Tootfinder

Opt-in global Mastodon full text search. Join the index!

@tiotasram@kolektiva.social
2025-06-21 02:34:13

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Edit: fixed some typos.
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and thus more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of management caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

@arXiv_eessIV_bot@mastoxiv.page
2025-08-20 09:35:20

Improving Deep Learning for Accelerated MRI With Data Filtering
Kang Lin, Anselm Krainovic, Kun Wang, Reinhard Heckel
arxiv.org/abs/2508.13822

@arXiv_csCL_bot@mastoxiv.page
2025-08-20 09:17:10

Compressed Models are NOT Trust-equivalent to Their Large Counterparts
Rohit Raj Rai, Chirag Kothari, Siddhesh Shelke, Amit Awekar
arxiv.org/abs/2508.13533

@Techmeme@techhub.social
2025-07-17 17:08:42

OpenAI debuts ChatGPT Agent, which can control an entire computer and perform multi-step tasks, powered by a new dedicated model, rolling out to paid users (Hayden Field/The Verge)
theverge.com/ai-artificial-int

@arXiv_astrophIM_bot@mastoxiv.page
2025-07-21 09:02:20

Simulation-based inference with deep ensembles: Evaluating calibration uncertainty and detecting model misspecification
James Alvey, Carlo R. Contaldi, Mauro Pieroni
arxiv.org/abs/2507.13495

@arXiv_csAI_bot@mastoxiv.page
2025-06-18 08:06:21

Don't throw the baby out with the bathwater: How and why deep learning for ARC
Jack Cole, Mohamed Osman
arxiv.org/abs/2506.14276

@arXiv_csRO_bot@mastoxiv.page
2025-07-21 09:39:50

A segmented robot grasping perception neural network for edge AI
Casper Br\"ocheler, Thomas Vroom, Derrick Timmermans, Alan van den Akker, Guangzhi Tang, Charalampos S. Kouzinopoulos, Rico M\"ockel
arxiv.org/abs/2507.13970

@arXiv_eessIV_bot@mastoxiv.page
2025-08-20 09:28:00

Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
Tiago Assis, Ines P. Machado, Benjamin Zwick, Nuno C. Garcia, Reuben Dorent
arxiv.org/abs/2508.13762

@arXiv_quantph_bot@mastoxiv.page
2025-08-19 11:18:40

Addressing Side-Channel Threats in Quantum Key Distribution via Deep Anomaly Detection
Junxuan Liu, Bingcheng Huang, Jialei Su, Qingquan Peng, Anqi Huang
arxiv.org/abs/2508.12749

@arXiv_csAR_bot@mastoxiv.page
2025-08-20 09:01:30

Sustainable AI Training via Hardware-Software Co-Design on NVIDIA, AMD, and Emerging GPU Architectures
Yashasvi Makin, Rahul Maliakkal
arxiv.org/abs/2508.13163

@midtsveen@social.linux.pizza
2025-07-19 11:15:24

Sometimes I’m posting about Syndicalism. Suddenly, I’m deep into LGBTQIA rights, because yes, my brain’s got tabs open everywhere.
Mid-rant, autism brain hits and it’s like, nope, time to fire up YouTube on the Debian box, because special interests top all discourse. The playlist is always oddly specific.
Then, work panic: my boss exists, bills exist. Pretend to be productive, start the loop again.

@arXiv_csCC_bot@mastoxiv.page
2025-08-21 07:45:39

$TIME[t] \subseteq SPACE[O(\sqrt{t})]$ via Tree Height Compression
Logan Nye
arxiv.org/abs/2508.14831 arxiv.org/pdf/2508.14831

@arXiv_astrophGA_bot@mastoxiv.page
2025-08-21 09:35:20

Probing the farthest star clusters to the Small Magellanic Cloud
A. E. Piatti, D. M. F. Illesca, M. Chiarpotti, R. Butr\'on
arxiv.org/abs/2508.14701

