Tootfinder

«I think it says that we are in a scary world where it is hard to tell if this is true or not. Like 10 years ago this wouldn’t even be a possibility but now it is very plausible. I think it shows a growing crack down on free speech and our rights. Bigger picture to me is that we are going to be unjustly held accountable for things that are much within our right to do/possess.»
'My Bad:' Babyface Vance Meme Creator On Norwegian Tourist's Detainment
https://www.404media.co/vance-babyface-meme-mads-mikkelsen-norway-tourist/

Shape2Animal: Creative Animal Generation from Natural Silhouettes
Quoc-Duy Tran, Anh-Tuan Vo, Dinh-Khoi Vo, Tam V. Nguyen, Minh-Triet Tran, Trung-Nghia Le
https://arxiv.org/abs/2506.20616

Shape2Animal: Creative Animal Generation from Natural Silhouettes
Humans possess a unique ability to perceive meaningful patterns in ambiguous stimuli, a cognitive phenomenon known as pareidolia. This paper introduces Shape2Animal framework to mimics this imaginative capacity by reinterpreting natural object silhouettes, such as clouds, stones, or flames, as plausible animal forms. Our automated framework first performs open-vocabulary segmentation to extract object silhouette and interprets semantically appropriate animal concepts using vision-language model…

Cosmological abundance of primordial black holes in mixed dark matter scenarios incorporating Kaluza-Klein dark matter
Yupeng Yang, Qianyong Li, Jiali Hao, Xiujuan Li
https://arxiv.org/abs/2506.20391

Cosmological abundance of primordial black holes in mixed dark matter scenarios incorporating Kaluza-Klein dark matter
The lightest Kaluza-Klein (KK) dark matter particles and primordial black holes (PBHs) emerge as plausible candidates for dark matter. In scenarios where dark matter is a mix of KK particles and PBHs, PBHs can attract surrounding KK dark matter particles post-formation, leading to the creation of ultracompact dark matter halos (UCMHs). The distribution of KK dark matter particles within UCMHs tends to be steeper than in classical dark matter halo structures, such as the Navarro-Frenk-White mode…

SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer
Diego Di Carlo (RIKEN AIP), Mathieu Fontaine (LTCI, IP Paris), Aditya Arie Nugraha (RIKEN AIP), Yoshiaki Bando (RIKEN AIP), Kazuyoshi Yoshii
https://arxiv.org/abs/2506.18954

SHAMaNS: Sound Localization with Hybrid Alpha-Stable Spatial Measure and Neural Steerer
This paper describes a sound source localization (SSL) technique that combines an $α$-stable model for the observed signal with a neural network-based approach for modeling steering vectors. Specifically, a physics-informed neural network, referred to as Neural Steerer, is used to interpolate measured steering vectors (SVs) on a fixed microphone array. This allows for a more robust estimation of the so-called $α$-stable spatial measure, which represents the most plausible direction of arrival…

If you see posts about how Netanyahu is playing Trump — which is plausible, Trump’s a vain nincompoop and people play him all the time — please •mind the undertone•.
Are they really taking about Netanyahu? Or is “Netanyahu” standing in for a larger group?
Is the focus on Trump being a gullible fool? Or is the focus on insinuating that some shadowy group secretly controls world politics?
Take a moment to digest this from @…, notice and recognize the pattern, and be aware as you scroll.
https://babka.social/@dukepaaron/114725457675254104

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
Zhiting Mei, Christina Zhang, Tenny Yin, Justin Lidard, Ola Shorinwa, Anirudha Majumdar
https://arxiv.org/abs/2506.18183

Reasoning about Uncertainty: Do Reasoning Models Know When They Don't Know?
Reasoning language models have set state-of-the-art (SOTA) records on many challenging benchmarks, enabled by multi-step reasoning induced using reinforcement learning. However, like previous language models, reasoning models are prone to generating confident, plausible responses that are incorrect (hallucinations). Knowing when and how much to trust these models is critical to the safe deployment of reasoning models in real-world applications. To this end, we explore uncertainty quantification…

Are there any email worms doing the rounds yet that use LLMs to individually craft super-plausible email body text to get people to click on stuff?

The unreasonable likelihood of being: origin of life, terraforming, and AI
Robert G. Endres
https://arxiv.org/abs/2507.18545 https://arxiv.org/pdf/2507.185…

The unreasonable likelihood of being: origin of life, terraforming, and AI
The origin of life on Earth via the spontaneous emergence of a protocell prior to Darwinian evolution remains a fundamental open question in physics and chemistry. Here, we develop a conceptual framework based on information theory and algorithmic complexity. Using estimates grounded in modern computational models, we evaluate the difficulty of assembling structured biological information under plausible prebiotic conditions. Our results highlight the formidable entropic and informational barri…

Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
Jianglong Ye, Keyi Wang, Chengjing Yuan, Ruihan Yang, Yiquan Li, Jiyue Zhu, Yuzhe Qin, Xueyan Zou, Xiaolong Wang
https://arxiv.org/abs/2506.17198

Dex1B: Learning with 1B Demonstrations for Dexterous Manipulation
Generating large-scale demonstrations for dexterous hand manipulation remains challenging, and several approaches have been proposed in recent years to address this. Among them, generative models have emerged as a promising paradigm, enabling the efficient creation of diverse and physically plausible demonstrations. In this paper, we introduce Dex1B, a large-scale, diverse, and high-quality demonstration dataset produced with generative models. The dataset contains one billion demonstrations fo…

"[Chain of reasoning] reports are untrustworthy on principle: they are plausible explanations for plausible responses, and since the inferences involved are more complex, they burn more compute and carbon per query as well as introducing more mistakes"
This is a particularly offensive point about #LLMs: we actually do have a class of systems, inference engines, which do reason and can…

Artificial intelligence is the opposite of education
Or: what if there is no middle ground?

