gatekeepers that don't even do a reliable or good job gatekeeping in the first place.

Ah, but the naive public still broadly believes in peer review, and that high profile journals do good review. And the prominence and reputation that comes from these journals arguably then relies on this (increasingly false) public perception.

Would scientists feel the same if the public was more educated about how bad journals and peer review are? Not so easy to disentangle IMO.

Don't know why you are being downvoted, you are largely correct. I've provided plenty of evidence in another post in this thread showing that journal-based peer review is highly farcical.

EDIT: I still want review from a community of scientific peers. I just don't want this review to be in the hands of a tiny number of gatekeepers entangled with journals that largely just slow things down.

The other factor preventing a fix is that people with no actual serious experience of academic publishing and peer review will defend these journals, because they still think that (journal-based) peer review acts like some kind of meaningful quality filter. But, it really doesn't.

Because someone is surely going to try to defend journals via peer review in this thread, I want to provide a counter to the arguments that journal peer review does much good. Also, since everyone knows that if you just go to a poor enough journal, you can be published, I am going to focus on the (IMO mostly false) claim that higher-profile journals are still doing a good thing here.

There are numerous studies showing that higher-profile journals in general have more retractions and research misconduct [1-2], lower research quality [3], in fact weaker statistical power and reliability [4], and that statistical reliability even in high prestige journals is still extremely poor overall [5]. Also, making it through peer review is highly random and dependent on who you get as a reviewer [6], or is just basically a coin toss even when looking at reviewer groups:

    In 2014, 49.5% of the papers accepted by the first committee were rejected by the second (with a fairly wide confidence interval as the experiment included only 116 papers).  This year, this number was 50.6%.  We can also look at the probability that a randomly chosen rejected paper would have been accepted if it were re-reviewed.  This number was 14.9% this year, compared to 17.5% in 2014. [7]

We should just move to arXiv-like approaches and allow the scientific community to broadly judge relevance and quality. Journals just slow things down and burn funding for very little gain or benefit to anyone other than the journal owners.

[1] https://pmc.ncbi.nlm.nih.gov/articles/PMC3187237/

[2] https://www.pnas.org/doi/10.1073/pnas.1212247109

[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC9382220/

[4] https://journals.plos.org/plosbiology/article?id=10.1371%2Fj...

[5] https://www.frontiersin.org/journals/human-neuroscience/arti...

[6] https://journals.plos.org/plosone/article?id=10.1371%2Fjourn...

[7] https://blog.neurips.cc/2021/12/08/the-neurips-2021-consiste...

> I'm speaking to the difference between, say, a reactive perception model (which is a model) and an abstracted cognition model (which is also a model, but one that can be interacted with without external input). It is clear almost all animals have the former, and a couple might have the latter, but this distinction is a core concern for usage of abstract systems such as linguistics.

This distinction seems perfectly fine and clear to me, so I suspect we probably actually don't disagree that much on specifics, and that this was maybe a semantic / violent agreement thing.

I still don't think this distinction helps defend your statement "Language constrains your perception of reality to only the set of concepts conceivable within that language.", because, obviously, you can have abstract cognitive models ('concepts') that are non-linguistic, and thus, your perception of reality is not constrained only by language. I.e. remove the "only" and I have no real substantive disagreement.

So it seems we can probably mostly agree on something like "Your abstractions constrain your perception of reality to only the set of concepts [ideas, representations] conceivable from those abstractions".

It is tricky because "concept" can strongly imply a linguistic model, and "model" is really the term we want here, e.g. a "visuospatial concept" is unusual, I admit. But, still, linguistic models are definitely not the only game in town re: perception.

> But they are fundamentally different in kind, in that those models respond to immediate sensory stimuli. Per se, they do not allow for abstract reasoning over internally generated representations

This is obviously deeply incorrect, and the kind of thing that people call "wordcel" thinking. Mathematical intelligence is highly visuospatial, and often requires constructing images and/or imagining motion (arguably invoking also either proprioceptive and/or kinaesthetic qualia).

Is it possible you are aphantasic? This seems to me to be the only way one could think that one cannot have non-verbal internally generated representations.

> Personality isn't an internal property - it's a judgment made by people watching behavior.

Partly, yes, but personality is also an internal property, or it is coherent and correct enough to generally say that it has internal aspects. I.e. a person's personality is the set of (relatively) stable and difficult-to-change patterns that manifest in their behaviour in broad contexts, and these patterns are almost certainly encoded internally in the brain in some form. It is not much different than saying a person's intelligence / IQ is partly internal.

Otherwise, I do agree with your more careful framing, and I wish people thought and spoke more carefully about these things, and doubly so for LLMs.

> Another thing to consider about LLMs is that the nature of the training and the core capability of transformers is to mimic the function of the processes by which the training data was produced; by training on human output, these LLMs are in many cases implicitly modeling the neural processes in human brains which resulted in the data. Lots of hacks, shortcuts, low resolution "good enough" approximations, but in some cases, it's uncovering precisely the same functions that we use in processing and producing information.

I would argue this is deeply false, my classic go-to examples being that neural networks have almost no real relations to any aspects of actual brains [1] and that modeling even a single cortical neuron requires an entire, fairly deep neural network [2]. Neural nets really have nothing to do with brains, although brains may have loosely inspired the earliest MLPs. Really NNs are just very powerful and sophisticated curve (manifold) fitters.

> Could be very TARS like, lol.

I just rewatched Interstellar recently and this is such a lovely thought in response to the paper!

[1] https://en.wikipedia.org/wiki/Biological_neuron_model

[2] https://www.sciencedirect.com/science/article/pii/S089662732...

Yup, I agree it is a general problem, and related to a tendency to over-anthropomorphize. At least in this case there was still something pretty good in the paper anyway.

