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#preprint

2 posts2 participants0 posts today
Continued thread
Eli Roberson (he/him)

#Academia
#AI
#LLM
#LLMs
#AcademicChatter

I *love* #preprint servers, but biorxiv, medrxiv, and research square all said they don't take more opinion type papers.

Hat tip to @WiseWoman for suggesting #arXiv.

I can *FINALLY* say that it's officially released as a preprint.

Differentiating hype from practical applications of large language models in medicine - a primer for healthcare professionals.

arxiv.org/abs/2507.19567

DOI: 10.48550/arXiv.2507.19567

arXiv logo
arXiv.orgDifferentiating hype from practical applications of large language models in medicine - a primer for healthcare professionalsThe medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.
Bibliolater 📚 📜 🖋

🖥️ 🧠 **Delusions by design? How everyday AIs might be fuelling psychosis (and what can be done about it)**

"_Emerging, and rapidly accumulating, evidence indicates that agential AI may mirror, validate or amplify delusional or grandiose content, particularly in users already vulnerable to psychosis, due in part to the models’ design to maximise engagement and affirmation, although notably it is not clear whether these interactions have resulted or can result in the emergence of de novo psychosis in the absence of pre-existing vulnerability._"

Morrin, H., Nicholls, L., Levin, M., Yiend, J., Iyengar, U., DelGuidice, F., … Pollak, T. (2025, July 11). Delusions by design? How everyday AIs might be fuelling psychosis (and what can be done about it). doi.org/10.31234/osf.io/cmy7n_

#Preprint #AI #ArtificialIntelligence #Technology #Tech #Psychology #Neuroscience #Psychiatry @psychology

Replied in thread
VelteropⓐⓊ🪂🇪🇺🇳🇱🇬🇧🇩🇪

@benlockwood
High time that Plan U is widely implemented!
Plan U journals.plos.org/plosbiology/
(Plan U: Universal access to scientific and medical research via funder #preprint mandates)

journals.plos.orgPlan U: Universal access to scientific and medical research via funder preprint mandatesPreprint servers are a low-cost mechanism for providing free access to research findings, and can also significantly accelerate research itself by making results available immediately. This Perspective article proposes that funding agencies should mandate preprint posting to ensure universal free access to the world’s scientific output, as well as stimulate new peer review and research evaluation initiatives.
Bibliolater 📚 📜 🖋

🖥️ **How Overconfidence in Initial Choices and Underconfidence Under Criticism Modulate Change of Mind in Large Language Models**

🔗 doi.org/10.48550/arXiv.2507.03.

arXiv.orgHow Overconfidence in Initial Choices and Underconfidence Under Criticism Modulate Change of Mind in Large Language ModelsLarge language models (LLMs) exhibit strikingly conflicting behaviors: they can appear steadfastly overconfident in their initial answers whilst at the same time being prone to excessive doubt when challenged. To investigate this apparent paradox, we developed a novel experimental paradigm, exploiting the unique ability to obtain confidence estimates from LLMs without creating memory of their initial judgments -- something impossible in human participants. We show that LLMs -- Gemma 3, GPT4o and o1-preview -- exhibit a pronounced choice-supportive bias that reinforces and boosts their estimate of confidence in their answer, resulting in a marked resistance to change their mind. We further demonstrate that LLMs markedly overweight inconsistent compared to consistent advice, in a fashion that deviates qualitatively from normative Bayesian updating. Finally, we demonstrate that these two mechanisms -- a drive to maintain consistency with prior commitments and hypersensitivity to contradictory feedback -- parsimoniously capture LLM behavior in a different domain. Together, these findings furnish a mechanistic account of LLM confidence that explains both their stubbornness and excessive sensitivity to criticism.
#Preprint#ComputerScience#MachineLearning
El Duvelle

I will never understand why the authors of a manuscript that they post on a preprint server spontaneously decide that it will be better for whoever reads their manuscript to have not only all the figures at the end, but also separated from the legends?

WHY 😭

(Same question for papers sent to review btw. Most journals allow for the format of your choice for the first submission. WHY not make it a nice, easily readable format??)

#ScientificJournals#ResearchPapers#Academia
Sascha Wolfer

New #preprint by Alex Koplenig and me:
"Statistical errors undermine claims about the evolution of polysynthetic languages". (doi.org/10.31219/osf.io/g72hw_)

This is a comment on Bromham et al. (2025): "Macroevolutionary analysis of polysynthesis..." published in #PNAS (doi.org/10.1073/pnas.250448312).

In a nutshell: Statistical models that fit the data better call almost all reported results into question.
- Most structure is due to phylogenetic and geographic clustering.
- Neither spatial nor phylogenetic isolation is significant.
- L1 population only partially significant, but effect direction is reversed.

doi.orgOSF
#Linguistics
Amelia Cervera 🧬

mRNA 3′UTRs chaperone intrinsically disordered regions to control protein activity.