"Pumping Water Onto Sea Ice Could Halt its Loss in the Arctic—Nonprofit Proves ‘Naive’ Idea Plausible"
#Arctic #Climate #ClimateChange

Pumping Water Onto Sea Ice Could Halt Ice Loss in the Arctic—Nonprofit Proves 'Naive' Idea Plausible
The craziness of the whole endeavor is only matched by the urgency with which they believe something must be done to address sea ice loss.

The Vela pulsar and its pulsar wind nebula Vela-X using 13 years of Fermi-LAT Observations
Alexander Lange, J. Eagle, O. Kargaltsev, Lucien Kuiper, Jeremy Hare
https://arxiv.org/abs/2506.16687

The Vela pulsar and its pulsar wind nebula Vela-X using 13 years of Fermi-LAT Observations
We present results of more than 13 years of Fermi-LAT data analysis for the Vela pulsar from 60 MeV to 100 GeV and its pulsar wind nebula (PWN), Vela-X, for E > 1 GeV in the off-pulse phases. We find the Vela-X PWN can be best characterized using two extended components: a large radial Gaussian accompanied by an off-set, compact radial disk, both with a similar spectral index, Γ\sim 2.3. The common spectral properties support a common PWN origin, but a supernova remnant component is plausible …

Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks
Hope Schroeder, Deb Roy, Jad Kabbara
https://arxiv.org/abs/2507.15821

Just Put a Human in the Loop? Investigating LLM-Assisted Annotation for Subjective Tasks
LLM use in annotation is becoming widespread, and given LLMs' overall promising performance and speed, simply "reviewing" LLM annotations in interpretive tasks can be tempting. In subjective annotation tasks with multiple plausible answers, reviewing LLM outputs can change the label distribution, impacting both the evaluation of LLM performance, and analysis using these labels in a social science task downstream. We conducted a pre-registered experiment with 410 unique annotators and over 7,000…

Belief Alignment vs Opinion Leadership: Understanding Cross-linguistic Digital Activism in K-pop and BLM Communities
Yuheun Kim, Joshua Introne
https://arxiv.org/abs/2507.16046

Belief Alignment vs Opinion Leadership: Understanding Cross-linguistic Digital Activism in K-pop and BLM Communities
The internet has transformed activism, giving rise to more organic, diverse, and dynamic social movements that transcend geo-political boundaries. Despite extensive research on the role of social media and the internet in cross-cultural activism, the fundamental motivations driving these global movements remain poorly understood. This study examines two plausible explanations for cross-cultural activism: first, that it is driven by influential online opinion leaders, and second, that it results…

Dream, Lift, Animate: From Single Images to Animatable Gaussian Avatars
Marcel C. B\"uhler, Ye Yuan, Xueting Li, Yangyi Huang, Koki Nagano, Umar Iqbal
https://arxiv.org/abs/2507.15979

Dream, Lift, Animate: From Single Images to Animatable Gaussian Avatars
We introduce Dream, Lift, Animate (DLA), a novel framework that reconstructs animatable 3D human avatars from a single image. This is achieved by leveraging multi-view generation, 3D Gaussian lifting, and pose-aware UV-space mapping of 3D Gaussians. Given an image, we first dream plausible multi-views using a video diffusion model, capturing rich geometric and appearance details. These views are then lifted into unstructured 3D Gaussians. To enable animation, we propose a transformer-based enco…

Analyzing Internal Activity and Robustness of SNNs Across Neuron Parameter Space
Szymon Mazurek, Jakub Caputa, Maciej Wielgosz
https://arxiv.org/abs/2507.14757

Analyzing Internal Activity and Robustness of SNNs Across Neuron Parameter Space
Spiking Neural Networks (SNNs) offer energy-efficient and biologically plausible alternatives to traditional artificial neural networks, but their performance depends critically on the tuning of neuron model parameters. In this work, we identify and characterize an operational space - a constrained region in the neuron hyperparameter domain (specifically membrane time constant tau and voltage threshold vth) - within which the network exhibits meaningful activity and functional behavior. Operati…

On Design of Representative Distributionally Robust Formulations for Evaluation of Tail Risk Measures
Anand Deo
https://arxiv.org/abs/2506.16230 https://…

On Design of Representative Distributionally Robust Formulations for Evaluation of Tail Risk Measures
Conditional Value-at-Risk (CVaR) is a risk measure widely used to quantify the impact of extreme losses. Owing to the lack of representative samples CVaR is sensitive to the tails of the underlying distribution. In order to combat this sensitivity, Distributionally Robust Optimization (DRO), which evaluates the worst-case CVaR measure over a set of plausible data distributions is often deployed. Unfortunately, an improper choice of the DRO formulation can lead to a severe underestimation of tai…

OK, so FFOTUS is making noises that he knew about Israel's plans to attack Iran, or that FFOTUS actually approved and supported that attack.
Whether that is true of not, FFOTUS has planted a very dangerous seed.
Now, there is plausible reason for Iran and Islamic radicals to conclude that there is an actual, but undeclared, war by the US on Iran.
Iran is not a stupid country, it knows that it can not directly attack the US, especially with anything nuclear.
But Ir…

This https://arxiv.org/abs/2404.10304 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

LLM-Powered Test Case Generation for Detecting Bugs in Plausible Programs
Detecting tricky bugs in plausible programs, those that pass existing test suites yet still contain bugs, remains a significant challenge in software testing. To address this problem, we propose TrickCatcher, an LLM-powered approach to generating test cases for uncovering bugs in plausible programs. TrickCatcher operates in three stages: First, it uses an LLM to generate program variants based on the program under test (PUT) and its specification. Second, it employs an LLM to construct an input…