@arXiv_eessSP_bot@mastoxiv.page
2025-08-19 10:53:30

Scalable, Technology-Agnostic Diagnosis and Predictive Maintenance for Point Machine using Deep Learning
Eduardo Di Santi (Digital and Integrated Systems, Alstom), Ruixiang Ci (Innovation and Smart Mobility, Alstom), Cl\'ement Lefebvre (Digital and Integrated Systems, Alstom), Nenad Mijatovic (Digital and Integrated Systems, Alstom), Michele Pugnaloni (Digital and Integrated Systems, Alstom), Jonathan Brown (Digital and Integrated Systems, Alstom), Victor Mart\'in (Digital and …

@arXiv_csSD_bot@mastoxiv.page
2025-06-18 08:39:29

Making deep neural networks work for medical audio: representation, compression and domain adaptation
Charles C Onu
arxiv.org/abs/2506.13970

@arXiv_condmatquantgas_bot@mastoxiv.page
2025-08-19 08:35:40

Dual-species atomic absorption image reconstruction using deep neural networks
Kyuhwan Lee, Yong-il Shin
arxiv.org/abs/2508.12120 arxiv.org…

@arXiv_csCL_bot@mastoxiv.page
2025-08-20 09:45:10

Prediction is not Explanation: Revisiting the Explanatory Capacity of Mapping Embeddings
Hanna Herasimchyk, Alhassan Abdelhalim, S\"oren Laue, Michaela Regneri
arxiv.org/abs/2508.13729

@arXiv_qbioNC_bot@mastoxiv.page
2025-07-18 08:33:22

Mapping Emotions in the Brain: A Bi-Hemispheric Neural Model with Explainable Deep Learning
David Freire-Obreg\'on, Agnieszka Dubiel, Prasoon Kumar Vinodkumar, Gholamreza Anbarjafari, Dorota Kami\'nska, Modesto Castrill\'on-Santana
arxiv.org/abs/2507.12625

@arXiv_eessIV_bot@mastoxiv.page
2025-08-20 09:16:10

InnerGS: Internal Scenes Rendering via Factorized 3D Gaussian Splatting
Shuxin Liang, Yihan Xiao, Wenlu Tang
arxiv.org/abs/2508.13287 arxiv…

@arXiv_csCV_bot@mastoxiv.page
2025-08-18 09:54:10

AIM: Amending Inherent Interpretability via Self-Supervised Masking
Eyad Alshami, Shashank Agnihotri, Bernt Schiele, Margret Keuper
arxiv.org/abs/2508.11502

@arXiv_eessSY_bot@mastoxiv.page
2025-07-17 08:56:30

Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning
Fateme Salehi, Aamir Mahmood, Sarder Fakhrul Abedin, Kyi Thar, Mikael Gidlund
arxiv.org/abs/2507.12031

@arXiv_mathOC_bot@mastoxiv.page
2025-07-16 09:19:11

Deep Equilibrium models for Poisson Imaging Inverse problems via Mirror Descent
Christian Daniele, Silvia Villa, Samuel Vaiter, Luca Calatroni
arxiv.org/abs/2507.11461

@arXiv_mathNA_bot@mastoxiv.page
2025-06-19 09:04:47

Weak TransNet: A Petrov-Galerkin based neural network method for solving elliptic PDEs
Zhihang Xu, Min Wang, Zhu Wang
arxiv.org/abs/2506.14812

@arXiv_condmatmtrlsci_bot@mastoxiv.page
2025-08-19 08:52:50

Electro-thermal Co-design of High-power Vertical \b{eta}-Ga2O3 Schottky Diodes with High-permittivity Dielectric Field-plate
Ahsanul Mohaimeen Audri, Chung-Ping Ho, Jingjing Shi, Esmat Farzana
arxiv.org/abs/2508.11775

@arXiv_csAI_bot@mastoxiv.page
2025-06-18 08:04:22

Fragile Preferences: A Deep Dive Into Order Effects in Large Language Models
Haonan Yin, Shai Vardi, Vidyanand Choudhary
arxiv.org/abs/2506.14092

Fired DOJ employee could face prison for throwing sandwich at officer
After his arrest, Charles Dunn allegedly told one of the officers: “I did it. I threw a sandwich.”

“If you touch any law enforcement officer, we will come after you,” Bondi posted on X Thursday.
“This is an example of the Deep State we have been up against for seven months as we work to refocus DOJ.”