#RNA #mRNA #3UTR #IntrinsicallyDisorderedRegions #IDRs #IntrinsicallyDisorderedProteins #Chaperones #Preprint

biorxiv.org/content/10.1101/20

bioRxiv · mRNA 3′UTRs chaperone intrinsically disordered regions to control protein activityMore than 2,700 human mRNA 3′UTRs have hundreds of highly conserved (HC) nucleotides, but their biological roles are unclear. Here, we show that mRNAs with HC 3′UTRs mostly encode proteins with long intrinsically disordered regions (IDRs), including MYC, UTX, and JMJD3. These proteins are only fully active when translated from mRNA templates that include their 3′UTRs, raising the possibility of functional interactions between 3′UTRs and IDRs. Rather than affecting protein abundance or localization, we find that HC 3′UTRs control transcriptional or histone demethylase activity through co-translationally determined protein oligomerization states that are kinetically stable. 3′UTR-dependent changes in protein folding require mRNA-IDR interactions, suggesting that mRNAs act as IDR chaperones. These mRNAs are multivalent, a biophysical RNA feature that enables their translation in network-like condensates, which provide favorable folding environments for proteins with long IDRs. These data indicate that the coding sequence is insufficient for the biogenesis of biologically active conformations of IDR-containing proteins and that RNA can catalyze protein folding. ### Competing Interest Statement The authors have declared no competing interest. Pershing Square Foundation, https://ror.org/04tce9s05 G. Harold & Leila Y. Mathers Foundation National Institutes of Health, DP1GM123454, R35GM144046 Memorial Sloan Kettering Cancer Center, https://ror.org/02yrq0923, P30 CA008748
ScienceOpen

'On the Comparative Analysis of Methods for Solving the Blasius Boundary Layer Problem of #CompressibleFlow' - a UnisaRxiv #Preprint #Research article open for review on #ScienceOpen:

🔗 scienceopen.com/hosted-documen

ScienceOpenOn the Comparative Analysis of Methods for Solving the Blasius Boundary Layer Problem of Compressible Flow<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d262782e115">In this article, we present a comparative analysis of two numerical methods for solving the problem of the Blasius boundary layer. To enrich this analysis, a detailed comparison of the localised radial basis function methods using both polyharmonic splines and Gaussian functions with shooting methods provides insight into the strengths and weaknesses of these approaches. The Blasius problem is crucial to fluid mechanics as it models the flow over flat surfaces, which is relevant in various engineering applications such as aerodynamics. We applied both methods to determine the velocity and temperature profiles of a fluid over a flat plate, and compared the results for accuracy and computational efficiency. </p>
*
Bibliolater 📚 📜 🖋

💻 **Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task**

"_Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning._"

Kosmyna, N. et al. (2025) Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task. arxiv.org/abs/2506.08872.

#Preprint #AI #ArtificialIntelligence #LLM #LLMS #ComputerScience #Technology #Tech #Research #Learning #Education @ai

arXiv logo
arXiv.orgYour Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing TaskThis study explores the neural and behavioral consequences of LLM-assisted essay writing. Participants were divided into three groups: LLM, Search Engine, and Brain-only (no tools). Each completed three sessions under the same condition. In a fourth session, LLM users were reassigned to Brain-only group (LLM-to-Brain), and Brain-only users were reassigned to LLM condition (Brain-to-LLM). A total of 54 participants took part in Sessions 1-3, with 18 completing session 4. We used electroencephalography (EEG) to assess cognitive load during essay writing, and analyzed essays using NLP, as well as scoring essays with the help from human teachers and an AI judge. Across groups, NERs, n-gram patterns, and topic ontology showed within-group homogeneity. EEG revealed significant differences in brain connectivity: Brain-only participants exhibited the strongest, most distributed networks; Search Engine users showed moderate engagement; and LLM users displayed the weakest connectivity. Cognitive activity scaled down in relation to external tool use. In session 4, LLM-to-Brain participants showed reduced alpha and beta connectivity, indicating under-engagement. Brain-to-LLM users exhibited higher memory recall and activation of occipito-parietal and prefrontal areas, similar to Search Engine users. Self-reported ownership of essays was the lowest in the LLM group and the highest in the Brain-only group. LLM users also struggled to accurately quote their own work. While LLMs offer immediate convenience, our findings highlight potential cognitive costs. Over four months, LLM users consistently underperformed at neural, linguistic, and behavioral levels. These results raise concerns about the long-term educational implications of LLM reliance and underscore the need for deeper inquiry into AI's role in learning.
Bibliolater 📚 📜 🖋

💻 **Dark LLMs: The Growing Threat of Unaligned AI Models**

"_In our research, we uncovered a universal jailbreak attack that effectively compromises multiple state-of-the-art models, enabling them to answer almost any question and produce harmful outputs upon request._"

Fire, M. et al. (2025) Dark LLMs: The growing threat of unaligned AI models. arxiv.org/abs/2505.10066.

#AI #ArtificialIntelligence #LLMS #DarkLLMS #Technology #Tech #Preprint #Research #ComputerScience @ai

arXiv.orgDark LLMs: The Growing Threat of Unaligned AI ModelsLarge Language Models (LLMs) rapidly reshape modern life, advancing fields from healthcare to education and beyond. However, alongside their remarkable capabilities lies a significant threat: the susceptibility of these models to jailbreaking. The fundamental vulnerability of LLMs to jailbreak attacks stems from the very data they learn from. As long as this training data includes unfiltered, problematic, or 'dark' content, the models can inherently learn undesirable patterns or weaknesses that allow users to circumvent their intended safety controls. Our research identifies the growing threat posed by dark LLMs models deliberately designed without ethical guardrails or modified through jailbreak techniques. In our research, we uncovered a universal jailbreak attack that effectively compromises multiple state-of-the-art models, enabling them to answer almost any question and produce harmful outputs upon request. The main idea of our attack was published online over seven months ago. However, many of the tested LLMs were still vulnerable to this attack. Despite our responsible disclosure efforts, responses from major LLM providers were often inadequate, highlighting a concerning gap in industry practices regarding AI safety. As model training becomes more accessible and cheaper, and as open-source LLMs proliferate, the risk of widespread misuse escalates. Without decisive intervention, LLMs may continue democratizing access to dangerous knowledge, posing greater risks than anticipated.
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