Using LLMs for Security Advisory Investigations: How Far Are We?
Bayu Fedra Abdullah, Yusuf Sulistyo Nugroho, Brittany Reid, Raula Gaikovina Kula, Kazumasa Shimari, Kenichi Matsumoto
https://arxiv.org/abs/2506.13161

Using LLMs for Security Advisory Investigations: How Far Are We?
Large Language Models (LLMs) are increasingly used in software security, but their trustworthiness in generating accurate vulnerability advisories remains uncertain. This study investigates the ability of ChatGPT to (1) generate plausible security advisories from CVE-IDs, (2) differentiate real from fake CVE-IDs, and (3) extract CVE-IDs from advisory descriptions. Using a curated dataset of 100 real and 100 fake CVE-IDs, we manually analyzed the credibility and consistency of the model's output…

I'm pretty floored by journalists on NYT Hard Fork asking @… how she could research a book without ChatGPT or generative AI.
The evidence is quite clear that these tools add plausible-sounding falsehoods to text and journalists have a special responsibility to not contaminate the stream of verified facts.

Precipitation plausible: magnetized thermal instability in the intracluster medium
Benjamin D. Wibking, G. Mark Voit, Brian W. O'Shea
https://arxiv.org/abs/2506.10277

Precipitation plausible: magnetized thermal instability in the intracluster medium
Observations of galaxy-cluster cores reveal that AGN feedback is strongly associated with both a short central cooling time ($t_{\rm c} \lesssim 10^9 \, {\rm yr}$) and accumulations of cold gas ($\lesssim 10^4 \, {\rm K}$). Also, the central ratio of cooling time to freefall time is rarely observed to drop below $t_{\rm c}/t_{\rm ff} \approx 10$, and large accumulations of cold gas are rarely observed in environments with $t_{\rm c} / t_{\rm ff} \gtrsim 30$. Here we show that the critical range…

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory
Jiancong Xiao, Zhekun Shi, Kaizhao Liu, Qi Long, Weijie J. Su
https://arxiv.org/abs/2506.12350

Theoretical Tensions in RLHF: Reconciling Empirical Success with Inconsistencies in Social Choice Theory
Despite its empirical success, Reinforcement Learning from Human Feedback (RLHF) has been shown to violate almost all the fundamental axioms in social choice theory -- such as majority consistency, pairwise majority consistency, and Condorcet consistency. This raises a foundational question: why does RLHF perform so well in practice if it fails these seemingly essential properties? In this paper, we resolve this paradox by showing that under mild and empirically plausible assumptions on the pre…

Plausible Counterfactual Explanations of Recommendations
Jakub \v{C}ern\'y, Ji\v{r}\'i N\v{e}me\v{c}ek, Ivan Dovica, Jakub Mare\v{c}ek
https://arxiv.org/abs/2507.07919 https://arxiv.org/pdf/2507.07919 https://arxiv.org/html/2507.07919
arXiv:2507.07919v1 Announce Type: new
Abstract: Explanations play a variety of roles in various recommender systems, from a legally mandated afterthought, through an integral element of user experience, to a key to persuasiveness. A natural and useful form of an explanation is the Counterfactual Explanation (CE). We present a method for generating highly plausible CEs in recommender systems and evaluate it both numerically and with a user study.
toXiv_bot_toot

HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
Roey Ron, Guy Tevet, Haim Sawdayee, Amit H. Bermano
https://arxiv.org/abs/2506.15625

HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
We present HOIDiNi, a text-driven diffusion framework for synthesizing realistic and plausible human-object interaction (HOI). HOI generation is extremely challenging since it induces strict contact accuracies alongside a diverse motion manifold. While current literature trades off between realism and physical correctness, HOIDiNi optimizes directly in the noise space of a pretrained diffusion model using Diffusion Noise Optimization (DNO), achieving both. This is made feasible thanks to our ob…

Partial identification via conditional linear programs: estimation and policy learning
Eli Ben-Michael
https://arxiv.org/abs/2506.12215 https://

Partial identification via conditional linear programs: estimation and policy learning
Many important quantities of interest are only partially identified from observable data: the data can limit them to a set of plausible values, but not uniquely determine them. This paper develops a unified framework for covariate-assisted estimation, inference, and decision making in partial identification problems where the parameter of interest satisfies a series of linear constraints, conditional on covariates. In such settings, bounds on the parameter can be written as expectations of solu…

Meta offering $100M signing bonuses may be an exaggeration by Altman, but $100M overall compensation packages are plausible given Meta's need to catch up in AI (M.G. Siegler/Spyglass)
https://spyglass.org/ai-signing-bonuses/

$100M AI Signing Bonuses
Exaggeration, conflation, a game of Telephone, directional accuracy, brinksmanship, or all of the above?

Excuse-of-a-dog now doing plausible otter impressions. I give up.

Chris Dillow on "Not Debating Immigration"
"Debates don't work, at least not as they should. They don't favour the truth, but plausible liars and for those who can best appeal to prejudice and cognitive bias."
To defeat the far right, people's living standards must improve Keir, Rachel & Liz.

Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example
Yuhan Helena Liu, Guangyu Robert Yang, Christopher J. Cueva
https://arxiv.org/abs/2506.06904

Can Biologically Plausible Temporal Credit Assignment Rules Match BPTT for Neural Similarity? E-prop as an Example
Understanding how the brain learns may be informed by studying biologically plausible learning rules. These rules, often approximating gradient descent learning to respect biological constraints such as locality, must meet two critical criteria to be considered an appropriate brain model: (1) good neuroscience task performance and (2) alignment with neural recordings. While extensive research has assessed the first criterion, the second remains underexamined. Employing methods such as Procruste…

Is it plausible that someone who did a psychology degree in the UK in the early 1980s but went on to work in a largely unrelated field has never heard of The Trolley Problem?
I ask only for a trivial reason and to satisfy my curiosity. Backstory is that a UK talk show host took a call in the last week where the caller said, "you know The Trolley Problem?" and he said no, but I am sure he did that only so the caller would explain in their own words for the purposes of the show…

Functional Emotion Modeling in Biomimetic Reinforcement Learning
Louis Wang
https://arxiv.org/abs/2507.11027 https://arxiv.org/pdf/25…

Functional Emotion Modeling in Biomimetic Reinforcement Learning
We explore a functionalist approach to emotion by employing an ansatz -- an initial set of assumptions -- that a hypothetical concept generation model incorporates unproven but biologically plausible traits. From these traits, we mathematically construct a theoretical reinforcement learning framework grounded in functionalist principles and examine how the resulting utility function aligns with emotional valence in biological systems. Our focus is on structuring the functionalist perspective th…

Meine Science-Fiction und Fantasy-Besprechungen für den Mai sind online. Mit For All Mankind, Nils Westerboers neuem Roman Lyneham (den ich sehr empfehle) und der Hidden-Seas-Reihe von A.M. Dellamonica.
https://blog.till-westermayer.de/index.ph…

Science Fiction und Fantasy im Mai 2025
For All Mankind (Apple TV) funktioniert für mich einfach. Ich habe gerade das Finale der Staffel 4 gesehen (das in einer alternativen Realität in einer Marskolonie im Jahr 2012 endet), und bin begeistert davon, weil die Serie es schafft, Politik, halbwegs plausible Wissenschaft bzw. halbwegs plausib

CBTOPE2: An improved method for predicting of conformational B-cell epitopes in an antigen from its primary sequence
Anupma Pandey, Megha, Nishant Kumar, Ruchir Sahni, Gajendra P. S. Raghava
https://arxiv.org/abs/2506.13395

CBTOPE2: An improved method for predicting of conformational B-cell epitopes in an antigen from its primary sequence
In 2009, our group pioneered a novel method CBTOPE for predicting conformational B-cell epitopes in a protein from its amino acid sequence, which received extensive citations from the scientific community. In a recent study, Cia et al. (2023) evaluated the performance of conformational B-cell epitope prediction methods on a well-curated dataset, revealing that most approaches, including CBTOPE, exhibited poor performance. One plausible cause of this diminished performance is that available meth…

Plausible GMM: A Quasi-Bayesian Approach
Victor Chernozhukov, Christian B. Hansen, Lingwei Kong, Weining Wang
https://arxiv.org/abs/2507.00555 https://

Plausible GMM: A Quasi-Bayesian Approach
Structural estimation in economics often makes use of models formulated in terms of moment conditions. While these moment conditions are generally well-motivated, it is often unknown whether the moment restrictions hold exactly. We consider a framework where researchers model their belief about the potential degree of misspecification via a prior distribution and adopt a quasi-Bayesian approach for performing inference on structural parameters. We provide quasi-posterior concentration results, …

End-to-end learning of safe stimulation parameters for cortical neuroprosthetic vision https://www.biorxiv.org/content/10.1101/2025.01.23.634543v1.full "typical range of 10-20 μA", so special measures must be taken to prevent that a 10,000-electrode…

#LB Esse texto sobre LLMs do @… é bem grande, mas excelente. Vale a leitura, apesar do tamanho.
…

This past night I had a strange nightmare. For some reason unknown to me I found myself visiting the US.
Minutes after passing through all the airport checks I was detained for terrorist conspiracy... because the other day I wrote about zip bombs as a defense against adversarial thief bots.
The crazy thing about this is that I find it plausible to happen in real life.

With FFOTUS now actively using the military against US citizens and legal residents we have entered a new phase.
It is pretty clear that "protesting" via symbolic acts and gatherings are largely ineffective and, indeed, often serve to create excuses - poor excuses, but plausible in the maga-klan kommunity - for more repression.
In other words we are moving past protest to a more confrontational time.
And for which I remind people of song found appropriate around t…

Using LLMs for Security Advisory Investigations: How Far Are We?
Bayu Fedra Abdullah, Yusuf Sulistyo Nugroho, Brittany Reid, Raula Gaikovina Kula, Kazumasa Shimari, Kenichi Matsumoto
https://arxiv.org/abs/2506.13161

Using LLMs for Security Advisory Investigations: How Far Are We?
Large Language Models (LLMs) are increasingly used in software security, but their trustworthiness in generating accurate vulnerability advisories remains uncertain. This study investigates the ability of ChatGPT to (1) generate plausible security advisories from CVE-IDs, (2) differentiate real from fake CVE-IDs, and (3) extract CVE-IDs from advisory descriptions. Using a curated dataset of 100 real and 100 fake CVE-IDs, we manually analyzed the credibility and consistency of the model's output…

Lower dimensional black holes in nonlinear electrodynamics: causal structure and scalar perturbations
Rodrigo Dal Bosco Fontana
https://arxiv.org/abs/2506.09303

Lower dimensional black holes in nonlinear electrodynamics: causal structure and scalar perturbations
We study the charged black hole solutions of a 2+1 nonlinear electrodynamical theory with cosmological constant. Considered as a one-parameter group of theories (the exponent of the squared Maxwell tensor) the causal structure of all possible black holes is scrutinized. We analyze the singularity character that each theory delivers together with their horizons and the plausible limitations in the black hole charges. The investigation demonstrates a rich structure of three different groups of th…

Pushing the Limits of Extreme Weather: Constructing Extreme Heatwave Storylines with Differentiable Climate Models
Tim Whittaker, Alejandro Di Luca
https://arxiv.org/abs/2506.10660

Pushing the Limits of Extreme Weather: Constructing Extreme Heatwave Storylines with Differentiable Climate Models
Understanding the plausible upper bounds of extreme weather events is essential for risk assessment in a warming climate. Existing methods, based on large ensembles of physics-based models, are often computationally expensive or lack the fidelity needed to simulate rare, high-impact extremes. Here, we present a novel framework that leverages a differentiable hybrid climate model, NeuralGCM, to optimize initial conditions and generate physically consistent worst-case heatwave trajectories. Appli…