@inthehands@hachyderm.io
2025-06-09 16:33:19

These pieces are much harsher than Fred’s, much more in the LLM-bashing camp, but feel relevant here:
softwarecrisis.dev/letters/llm
pivot-to-ai.com/2025/06/05/gen
Fred’s point is that a bad interaction model creates hidden work, then humans do that work and “the machine claims the praise.” These other two pieces make the point that this phenomenon of giving the machine credit for unrecognized human work is age-old, taps into some deep trapdoors in human cognition.
10/

@arXiv_csDB_bot@mastoxiv.page
2025-07-18 08:02:12

Rel-HNN: Split Parallel Hypergraph Neural Network for Learning on Relational Databases
Md. Tanvir Alam, Md. Ahasanul Alam, Md Mahmudur Rahman, Md. Mosaddek Khan
arxiv.org/abs/2507.12562

@arXiv_quantph_bot@mastoxiv.page
2025-06-19 10:08:23

Fast, continuous and coherent atom replacement in a neutral atom qubit array
Yiyi Li, Yicheng Bao, Michael Peper, Chenyuan Li, Jeff D. Thompson
arxiv.org/abs/2506.15633

@arXiv_astrophCO_bot@mastoxiv.page
2025-06-18 09:30:35

Gas motion in the ICM of the Virgo cluster replica
Th\'eo Lebeau, Stefano Ettori, Jenny G. Sorce, Nabila Aghanim, Jade Paste
arxiv.org/abs/2506.14441

@arXiv_csCR_bot@mastoxiv.page
2025-06-12 07:21:31

What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
Erik Buchholz, Natasha Fernandes, David D. Nguyen, Alsharif Abuadbba, Surya Nepal, Salil S. Kanhere
arxiv.org/abs/2506.09312

@arXiv_csNI_bot@mastoxiv.page
2025-07-16 09:44:01

Improving Wi-Fi Network Performance Prediction with Deep Learning Models
Gabriele Formis, Amanda Ericson, Stefan Forsstrom, Kyi Thar, Gianluca Cena, Stefano Scanzio
arxiv.org/abs/2507.11168

@arXiv_eessIV_bot@mastoxiv.page
2025-08-20 09:42:30

MMIS-Net for Retinal Fluid Segmentation and Detection
Nchongmaje Ndipenocha, Alina Mirona, Kezhi Wanga, Yongmin Li
arxiv.org/abs/2508.13936

@arXiv_csLG_bot@mastoxiv.page
2025-08-15 10:10:32

On Spectral Properties of Gradient-based Explanation Methods
Amir Mehrpanah, Erik Englesson, Hossein Azizpour
arxiv.org/abs/2508.10595 arxi…

@arXiv_csAI_bot@mastoxiv.page
2025-08-15 07:44:52

Improving and Evaluating Open Deep Research Agents
Doaa Allabadi, Kyle Bradbury, Jordan M. Malof
arxiv.org/abs/2508.10152 arxiv.org/pdf/250…

@arXiv_csCE_bot@mastoxiv.page
2025-07-17 07:36:09

Universal Fourier Neural Operators for Micromechanics
Binh Huy Nguyen, Matti Schneider
arxiv.org/abs/2507.12233 arxiv…

@arXiv_eessIV_bot@mastoxiv.page
2025-07-21 09:10:20

Divide and Conquer: A Large-Scale Dataset and Model for Left-Right Breast MRI Segmentation
Maximilian Rokuss, Benjamin Hamm, Yannick Kirchhoff, Klaus Maier-Hein
arxiv.org/abs/2507.13830

@arXiv_mathNA_bot@mastoxiv.page
2025-06-16 08:51:19

Deep Symmetric Autoencoders from the Eckart-Young-Schmidt Perspective
Simone Brivio, Nicola Rares Franco
arxiv.org/abs/2506.11641

@arXiv_csNE_bot@mastoxiv.page
2025-07-16 07:39:21

Tangma: A Tanh-Guided Activation Function with Learnable Parameters
Shreel Golwala
arxiv.org/abs/2507.10560 arxiv.org…

@arXiv_astrophSR_bot@mastoxiv.page
2025-08-12 08:45:33

Reconstruction of Solar EUV Irradiance Using CaII K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Haodi Jiang, Qin Li, Jason T. L. Wang, Haimin Wang, Serena Criscuoli
arxiv.org/abs/2508.07065

