@tiotasram@kolektiva.social2025-07-28 13:04:34
How popular media gets love wrong
Okay, so what exactly are the details of the "engineered" model of love from my previous post? I'll try to summarize my thoughts and the experiences they're built on.
1. "Love" can be be thought of like a mechanism that's built by two (or more) people. In this case, no single person can build the thing alone, to work it needs contributions from multiple people (I suppose self-love might be an exception to that). In any case, the builders can intentionally choose how they build (and maintain) the mechanism, they can build it differently to suit their particular needs/wants, and they will need to maintain and repair it over time to keep it running. It may need winding, or fuel, or charging plus oil changes and bolt-tightening, etc.
2. Any two (or more) people can choose to start building love between them at any time. No need to "find your soulmate" or "wait for the right person." Now the caveat is that the mechanism is difficult to build and requires lots of cooperation, so there might indeed be "wrong people" to try to build love with. People in general might experience more failures than successes. The key component is slowly-escalating shared commitment to the project, which is negotiated between the partners so that neither one feels like they've been left to do all the work themselves. Since it's a big scary project though, it's very easy to decide it's too hard and give up, and so the builders need to encourage each other and pace themselves. The project can only succeed if there's mutual commitment, and that will certainly require compromise (sometimes even sacrifice, though not always). If the mechanism works well, the benefits (companionship; encouragement; praise; loving sex; hugs; etc.) will be well worth the compromises you make to build it, but this isn't always the case.
3. The mechanism is prone to falling apart if not maintained. In my view, the "fire" and "appeal" models of love don't adequately convey the need for this maintenance and lead to a lot of under-maintained relationships many of which fall apart. You'll need to do things together that make you happy, do things that make your partner happy (in some cases even if they annoy you, but never in a transactional or box-checking way), spend time with shared attention, spend time alone and/or apart, reassure each other through words (or deeds) of mutual beliefs (especially your continued commitment to the relationship), do things that comfort and/or excite each other physically (anywhere from hugs to hand-holding to sex) and probably other things I'm not thinking of. Not *every* relationship needs *all* of these maintenance techniques, but I think most will need most. Note especially that patriarchy teaches men that they don't need to bother with any of this, which harms primarily their romantic partners but secondarily them as their relationships fail due to their own (cultivated-by-patriarchy) incompetence. If a relationship evolves to a point where one person is doing all the maintenance (& improvement) work, it's been bent into a shape that no longer really qualifies as "love" in my book, and that's super unhealthy.
4. The key things to negotiate when trying to build a new love are first, how to work together in the first place, and how to be comfortable around each others' habits (or how to change those habits). Second, what level of commitment you have right now, and what how/when you want to increase that commitment. Additionally, I think it's worth checking in about what you're each putting into and getting out of the relationship, to ensure that it continues to be positive for all participants. To build a successful relationship, you need to be able to incrementally increase the level of commitment to one that you're both comfortable staying at long-term, while ensuring that for both partners, the relationship is both a net benefit and has manageable costs (those two things are not the same). Obviously it's not easy to actually have conversations about these things (congratulations if you can just talk about this stuff) because there's a huge fear of hearing an answer that you don't want to hear. I think the range of discouraging answers which actually spell doom for a relationship is smaller than people think and there's usually a reasonable "shoulder" you can fall into where things aren't on a good trajectory but could be brought back into one, but even so these conversations are scary. Still, I think only having honest conversations about these things when you're angry at each other is not a good plan. You can also try to communicate some of these things via non-conversational means, if that feels safer, and at least being aware that these are the objectives you're pursuing is probably helpful.
I'll post two more replies here about my own experiences that led me to this mental model and trying to distill this into advice, although it will take me a moment to get to those.
#relationships #love
@arXiv_quantph_bot@mastoxiv.page2025-08-06 08:17:20
Stabilizing ergotropy in Spin-Chain Quantum Batteries via Energy-Invariant Catalysis under Strong Non-Markovian Coupling
Shun-Cai Zhao, Liang Luo, Ni-Ya Zhuang
https://arxiv.org/abs/2508.02772
@tiotasram@kolektiva.social2025-09-14 12:01:38
TL;DR: what if instead of denying the harms of fascism, we denied its suppressive threats of punishment
Many of us have really sharpened our denial skills since the advent of the ongoing pandemic (perhaps you even hesitated at the word "ongoing" there and thought "maybe I won't read this one, it seems like it'll be tiresome"). I don't say this as a preface to a fiery condemnation or a plea to "sanity" or a bunch of evidence of how bad things are, because I too have honed my denial skills in these recent years, and I feel like talking about that development.