Invariant non-equilibrium dynamics of transcriptional regulation optimize information flow
Benjamin Zoller, Alexis B\'enichou, Thomas Gregor, Ga\v{s}per Tka\v{c}ik
https://arxiv.org/abs/2507.12395

Invariant non-equilibrium dynamics of transcriptional regulation optimize information flow
Eukaryotic gene regulation is based on stochastic yet controlled promoter switching, during which genes transition between transcriptionally active and inactive states. Despite the molecular complexity of this process, recent studies reveal a surprising invariance of the "switching correlation time" ($T_C$), which characterizes promoter activity fluctuations, across gene expression levels in diverse genes and organisms. A biophysically plausible explanation for this invariance remains missing. …

A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models
Shuliang Liu, Hongyi Liu, Aiwei Liu, Bingchen Duan, Qi Zheng, Yibo Yan, He Geng, Peijie Jiang, Jia Liu, Xuming Hu
https://arxiv.org/abs/2507.05288

A Survey on Proactive Defense Strategies Against Misinformation in Large Language Models
The widespread deployment of large language models (LLMs) across critical domains has amplified the societal risks posed by algorithmically generated misinformation. Unlike traditional false content, LLM-generated misinformation can be self-reinforcing, highly plausible, and capable of rapid propagation across multiple languages, which traditional detection methods fail to mitigate effectively. This paper introduces a proactive defense paradigm, shifting from passive post hoc detection to antic…

Revisiting Active Learning under (Human) Label Variation
Cornelia Gruber, Helen Alber, Bernd Bischl, G\"oran Kauermann, Barbara Plank, Matthias A{\ss}enmacher
https://arxiv.org/abs/2507.02593

Revisiting Active Learning under (Human) Label Variation
Access to high-quality labeled data remains a limiting factor in applied supervised learning. While label variation (LV), i.e., differing labels for the same instance, is common, especially in natural language processing, annotation frameworks often still rest on the assumption of a single ground truth. This overlooks human label variation (HLV), the occurrence of plausible differences in annotations, as an informative signal. Similarly, active learning (AL), a popular approach to optimizing th…

Auditory-Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients
Saitarun Nadipineni, Chapa Sirithunge, Yue Xie, Fumiya Iida, Thilina Dulantha Lalitharatne
https://arxiv.org/abs/2506.11827

Auditory-Tactile Congruence for Synthesis of Adaptive Pain Expressions in RoboPatients
Misdiagnosis can lead to delayed treatments and harm. Robotic patients offer a controlled way to train and evaluate clinicians in rare, subtle, or complex cases, reducing diagnostic errors. We present RoboPatient, a medical robotic simulator aimed at multimodal pain synthesis based on haptic and auditory feedback during palpation-based training scenarios. The robopatient functions as an adaptive intermediary, capable of synthesizing plausible pain expressions vocal and facial in response to tac…

#P is Sandwiched by One and Two #2DNF Calls: Is Subtraction Stronger Than We Thought?
Max Bannach, Erik D. Demaine, Timothy Gomez, Markus Hecher
https://

#P is Sandwiched by One and Two #2DNF Calls: Is Subtraction Stronger Than We Thought?
The canonical class in the realm of counting complexity is #P. It is well known that the problem of counting the models of a propositional formula in disjunctive normal form (#DNF) is complete for #P under Turing reductions. On the other hand, #DNF $\in$ spanL and spanL $\not\subseteq$ #P unless NL = NP. Hence, the class of functions logspace-reducible to #DNF is a strict subset of #P under plausible complexity-theoretic assumptions. By contrast, we show that two calls to a (restricted) #2DNF o…

From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
Jacob de Nobel, Jeroen J. Briaire, Thomas H. W. Baeck, Anna V. Kononova, Johan H. M. Frijns
https://arxiv.org/abs/2506.03959

From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation
We present NeuroVoc, a flexible model-agnostic vocoder framework that reconstructs acoustic waveforms from simulated neural activity patterns using an inverse Fourier transform. The system applies straightforward signal processing to neurogram representations, time-frequency binned outputs from auditory nerve fiber models. Crucially, the model architecture is modular, allowing for easy substitution or modification of the underlying auditory models. This flexibility eliminates the need for speec…

For example:
- Telling apart photos of cats and dogs is “AI.”
- Making up fake but plausible facts on an arbitrary topic is “AI.”
- Walking is “AI.”
- Doing long multiplication is something we might call “intelligence” in humans, but it is not “AI” because computers have •always• been good at it.
- Winning at checkers •used• to be “AI” because computers didn’t used to be able to do that, but now it’s not “AI” because computers have been good at it for too long.
5/

Wavelet-GS: 3D Gaussian Splatting with Wavelet Decomposition
Beizhen Zhao, Yifan Zhou, Sicheng Yu, Zijian Wang, Hao Wang
https://arxiv.org/abs/2507.12498 h…

Wavelet-GS: 3D Gaussian Splatting with Wavelet Decomposition
3D Gaussian Splatting (3DGS) has revolutionized 3D scene reconstruction, which effectively balances rendering quality, efficiency, and speed. However, existing 3DGS approaches usually generate plausible outputs and face significant challenges in complex scene reconstruction, manifesting as incomplete holistic structural outlines and unclear local lighting effects. To address these issues simultaneously, we propose a novel decoupled optimization framework, which integrates wavelet decomposition …

Position: Simulating Society Requires Simulating Thought
Chance Jiajie Li, Jiayi Wu, Zhenze Mo, Ao Qu, Yuhan Tang, Kaiya Ivy Zhao, Yulu Gan, Jie Fan, Jiangbo Yu, Jinhua Zhao, Paul Liang, Luis Alonso, Kent Larson
https://arxiv.org/abs/2506.06958