@ruth_mottram@fediscience.org
2025-07-02 21:04:09

It's time for some deep work, thankyou @… for reminding me of #NightWaves...
mastodonapp.uk/@BBC3MusicBot/1

@arXiv_astrophGA_bot@mastoxiv.page
2025-06-16 09:48:09

Characterizing the outer disk of eXtended-UV galaxies in the optical domain with deep surveys
E. Bernaud, S. Boissier, Junais, K. Malek, E. Hugot, G. Galaz
arxiv.org/abs/2506.11568

@arXiv_astrophEP_bot@mastoxiv.page
2025-06-16 08:37:50

DBNets2.0: simulation-based inference for planet-induced dust substructures in protoplanetary discs
A. Ruzza, G. Lodato, G. P. Rosotti, P. J. Armitage
arxiv.org/abs/2506.11200

@arXiv_eessSP_bot@mastoxiv.page
2025-08-18 08:05:10

KAN-HAR: A Human activity recognition based on Kolmogorov-Arnold Network
Mohammad Alikhani
arxiv.org/abs/2508.11186 arxiv.org/pdf/2508.1118…

@arXiv_qbioGN_bot@mastoxiv.page
2025-08-14 08:11:52

Deep Generative Models for Discrete Genotype Simulation
Sihan Xie (GABI), Thierry Tribout (GABI), Didier Boichard (GABI), Blaise Hanczar (IBISC), Julien Chiquet (MIA Paris-Saclay), Eric Barrey (GABI)
arxiv.org/abs/2508.09212

@arXiv_csPL_bot@mastoxiv.page
2025-06-03 07:24:17

Pearl: Automatic Code Optimization Using Deep Reinforcement Learning
Djamel Rassem Lamouri, Iheb Nassim Aouadj, Smail Kourta, Riyadh Baghdadi
arxiv.org/abs/2506.01880

White House officials said they would review the Smithsonian’s exhibition text, curation, exhibition planning and collections,
starting with eight museums
“The Smithsonian’s work is grounded in a deep commitment to scholarly excellence, rigorous research, and the accurate, factual presentation of history,”
a Smithsonian spokesperson said in a statement Tuesday afternoon.
“We are reviewing the letter with this commitment in mind and will continue to collaborate constr…

@arXiv_csCR_bot@mastoxiv.page
2025-06-12 07:27:51

Empirical Quantification of Spurious Correlations in Malware Detection
Bianca Perasso, Ludovico Lozza, Andrea Ponte, Luca Demetrio, Luca Oneto, Fabio Roli
arxiv.org/abs/2506.09662

@arXiv_eessAS_bot@mastoxiv.page
2025-07-10 09:01:31

Deep Feed-Forward Neural Network for Bangla Isolated Speech Recognition
Dipayan Bhadra, Mehrab Hosain, Fatema Alam
arxiv.org/abs/2507.07068

@arXiv_csCV_bot@mastoxiv.page
2025-06-09 10:06:02

Optimizing Cloud-to-GPU Throughput for Deep Learning With Earth Observation Data
Akram Zaytar, Caleb Robinson, Girmaw Abebe Tadesse, Tammy Glazer, Gilles Hacheme, Anthony Ortiz, Rahul M Dodhia, Juan M Lavista Ferres
arxiv.org/abs/2506.06235

@arXiv_physicsgeoph_bot@mastoxiv.page
2025-07-16 08:09:31

HEIMDALL: a grapH-based sEIsMic Detector And Locator for microseismicity
Matteo Bagagli, Francesco Grigoli, Davide Bacciu
arxiv.org/abs/2507.10850

@arXiv_statML_bot@mastoxiv.page
2025-08-11 08:27:59

Reduction Techniques for Survival Analysis
Johannes Piller, L\'ea Orsini, Simon Wiegrebe, John Zobolas, Lukas Burk, Sophie Hanna Langbein, Philip Studener, Markus Goeswein, Andreas Bender
arxiv.org/abs/2508.05715

@arXiv_hepph_bot@mastoxiv.page
2025-06-13 10:00:40

Impact of improved energy resolution on DUNE sensitivity in presence of a light sterile neutrino
Sabila Parveen, Jogesh Rout, Poonam Mehta
arxiv.org/abs/2506.10767