Denial comes in many forms, including strategic information avoidance ("I don't have time to look that up right now", "I keep forgetting to look into that", "well this author made a tiny mistake, so I'll click away and read something else", "I'm so tired of hearing about this, let me scroll farther", etc.) strategic dismissal ("look, there's a bit of uncertainty here, I should ignore this", "this doesn't line up perfectly with my anecdotal experience, it must be completely wrong", etc.) and strategic forgetting ("I don't remember what that one study said exactly; it was painful to think about", "I forgot exactly what my friend was saying when we got into that argument", etc.). It's in fact a kind of skill that you can get better at, along with the complementary skill of compartmentalization. It can of course be incredibly harmful, and a huge genre of fables exists precisely to highlight its harms, but it also has some short-term psychological benefits, chiefly in the form of muting anxiety. This is not an endorsement of denial (the harms can be catastrophic), but I want to acknowledge that there *are* short-term benefits. Via compartmentalization, it's even possible to be honest with ourselves about some of our own denials without giving them up immediately.
But as I said earlier, I'm not here to talk you out of your denials. Instead, given that we are so good at denial now, I'm here to ask you to be strategic about it. In particular, we live in a world awash with propaganda/advertising that serves both political and commercial ends. Why not use some of our denial skills to counteract that?
For example, I know quite a few people in complete denial of our current political situation, but those who aren't (including myself) often express consternation about just how many people in the country are supporting literal fascism. Of course, logically that appearance of widespread support is going to be partly a lie, given how much our public media is beholden to the fascists or outright in their side. Finding better facts on the true level of support is hard, but in the meantime, why not be in denial about the "fact" that Trump has widespread popular support?
To give another example: advertisers constantly barrage us with messages about our bodies and weight, trying to keep us insecure (and thus in the mood to spend money to "fix" the problem). For sure cutting through that bullshit by reading about body positivity etc. is a better solution, but in the meantime, why not be in denial about there being anything wrong with your body?
This kind of intentional denial certainly has its own risks (our bodies do actually need regular maintenance, for example, so complete denial on that front is risky) but there's definitely a whole lot of misinformation out there that it would be better to ignore. To the extent such denial expands to a more general denial of underlying problems, this idea of intentional denial is probably just bad. But I sure wish that in a world where people (including myself) routinely deny significant widespread dangers like COVID-19's long-term risks or the ongoing harms of escalating fascism, they'd at least also deny some of the propaganda keeping them unhappy and passive. Instead of being in denial about US-run concentration camps, why not be in denial that the state will be able to punish you for resisting them?
@arXiv_qbioQM_bot@mastoxiv.page2025-10-03 09:46:41
A Multicentric Dataset for Training and Benchmarking Breast Cancer Segmentation in H&E Slides
Carlijn Lems, Leslie Tessier, John-Melle Bokhorst, Mart van Rijthoven, Witali Aswolinskiy, Matteo Pozzi, Natalie Klubickova, Suzanne Dintzis, Michela Campora, Maschenka Balkenhol, Peter Bult, Joey Spronck, Thomas Detone, Mattia Barbareschi, Enrico Munari, Giuseppe Bogina, Jelle Wesseling, Esther H. Lips, Francesco Ciompi, Fr\'ed\'erique Meeuwsen, Jeroen van der Laak
@arXiv_physicsfludyn_bot@mastoxiv.page2025-08-01 09:04:41
CFDagent: A Language-Guided, Zero-Shot Multi-Agent System for Complex Flow Simulation
Zhaoyue Xu, Long Wang, Chunyu Wang, Yixin Chen, Qingyong Luo, Hua-Dong Yao, Shizhao Wang, Guowei He
https://arxiv.org/abs/2507.23693
@tiotasram@kolektiva.social2025-07-31 16:25:48
LLM coding is the opposite of DRY
An important principle in software engineering is DRY: Don't Repeat Yourself. We recognize that having the same code copied in more than one place is bad for several reasons:
1. It makes the entire codebase harder to read.
2. It increases maintenance burden, since any problems in the duplicated code need to be solved in more than one place.
3. Because it becomes possible for the copies to drift apart if changes to one aren't transferred to the other (maybe the person making the change has forgotten there was a copy) it makes the code more error-prone and harder to debug.