Position: Simulating Society Requires Simulating Thought
Simulating society with large language models (LLMs), we argue, requires more than generating plausible behavior -- it demands cognitively grounded reasoning that is structured, revisable, and traceable. LLM-based agents are increasingly used to emulate individual and group behavior -- primarily through prompting and supervised fine-tuning. Yet they often lack internal coherence, causal reasoning, and belief traceability -- making them unreliable for analyzing how people reason, deliberate, or …

Neuromorphic Online Clustering and Its Application to Spike Sorting
James E. Smith
https://arxiv.org/abs/2506.12555 https://arxiv.org…

Neuromorphic Online Clustering and Its Application to Spike Sorting
Active dendrites are the basis for biologically plausible neural networks possessing many desirable features of the biological brain including flexibility, dynamic adaptability, and energy efficiency. A formulation for active dendrites using the notational language of conventional machine learning is put forward as an alternative to a spiking neuron formulation. Based on this formulation, neuromorphic dendrites are developed as basic neural building blocks capable of dynamic online clustering. …

For example:
- Telling apart photos of cats and dogs is “AI.”
- Making up fake but plausible facts on an arbitrary topic is “AI.”
- Walking is “AI.”
- Doing long multiplication is something we might call “intelligence” in humans, but it is not “AI” because computers have •always• been good at it.
- Winning at checkers •used• to be “AI” because computers didn’t used to be able to do that, but now it’s not “AI” because computers have been good at it for too long.
5/

This https://arxiv.org/abs/2408.12228 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

A nonstandard numerical scheme for a novel SECIR integro-differential equation-based model allowing nonexponentially distributed stay times
Ordinary differential equations (ODE) are a popular tool to model the spread of infectious diseases, yet they implicitly assume an exponential distribution to describe the flow from one infection state to another. However, scientific experience yields more plausible distributions where the likelihood of disease progression changes accordingly with the duration spent in a particular state of the disease. Furthermore, transmission dynamics depend heavily on the infectiousness of individuals. The …

A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models
Haley Stone, Jing Du, Hao Xue, Matthew Scotch, David Heslop, Andreas Z\"ufle, Chandini Raina MacIntyre, Flora Salim
https://arxiv.org/abs/2506.09076

A Probabilistic Framework for Imputing Genetic Distances in Spatiotemporal Pathogen Models
Pathogen genome data offers valuable structure for spatial models, but its utility is limited by incomplete sequencing coverage. We propose a probabilistic framework for inferring genetic distances between unsequenced cases and known sequences within defined transmission chains, using time-aware evolutionary distance modeling. The method estimates pairwise divergence from collection dates and observed genetic distances, enabling biologically plausible imputation grounded in observed divergence …

Text-Aware Image Restoration with Diffusion Models
Jaewon Min, Jin Hyeon Kim, Paul Hyunbin Cho, Jaeeun Lee, Jihye Park, Minkyu Park, Sangpil Kim, Hyunhee Park, Seungryong Kim
https://arxiv.org/abs/2506.09993

Text-Aware Image Restoration with Diffusion Models
Image restoration aims to recover degraded images. However, existing diffusion-based restoration methods, despite great success in natural image restoration, often struggle to faithfully reconstruct textual regions in degraded images. Those methods frequently generate plausible but incorrect text-like patterns, a phenomenon we refer to as text-image hallucination. In this paper, we introduce Text-Aware Image Restoration (TAIR), a novel restoration task that requires the simultaneous recovery of…

Search for High Energy Neutrinos from Infrared Flares
Teresa Pernice (for the IceCube Collaboration), Giacomo Sommani (for the IceCube Collaboration)
https://arxiv.org/abs/2507.06934

Search for High Energy Neutrinos from Infrared Flares
IceCube has detected a diffuse flux of high-energy neutrinos, whose origin still remains uncertain. Accreting supermassive black holes (SMBHs) have been proposed as plausible sources of neutrinos. Candidate sources include AT2019dsg, which is likely a stellar tidal disruption event (TDE), and AT2019dfr, an AGN flare. Both present delayed emission in the IR band with respect to the optical signal. This emission can be interpreted as the reprocessing of X-rays to optical light of the flare by dus…

Generative AI and Organizational Structure in the Knowledge Economy
Fasheng Xu, Jing Hou, Wei Chen, Karen Xie
https://arxiv.org/abs/2506.00532 https://

Generative AI and Organizational Structure in the Knowledge Economy
The adoption of GenAI is fundamentally reshaping organizations in the knowledge economy. GenAI can significantly enhance workers' problem-solving abilities and productivity, yet it also presents a major reliability challenge: hallucinations, or errors presented as plausible outputs. This study develops a theoretical model to examine GenAI's impact on organizational structure and the role of human-in-the-loop oversight. Our findings indicate that successful GenAI adoption hinges primarily on mai…

KINDLE: Knowledge-Guided Distillation for Prior-Free Gene Regulatory Network Inference
Rui Peng, Yuchen Lu, Qichen Sun, Yuxing Lu, Chi Zhang, Ziru Liu, Jinzhuo Wang
https://arxiv.org/abs/2505.09664

KINDLE: Knowledge-Guided Distillation for Prior-Free Gene Regulatory Network Inference
Gene regulatory network (GRN) inference serves as a cornerstone for deciphering cellular decision-making processes. Early approaches rely exclusively on gene expression data, thus their predictive power remain fundamentally constrained by the vast combinatorial space of potential gene-gene interactions. Subsequent methods integrate prior knowledge to mitigate this challenge by restricting the solution space to biologically plausible interactions. However, we argue that the effectiveness of thes…