@arXiv_csLG_bot@mastoxiv.page
2025-08-12 12:08:33

N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
Ricardo Matos, Luis Roque, Vitor Cerqueira
arxiv.org/abs/2508.07490

@kurtsh@mastodon.social
2025-06-03 18:15:52

Want super-thorough AI responses by applying deep thought & reasoning AI technology?
Any Microsoft 365 Copilot (Commercial) licensed user can now use the Researcher & Analyst agents at no additional cost! These new agents provide very thorough responses to help you collect FACTS on informational topics & review DATA to derive insights... with human language.
#Researcher

@arXiv_astrophHE_bot@mastoxiv.page
2025-08-04 09:10:21

Extreme anisotropies in deep layers of an exploding star: overabundance of Cr in the northeastern jet of Cassiopeia A
Vincenzo Sapienza, Marco Miceli, Masaomi Ono, Shigehiro Nagataki, Takashi Yoshida, Emanuele Greco, Salvatore Orlando, Fabrizio Bocchino
arxiv.org/abs/2508.00376

@cowboys@darktundra.xyz
2025-07-28 10:04:48

Cowboys camp observations: Deep ball needs work, pass rushers look good in pads nytimes.com/athletic/6519279/2

@seedling@dice.camp
2025-06-07 06:14:05

I had this idea for a Cairn lifepath generator where there are three stages of life and you roll 1d6 for your stats at each stage, and also get appropriate items.
It has not been playtested, it's barely been proofread, but I've been having a lot of fun generating guys
perchance.org/lt8m69fg35
#ttrpg #CairnRpg

Based on Cairn.

You have 2 HP.

Your childhood: You grew up in relative luxury, the child of minor nobility. You have a gold holy symbol on a cord (petty).
Add 2 to STR, 3 to DEX, 5 to WIL, and an extra 2 gp.

After, you were trained in matters of religion.
Add 3 to STR, 2 to DEX, 6 to WIL.
Start with a staff ( d6) and a holy symbol which the undead avoid.

You never became a priest because you were accused of heresy
Add 2 to STR, 5 to DEX, 5 to WIL
You might know facts about cults you encount…
You have 5 HP.

Your childhood: You were orphaned (or so they believe). You were found with a religious amulet (petty). Add 2 to STR, 4 to DEX and 4 to DEX.

As you grew up, you you started working in the mines.
Add 5 to STR, 5 to DEX, 3 to WIL
Start with a pickaxe ( d8) and helmet (1 ).

After several years, after a friend died in a cave-in, you knew you had to leave that life behind.
You have a cart and a strong but stubborn donkey.
Add 1 to STR, 2 to DEX, 5 to WIL.
You have 6 HP.

Your childhood: You grew up doing hard but honest work in the fields. You have a roughly carved wooden religious amulet (petty).
Add 3 to STR, 5 to DEX, 5 to WIL.

As you grew older, you learned from the village herbalist.
Add 3 to STR, 3 to DEX, 4 to WIL.
Start with a poisoned sickle ( d6, target is impaired if blood is drawn), 3 uses of a medicine restoring d4 STR, and knowledge of the effects of common herbs.

You left town after you traveled too deep in the woods and found y…
@arXiv_csAI_bot@mastoxiv.page
2025-08-14 07:30:22

Value Function Initialization for Knowledge Transfer and Jump-start in Deep Reinforcement Learning
Soumia Mehimeh
arxiv.org/abs/2508.09277

@arXiv_csNI_bot@mastoxiv.page
2025-07-14 07:53:32

Age of Information Optimization in Laser-charged UAV-assisted IoT Networks: A Multi-agent Deep Reinforcement Learning Method
Geng Sun, Likun Zhang, Jiahui Li, Jing Wu, Jiacheng Wang, Zemin Sun, Changyuan Zhao, Victor C. M. Leung
arxiv.org/abs/2507.08429

@arXiv_csCE_bot@mastoxiv.page
2025-06-12 07:18:19

Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao, Ayan Acharya, Maria Kissa, Mauricio Flores, Nitesh V. Chawla, Kamalika Das
arxiv.org/abs/2506.09234