> Disproven by whom and under which context?

See any of my links, but especially the third. Animal cognition and human neuroscience studies strongly disprove the importance of language to cognition. Conflating language and thought is so obviously false in 2026 it is extraordinary that people still think like this.

I was ignoring the comment about fascists because it is simplistic and low-quality, and will similarly not be responding to whatever you (incorrectly) think I was claiming about universal human rights. I only wanted to correct the extremely false (or at least hugely overstated) assumptions about language and perception of reality.

> Now LLMs may not be a model for how we do it but they are certainly going to bring back structuralist and "wordcel" positions because they do seem to show, somehow, that "language is all you need" to accomplish whatever it is LLMs accomplish.

People will try to bring back these obviously false models of cognition, but, so far, the dismal performance of LLMs on e.g. SpatialBench [1], and, almost certainly ARC-AGI-3, or e.g. the kind of data and effort required to get something like V-JEPA-2 [2], will be strong counter-examples to this. And, yeah, obviously animal cognition, esp. smart animals like birds, or the crazy stuff we see in chimp and gorilla ethology (border patrols, genocides, humor, theory of mind, bla bla bla).

[1] https://spicylemonade.github.io/spatialbench/

[2] https://arxiv.org/abs/2506.09985

And we should IMO resist Anthropic wanting us to anthropomorphize it, because Claude is not a person with a gender!

The use of "him" by GP is extremely unusual IMO, and I suspect is odd for anyone with English as their native language. The current convention among normal people seems to me to be to avoid pronouns other than "it" with these tools, and generally just use the name. The name is not really relevant: like, sure, in some contexts we think of ships as "she/her", and may prefer feminine names for them, but if you used e.g. "she" rather than "it" to refer to the Titanic or any other ship with a female name, this is going to cause some double-takes / disfluent comprehension in the vast majority of native speakers in most cases.

Only if you imagine e.g. some stereotypical pirate with an eyepatch slapping the hull and saying something like "Aye, but she weathered the storm, as she always does" might this feel normal. Or, maybe if you are a Redditor and trying to make it your AI boyfriend / girlfriend, you can use he/him or some other neo-pronoun, but this is currently abnormal and not the general context.

And the fact that you can make the model act as any gender again shows why choosing "him" as some default here is strange. Absent any specific context, the choice of "him" here is poorly justified.

It's not even really the researchers' fault, academic psychological personality research is in general philosophically very weak / poor, in that they also almost always conflate "models of / talking about personality" with actual personality, and rarely actually check if things like the MBTI or Five-Factor Model actually correlate meaningfully with real behaviours.

Those that do find correlations between self-reported personality and actual behaviours tend to find those to be in a range of something like 0.0 to 0.3 or so, maybe 0.4 if you are really lucky. Which means "personality" measured this way is explaining something like 16% of the variance in behaviour, at max.

It is a wordcel problem, i.e. the belief that language is all there is for modeling reality, even though this is obviously false and has been clearly disproven by decades of research in psychology, cognitive science, and neuroscience. At best we can say that sometimes language has a strong influence on our perceptions of reality.

EDIT: For a neuroscience reference that also argues why the general perspective is obviously false: https://pmc.ncbi.nlm.nih.gov/articles/PMC4874898/. But really, these things ought to be obvious from introspection.

This is IMO largely false, and empirically things like Sapir-Worf and strong linguistic relativism, or that language == thought are widely considered disproven [1-3].

This is also sort of a wordcel take, in that it neglects that there are plenty of mental structures that are not solely linguistic. I.e. visuo-spatial models, auditory models, kinaesthetic, proprioceptive, emotional, gustatory, or even maybe intuitive models, and symbolic models (which have both linguistic and visuo-spatial aspects). Yes, your models constrain your perception of reality, but it is not clear how important language really is to many of those models (and there is strong evidence it may not matter at all to a lot of cognition [3]).

[1] https://en.wikipedia.org/wiki/Linguistic_relativity

[2] https://plato.stanford.edu/archives/sum2015/entries/relativi...

[3] https://pmc.ncbi.nlm.nih.gov/articles/PMC4874898/

The personality thing seems kind of tautological / uninteresting, as I have pointed out before: https://news.ycombinator.com/item?id=46905692.

Psychological instruments and concepts (like MBTI) are constructed from the semantics of everyday language. Personality models (being based on self-report, and not actual behaviour) are not models of actual personality, but the correlation patterns in the language used to discuss things semantically related to "personality". It would be thus extremely surprising if LLM-output patterns (trained on people's discussions and thinking about personality) would not also result in learning similar correlational patterns (and thus similar patterns of responses when prompted with questions from personality inventories).

The real and more interesting part of the paper is the use of statistical techniques to isolate sub-networks which can then be used to emit outputs more consistent with some desired personality configuration. There is no obvious reason to me that this couldn't be extended to other types of concepts, and it kind reads to me like a way of doing a very cheap, training-free sort of "fine-tuning".

Part of the reason there is too much code to read and review is because we lack the information to contextualize that code.

In many cases, seeing the prompts would help to dramatically speed up rejecting lazy slop PRs (or accepting more careful AI-assisted PRs).

Because "him" is objectively wrong, under almost any interpretation of any words involved. You can cause Claude, or any text-based LLM, to emit language that matches almost any personality / gender / character in the training set. At best you might be able to say "the default outputs have a masculine tone / vibe", but this still doesn't justify, by modern discourse, the "him".

Appreciate this very sane take. The actual code always is more important than the intentions, and this is basically tautological.

When dealing with a particularly subtle / nuanced issue, knowing the intentions is still invaluable, but this is usually rare. How often AI code runs you into these issues is currently unclear, and constantly changing (and how often such issue are actually crucial depends heavily on the domain).