Dating N loud AGNs at high redshift: GS3073 as a snapshot of wCen like evolution of a nuclear star cluster
F. D'Antona, P. Ventura, A. F. Marino, A. P. Milone, E. Vesperini, F. Calura, M. Tailo, R. Valiante, V. Caloi, A. D'Ercole, F. Dell'Agli
https://arxiv.org/abs/2507.06311…

Dating N loud AGNs at high redshift: GS3073 as a snapshot of wCen like evolution of a nuclear star cluster
In this paper we address two major questions raised by recent James Webb Space Telescope observations of the young Universe, namely: 1) what are the seed initial masses, and how rapidly have supermassive black holes (BHs) with masses of 1e6-1e8Msun grown in active galactic nuclei (AGN) hosted by very young galaxies? 2) What are the plausible explanations for the super solar abundances of nitrogen in a fraction of young galaxies at high redshift, both with and without evidence of a massive centr…

Asymptotic convexity of wide and shallow neural networks
Vivek Borkar, Parthe Pandit
https://arxiv.org/abs/2507.01044 https://arxiv.o…

Asymptotic convexity of wide and shallow neural networks
For a simple model of shallow and wide neural networks, we show that the epigraph of its input-output map as a function of the network parameters approximates epigraph of a. convex function in a precise sense. This leads to a plausible explanation of their observed good performance.

Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales
Veronica Centorrino, Francesco Bullo, Giovanni Russo
https://arxiv.org/abs/2506.06134

Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales
A recent breakthrough in biologically-plausible normative frameworks for dimensionality reduction is based upon the similarity matching cost function and the low-rank matrix approximation problem. Despite clear biological interpretation, successful application in several domains, and experimental validation, a formal complete convergence analysis remains elusive. Building on this framework, we consider and analyze a continuous-time neural network, the \emph{similarity matching network}, for pri…

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling
Xin Dong, Shichao Dong, Jin Wang, Jing Huang, Li Zhou, Zenghui Sun, Lihua Jing, Jingsong Lan, Xiaoyong Zhu, Bo Zheng
https://arxiv.org/abs/2507.05056

INTER: Mitigating Hallucination in Large Vision-Language Models by Interaction Guidance Sampling
Hallucinations in large vision-language models (LVLMs) pose significant challenges for real-world applications, as LVLMs may generate responses that appear plausible yet remain inconsistent with the associated visual content. This issue rarely occurs in human cognition. We argue that this discrepancy arises from humans' ability to effectively leverage multimodal interaction information in data samples. Specifically, humans typically first gather multimodal information, analyze the interactions …

A Complete Survey from the $\texttt{CompactObject}$ Perspective on Equation of State Cross-Comparison Using Observational and Nuclear Experimental Constraints
Jo\~ao Cartaxo, Chun Huang, Tuhin Malik, Shashwat Sourav, Wen-Li Yuan, Tianzhe Zhou, Xuezhi Liu, Constan\c{c}a Provid\^encia
https://arxiv.org/abs/2506.03112

A Complete Survey from the $\texttt{CompactObject}$ Perspective on Equation of State Cross-Comparison Using Observational and Nuclear Experimental Constraints
This extensive survey seeks to analyze and contrast different phenomenological methods used to model the nuclear equation of state (EOS) for neutron star matter based on covariant energy density functionals (CEDF). Using two complementary methodologies, we seek to capture a comprehensive picture of the potential behaviors of ultra-dense nucleonic matter and identify the most plausible models based on current observational and experimental constraints. Observational data from radio pulsar timing…

Nearby dwarf galaxies with extreme star formation rates: a window into dwarf-galaxy evolution in the early Universe
S. Kaviraj, B. Bichang'a, I. Lazar, A. E. Watkins, G. Martin, R. A. Jackson
https://arxiv.org/abs/2506.03265

Nearby dwarf galaxies with extreme star formation rates: a window into dwarf-galaxy evolution in the early Universe
We study a sample of nearby (z~0.2) low-luminosity dwarf (10^7 MSun < M* < 10^8 MSun) galaxies which have extreme (0.1 - 3 MSun/yr) star formation rates (SFRs) for this mass regime, making them plausible analogues of dwarfs at z~5.5. We compare the properties of these analogues to control samples of 'normal' dwarfs, which reside on the star formation main sequence (SFMS) at z~0.2 and are matched in their stellar mass and redshift distributions to the analogue population. The analogue and normal…

Complex-Valued Holographic Radiance Fields
Yicheng Zhan, Dong-Ha Shin, Seung-Hwan Baek, Kaan Ak\c{s}it
https://arxiv.org/abs/2506.08350 https://

Complex-Valued Holographic Radiance Fields
Modeling the full properties of light, including both amplitude and phase, in 3D representations is crucial for advancing physically plausible rendering, particularly in holographic displays. To support these features, we propose a novel representation that optimizes 3D scenes without relying on intensity-based intermediaries. We reformulate 3D Gaussian splatting with complex-valued Gaussian primitives, expanding support for rendering with light waves. By leveraging RGBD multi-view images, our …

Poisoning Attacks to Local Differential Privacy for Ranking Estimation
Pei Zhan (School of Cyber Science and Technology, Shandong University, State Key Laboratory of Cryptography and Digital Economy Security, Shandong University, Qingdao, China), Peng Tang (School of Cyber Science and Technology, Shandong University, State Key Laboratory of Cryptography and Digital Economy Security, Shandong University, Qingdao, China), Yangzhuo Li (School of Cyber Science and Technology, Shandong Univ…

Poisoning Attacks to Local Differential Privacy for Ranking Estimation
Local differential privacy (LDP) involves users perturbing their inputs to provide plausible deniability of their data. However, this also makes LDP vulnerable to poisoning attacks. In this paper, we first introduce novel poisoning attacks for ranking estimation. These attacks are intricate, as fake attackers do not merely adjust the frequency of target items. Instead, they leverage a limited number of fake users to precisely modify frequencies, effectively altering item rankings to maximize ga…