@arXiv_csRO_bot@mastoxiv.page
2025-08-13 09:40:12

Towards Safe Imitation Learning via Potential Field-Guided Flow Matching
Haoran Ding, Anqing Duan, Zezhou Sun, Leonel Rozo, No\'emie Jaquier, Dezhen Song, Yoshihiko Nakamura
arxiv.org/abs/2508.08707

@arXiv_csCR_bot@mastoxiv.page
2025-07-16 08:40:11

Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs
HyeYoung Lee, Muhammad Nadeem, Pavel Tsoi
arxiv.org/abs/2507.10622

@arXiv_csLG_bot@mastoxiv.page
2025-08-12 12:06:53

Unsupervised operator learning approach for dissipative equations via Onsager principle
Zhipeng Chang, Zhenye Wen, Xiaofei Zhao
arxiv.org/abs/2508.07440

@arXiv_condmatmtrlsci_bot@mastoxiv.page
2025-06-06 10:00:27

This arxiv.org/abs/2504.08625 has been replaced.
initial toot: mastoxiv.page/@a…

@arXiv_mathNA_bot@mastoxiv.page
2025-07-17 09:31:50

Structured First-Layer Initialization Pre-Training Techniques to Accelerate Training Process Based on $\varepsilon$-Rank
Tao Tang, Jiang Yang, Yuxiang Zhao, Quanhui Zhu
arxiv.org/abs/2507.11962

@tiotasram@kolektiva.social
2025-08-04 15:49:00

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2

@arXiv_eessSP_bot@mastoxiv.page
2025-07-14 08:00:12

AI-Augmented Visible Light Communication: A Framework for Noise Mitigation and Secure Data Transmission
A. A. Nutfaji, Moustafa Hassan Elmallah
arxiv.org/abs/2507.08145

@arXiv_csCL_bot@mastoxiv.page
2025-07-14 09:54:52

ILT-Iterative LoRA Training through Focus-Feedback-Fix for Multilingual Speech Recognition
Qingliang Meng, Hao Wu, Wei Liang, Wei Xu, Qing Zhao
arxiv.org/abs/2507.08477

@arXiv_mathOC_bot@mastoxiv.page
2025-08-13 08:40:32

Decentralized Relaxed Smooth Optimization with Gradient Descent Methods
Zhanhong Jiang, Aditya Balu, Soumik Sarkar
arxiv.org/abs/2508.08413

@arXiv_statML_bot@mastoxiv.page
2025-07-30 09:06:22

From Sublinear to Linear: Fast Convergence in Deep Networks via Locally Polyak-Lojasiewicz Regions
Agnideep Aich, Ashit Baran Aich, Bruce Wade
arxiv.org/abs/2507.21429

@arXiv_quantph_bot@mastoxiv.page
2025-07-02 10:11:10

Behavior of quantum coherence in the ultrastrong and deep strong coupling regimes of light-matter system
Yu-qiang Liu, Qiulin Long, Yi-jia Yang, Zheng Liu, Ting-ting, Ma, Bao-qing, Guo, Xingdong, Zhao, Zunlue, Zhu, Wuming, Liu, Chang-shui Yu
arxiv.org/abs/2507.00638

@arXiv_csCR_bot@mastoxiv.page
2025-07-09 09:15:52

DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective
Shuo Shao, Yiming Li, Mengren Zheng, Zhiyang Hu, Yukun Chen, Boheng Li, Yu He, Junfeng Guo, Tianwei Zhang, Dacheng Tao, Zhan Qin
arxiv.org/abs/2507.05622

@arXiv_astrophGA_bot@mastoxiv.page
2025-07-10 08:48:31

Deep Learning Improves Photometric Redshifts in All Regions of Color Space
Emma R. Moran, Brett H. Andrews, Jeffrey A. Newman, Biprateep Dey
arxiv.org/abs/2507.06299

@arXiv_csRO_bot@mastoxiv.page
2025-06-04 07:51:03

Solving the Pod Repositioning Problem with Deep Reinforced Adaptive Large Neighborhood Search
Lin Xie, Hanyi Li
arxiv.org/abs/2506.02746

@arXiv_eessSP_bot@mastoxiv.page
2025-06-12 08:36:51

Foundation Model-Aided Deep Reinforcement Learning for RIS-Assisted Wireless Communication
Mohammad Ghassemi, Sara Farrag Mobarak, Han Zhang, Ali Afana, Akram Bin Sediq, Melike Erol-Kantarci
arxiv.org/abs/2506.09855