All modern programming languages make it almost entirely unnecessary to repeat code: we can move the repeated code into a "function" or "module" and then reference it from all the different places it's needed. At a larger scale, someone might write an open-source "library" of such functions or modules and instead of re-implementing that functionality ourselves, we can use their code, with an acknowledgement. Using another person's library this way is complicated, because now you're dependent on them: if they stop maintaining it or introduce bugs, you've inherited a problem, but still, you could always copy their project and maintain your own version, and it would be not much more work than if you had implemented stuff yourself from the start. It's a little more complicated than this, but the basic principle holds, and it's a foundational one for software development in general and the open-source movement in particular. The network of "citations" as open-source software builds on other open-source software and people contribute patches to each others' projects is a lot of what makes the movement into a community, and it can lead to collaborations that drive further development. So the DRY principle is important at both small and large scales.
Unfortunately, the current crop of hyped-up LLM coding systems from the big players are antithetical to DRY at all scales:
- At the library scale, they train on open source software but then (with some unknown frequency) replicate parts of it line-for-line *without* any citation [1]. The person who was using the LLM has no way of knowing that this happened, or even any way to check for it. In theory the LLM company could build a system for this, but it's not likely to be profitable unless the courts actually start punishing these license violations, which doesn't seem likely based on results so far and the difficulty of finding out that the violations are happening. By creating these copies (and also mash-ups, along with lots of less-problematic stuff), the LLM users (enabled and encouraged by the LLM-peddlers) are directly undermining the DRY principle. If we see what the big AI companies claim to want, which is a massive shift towards machine-authored code, DRY at the library scale will effectively be dead, with each new project simply re-implementing the functionality it needs instead of every using a library. This might seem to have some upside, since dependency hell is a thing, but the downside in terms of comprehensibility and therefore maintainability, correctness, and security will be massive. The eventual lack of new high-quality DRY-respecting code to train the models on will only make this problem worse.
- At the module & function level, AI is probably prone to re-writing rather than re-using the functions or needs, especially with a workflow where a human prompts it for many independent completions. This part I don't have direct evidence for, since I don't use LLM coding models myself except in very specific circumstances because it's not generally ethical to do so. I do know that when it tries to call existing functions, it often guesses incorrectly about the parameters they need, which I'm sure is a headache and source of bugs for the vibe coders out there. An AI could be designed to take more context into account and use existing lookup tools to get accurate function signatures and use them when generating function calls, but even though that would probably significantly improve output quality, I suspect it's the kind of thing that would be seen as too-baroque and thus not a priority. Would love to hear I'm wrong about any of this, but I suspect the consequences are that any medium-or-larger sized codebase written with LLM tools will have significant bloat from duplicate functionality, and will have places where better use of existing libraries would have made the code simpler. At a fundamental level, a principle like DRY is not something that current LLM training techniques are able to learn, and while they can imitate it from their training sets to some degree when asked for large amounts of code, when prompted for many smaller chunks, they're asymptotically likely to violate it.
I think this is an important critique in part because it cuts against the argument that "LLMs are the modern compliers, if you reject them you're just like the people who wanted to keep hand-writing assembly code, and you'll be just as obsolete." Compilers actually represented a great win for abstraction, encapsulation, and DRY in general, and they supported and are integral to open source development, whereas LLMs are set to do the opposite.
[1] to see what this looks like in action in prose, see the example on page 30 of the NYTimes copyright complaint against OpenAI (#AI #GenAI #LLMs #VibeCoding
@arXiv_physicsoptics_bot@mastoxiv.page2025-07-29 11:08:51
Long cavity spectral disperser at sub-picometer resolution. Design and analysis
Fran\c{c}ois H\'enault, Yan Feng
https://arxiv.org/abs/2507.20986 https://
@arXiv_physicsoptics_bot@mastoxiv.page2025-09-15 08:15:01
Multi-Channel Microwave-to-Optics Conversion Utilizing a Hybrid Photonic-Phononic Waveguide
Yuan-Hao Yang, Jia-Qi Wang, Zheng-Xu Zhu, Yu Zeng, Ming Li, Yan-Lei Zhang, Juanjuan Lu, Qiang Zhang, Weiting Wang, Chun-Hua Dong, Xin-Biao Xu, Guang-Can Guo, Luyan Sun, Chang-Ling Zou
https://arxiv.org/abs/2509.10052…