Tracing the light: Identification for the optical counterpart candidates of binary black-holes during O3
Lei He, Zhengyan Liu, Rui Niu, Bingzhou Gao, Mingshen Zhou, Purun Zou, Runduo Liang, Wen Zhao, Ning Jiang, Zhen-Yi Cai, Zi-Gao Dai, Ye-Fei Yuan
https://arxiv.org/abs/2507.02475

Tracing the light: Identification for the optical counterpart candidates of binary black-holes during O3
The accretion disks of active galactic nuclei (AGN) are widely considered the ideal environments for binary black hole (BBH) mergers and the only plausible sites for their electromagnetic (EM) counterparts. Graham et al.(2023) identified seven AGN flares that are potentially associated with gravitational-wave (GW) events detected by the LIGO-Virgo-KAGRA (LVK) Collaboration during the third observing run. In this article, utilizing an additional three years of Zwicky Transient Facility (ZTF) pub…

FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models
Yuxuan Wang, Tianwei Cao, Huayu Zhang, Zhongjiang He, Kongming Liang, Zhanyu Ma
https://arxiv.org/abs/2507.02714

FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models
Image generation has achieved remarkable progress with the development of large-scale text-to-image models, especially diffusion-based models. However, generating human images with plausible details, such as faces or hands, remains challenging due to insufficient supervision of local regions during training. To address this issue, we propose FairHuman, a multi-objective fine-tuning approach designed to enhance both global and local generation quality fairly. Specifically, we first construct thr…

This https://arxiv.org/abs/2501.09976 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csNE_…

Dendritic Localized Learning: Toward Biologically Plausible Algorithm
Backpropagation is the foundational algorithm for training neural networks and a key driver of deep learning's success. However, its biological plausibility has been challenged due to three primary limitations: weight symmetry, reliance on global error signals, and the dual-phase nature of training, as highlighted by the existing literature. Although various alternative learning approaches have been proposed to address these issues, most either fail to satisfy all three criteria simultaneously …

This https://arxiv.org/abs/2505.18117 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Multi-Modal Spectral Parametrization Method (MMSPM) for analyzing EEG activity with distinct scaling regimes
Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, which is suspected to be affected in numerous clinical conditions. This phenomenon is characterized via the aperiodic scaling exponent $β$, equal to the spectral slope following log-log transformation of power spectra. Despite recent progress, however, most current methods do not take into consideration the plausible multimodal nature in the power …

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Qingdong He, Xueqin Chen, Chaoyi Wang, Yanjie Pan, Xiaobin Hu, Zhenye Gan, Yabiao Wang, Chengjie Wang, Xiangtai Li, Jiangning Zhang
https://arxiv.org/abs/2507.01908

Reasoning to Edit: Hypothetical Instruction-Based Image Editing with Visual Reasoning
Instruction-based image editing (IIE) has advanced rapidly with the success of diffusion models. However, existing efforts primarily focus on simple and explicit instructions to execute editing operations such as adding, deleting, moving, or swapping objects. They struggle to handle more complex implicit hypothetical instructions that require deeper reasoning to infer plausible visual changes and user intent. Additionally, current datasets provide limited support for training and evaluating rea…

Facial Appearance Capture at Home with Patch-Level Reflectance Prior
Yuxuan Han, Junfeng Lyu, Kuan Sheng, Minghao Que, Qixuan Zhang, Lan Xu, Feng Xu
https://arxiv.org/abs/2506.03478

Facial Appearance Capture at Home with Patch-Level Reflectance Prior
Existing facial appearance capture methods can reconstruct plausible facial reflectance from smartphone-recorded videos. However, the reconstruction quality is still far behind the ones based on studio recordings. This paper fills the gap by developing a novel daily-used solution with a co-located smartphone and flashlight video capture setting in a dim room. To enhance the quality, our key observation is to solve facial reflectance maps within the data distribution of studio-scanned ones. Spec…

Minimal Neuron Circuits -- Part I: Resonators
Amr Nabil, T. Nandha Kumar, Haider Abbas F. Almurib
https://arxiv.org/abs/2506.02341 https://

Minimal Neuron Circuits -- Part I: Resonators
Spiking Neural Networks have earned increased recognition in recent years owing to their biological plausibility and event-driven computation. Spiking neurons are the fundamental building components of Spiking Neural Networks. Those neurons act as computational units that determine the decision to fire an action potential. This work presents a methodology to implement biologically plausible yet scalable spiking neurons in hardware. We show that it is more efficient to design neurons that mimic …

This https://arxiv.org/abs/2408.02211 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

SceneMotifCoder: Example-driven Visual Program Learning for Generating 3D Object Arrangements
Despite advances in text-to-3D generation methods, generation of multi-object arrangements remains challenging. Current methods exhibit failures in generating physically plausible arrangements that respect the provided text description. We present SceneMotifCoder (SMC), an example-driven framework for generating 3D object arrangements through visual program learning. SMC leverages large language models (LLMs) and program synthesis to overcome these challenges by learning visual programs from ex…

Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Chenxiang Ma, Xinyi Chen, Kay Chen Tan, Jibin Wu
https://arxiv.org/abs/2506.01117 https://…

Spatio-Temporal Decoupled Learning for Spiking Neural Networks
Spiking neural networks (SNNs) have gained significant attention for their potential to enable energy-efficient artificial intelligence. However, effective and efficient training of SNNs remains an unresolved challenge. While backpropagation through time (BPTT) achieves high accuracy, it incurs substantial memory overhead. In contrast, biologically plausible local learning methods are more memory-efficient but struggle to match the accuracy of BPTT. To bridge this gap, we propose spatio-tempora…