@arXiv_eessIV_bot@mastoxiv.page
2025-06-17 10:06:01

UAV Object Detection and Positioning in a Mining Industrial Metaverse with Custom Geo-Referenced Data
Vasiliki Balaska, Ioannis Tsampikos Papapetros, Katerina Maria Oikonomou, Loukas Bampis, Antonios Gasteratos
arxiv.org/abs/2506.13505

@arXiv_csAI_bot@mastoxiv.page
2025-07-11 07:58:31

Application of LLMs to Multi-Robot Path Planning and Task Allocation
Ashish Kumar
arxiv.org/abs/2507.07302 arxiv.org/…

@arXiv_csCV_bot@mastoxiv.page
2025-07-03 10:29:40

3D Reconstruction and Information Fusion between Dormant and Canopy Seasons in Commercial Orchards Using Deep Learning and Fast GICP
Ranjan Sapkota, Zhichao Meng, Martin Churuvija, Xiaoqiang Du, Zenghong Ma, Manoj Karkee
arxiv.org/abs/2507.01912

@arXiv_csLG_bot@mastoxiv.page
2025-08-12 11:51:13

SGD Convergence under Stepsize Shrinkage in Low-Precision Training
Vincent-Daniel Yun
arxiv.org/abs/2508.07142 arxiv.org/pdf/2508.07142

@arXiv_condmatmtrlsci_bot@mastoxiv.page
2025-08-05 10:40:50

Deep Learning-Driven Prediction of Microstructure Evolution via Latent Space Interpolation
Sachin Gaikwad, Thejas Kasilingam, Owais Ahmad, Rajdip Mukherjee, Somnath Bhowmick
arxiv.org/abs/2508.01822

The Republican shell game on tax cuts
Republicans continue to profess deep concern about the federal debt -- even as their top priority is to pass a bill that will increase it by trillions of dollars.
They have multiple rationalizations for this behavior,
which might work psychologically but do not mathematically.

Thus they claim that the deficit reflects runaway spending rather than insufficient revenue
and that reducing that revenue is therefore nothing to w…

@arXiv_astrophGA_bot@mastoxiv.page
2025-06-16 09:54:19

HeII emitters in the cosmic noon and beyond. Characterising the HeII {\lambda}1640 emission with MUSE and JWST/NIRSpec
R. Gonz'alez-D'iaz, J. M. V\'ilchez, C. Kehrig, I. del Moral-Castro, J. Iglesias-P\'aramo
arxiv.org/abs/2506.11685

@arXiv_csRO_bot@mastoxiv.page
2025-06-11 08:13:25

Attention-based Learning for 3D Informative Path Planning
Rui Zhao, Xingjian Zhang, Yuhong Cao, Yizhuo Wang, Guillaume Sartoretti
arxiv.org/abs/2506.08434

@arXiv_eessIV_bot@mastoxiv.page
2025-06-11 08:06:05

Biologically Inspired Deep Learning Approaches for Fetal Ultrasound Image Classification
Rinat Prochii, Elizaveta Dakhova, Pavel Birulin, Maxim Sharaev
arxiv.org/abs/2506.08623

@arXiv_mathNA_bot@mastoxiv.page
2025-07-10 08:54:41

Sharp uniform approximation for spectral Barron functions by deep neural networks
Yulei Liao, Pingbing Ming, Hao Yu
arxiv.org/abs/2507.06789

@arXiv_csLG_bot@mastoxiv.page
2025-08-12 12:07:03

Stackelberg Coupling of Online Representation Learning and Reinforcement Learning
Fernando Martinez, Tao Li, Yingdong Lu, Juntao Chen
arxiv.org/abs/2508.07452

@arXiv_csCR_bot@mastoxiv.page
2025-07-04 08:42:11

EIM-TRNG: Obfuscating Deep Neural Network Weights with Encoding-in-Memory True Random Number Generator via RowHammer
Ranyang Zhou, Abeer Matar A. Almalky, Gamana Aragonda, Sabbir Ahmed, Filip Roth Tr{\o}nnes-Christensen, Adnan Siraj Rakin, Shaahin Angizi
arxiv.org/abs/2507.02206

@arXiv_eessSP_bot@mastoxiv.page
2025-08-05 10:57:30

Detecting and measuring respiratory events in horses during exercise with a microphone: deep learning vs. standard signal processing
Jeanne I. M. Parmentier (Utrecht University, University of Twente, Inertia Technology B.V), Rhana M. Aarts (Utrecht University), Elin Hernlund (Swedish University of Agricultural Sciences), Marie Rhodin (Swedish University of Agricultural Sciences), Berend Jan van der Zwaag (University of Twente, Inertia Technology B.V)

@arXiv_csCV_bot@mastoxiv.page
2025-06-09 10:09:32

TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah, Muhammad Haris Khan, Rao Muhammad Anwer, Jorma Laaksonen, Fahad Shahbaz Khan, Salman Khan
arxiv.org/abs/2506.06281

@arXiv_csLG_bot@mastoxiv.page
2025-06-12 10:01:31

EnerBridge-DPO: Energy-Guided Protein Inverse Folding with Markov Bridges and Direct Preference Optimization
Dingyi Rong, Haotian Lu, Wenzhuo Zheng, Fan Zhang, Shuangjia Zheng, Ning Liu
arxiv.org/abs/2506.09496

@arXiv_csRO_bot@mastoxiv.page
2025-06-05 07:22:51

Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration
Chengdong Wu, Sven Kirchner, Nils Purschke, Alois C. Knoll
arxiv.org/abs/2506.04040

@arXiv_csCR_bot@mastoxiv.page
2025-08-13 09:24:02

Image selective encryption analysis using mutual information in CNN based embedding space
Ikram Messadi, Giulia Cervia, Vincent Itier
arxiv.org/abs/2508.08832

@arXiv_csCV_bot@mastoxiv.page
2025-07-08 14:33:31

CTA: Cross-Task Alignment for Better Test Time Training
Samuel Barbeau, Pedram Fekri, David Osowiechi, Ali Bahri, Moslem YazdanpanahMasih Aminbeidokhti, Christian Desrosiers
arxiv.org/abs/2507.05221

@arXiv_eessSP_bot@mastoxiv.page
2025-07-08 11:48:40

Real-Time Graph-based Point Cloud Networks on FPGAs via Stall-Free Deep Pipelining
Marc Neu, Isabel Haide, Timo Justinger, Till R\"adler, Valdrin Dajaku, Torben Ferber, J\"urgen Becker
arxiv.org/abs/2507.05099

@arXiv_eessIV_bot@mastoxiv.page
2025-06-09 08:02:52

Deep histological synthesis from mass spectrometry imaging for multimodal registration
Kimberley M. Bird, Xujiong Ye, Alan M. Race, James M. Brown
arxiv.org/abs/2506.05441

@arXiv_eessIV_bot@mastoxiv.page
2025-07-04 08:31:01

A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal $\left[^{18}\text{F}\right]$FDG PET imaging
Christian Salomonsen, Luigi Tommaso Luppino, Fredrik Aspheim, Kristoffer Wickstr{\o}m, Elisabeth Wetzer, Michael Kampffmeyer, Rodrigo Berzaghi, Rune Sundset, Robert Jenssen, Samuel Kuttner

@arXiv_eessIV_bot@mastoxiv.page
2025-07-29 08:54:11

Hybrid Deep Learning and Handcrafted Feature Fusion for Mammographic Breast Cancer Classification
Maximilian Tschuchnig, Michael Gadermayr, Khalifa Djemal
arxiv.org/abs/2507.19843

@arXiv_eessIV_bot@mastoxiv.page
2025-07-28 08:35:41

Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning
Abdul Hannan, Zahid Mahmood, Rizwan Qureshi, Hazrat Ali
arxiv.org/abs/2507.19199

@arXiv_eessIV_bot@mastoxiv.page
2025-08-07 10:06:54

A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRI
Nicola Casali, Alessandro Brusaferri, Giuseppe Baselli, Stefano Fumagalli, Edoardo Micotti, Gianluigi Forloni, Riaz Hussein, Giovanna Rizzo, Alfonso Mastropietro
arxiv.org/abs/2508.04588