Tootfinder

Improving Narrative Classification and Explanation via Fine Tuned Language Models
Rishit Tyagi, Rahul Bouri, Mohit Gupta
https://arxiv.org/abs/2509.04077 https://

Improving Narrative Classification and Explanation via Fine Tuned Language Models
Understanding covert narratives and implicit messaging is essential for analyzing bias and sentiment. Traditional NLP methods struggle with detecting subtle phrasing and hidden agendas. This study tackles two key challenges: (1) multi-label classification of narratives and sub-narratives in news articles, and (2) generating concise, evidence-based explanations for dominant narratives. We fine-tune a BERT model with a recall-oriented approach for comprehensive narrative detection, refining predi…

TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation
Vincent Ochs, Florentin Bieder, Sidaty el Hadramy, Paul Friedrich, Stephanie Taha-Mehlitz, Anas Taha, Philippe C. Cattin
https://arxiv.org/abs/2510.01136

TabINR: An Implicit Neural Representation Framework for Tabular Data Imputation
Tabular data builds the basis for a wide range of applications, yet real-world datasets are frequently incomplete due to collection errors, privacy restrictions, or sensor failures. As missing values degrade the performance or hinder the applicability of downstream models, and while simple imputing strategies tend to introduce bias or distort the underlying data distribution, we require imputers that provide high-quality imputations, are robust across dataset sizes and yield fast inference. We …

Inductive Bias and Spectral Properties of Single-Head Attention in High Dimensions
Fabrizio Boncoraglio, Vittorio Erba, Emanuele Troiani, Florent Krzakala, Lenka Zdeborov\'a
https://arxiv.org/abs/2509.24914

Inductive Bias and Spectral Properties of Single-Head Attention in High Dimensions
We study empirical risk minimization in a single-head tied-attention layer trained on synthetic high-dimensional sequence tasks, given by the recently introduced attention-indexed model. Using tools from random matrix theory, spin-glass physics, and approximate message passing, we derive sharp asymptotics for training and test errors, locate interpolation and recovery thresholds, and characterize the limiting spectral distribution of the learned weights. Weight decay induces an implicit nuclear…

Quantifying Clinician Bias and its Effects on Schizophrenia Diagnosis in the Emergency Department of the Mount Sinai Health System
Alissa A. Valentine, Lauren A. Lepow, Lili Chan, Alexander W. Charney, Isotta Landi
https://arxiv.org/abs/2509.02651

Quantifying Clinician Bias and its Effects on Schizophrenia Diagnosis in the Emergency Department of the Mount Sinai Health System
In the United States, schizophrenia (SCZ) carries a race and sex disparity that may be explained by clinician bias - a belief held by a clinician about a patient that prevents impartial clinical decision making. The emergency department (ED) is marked by higher rates of stress that lead to clinicians relying more on implicit biases during decision making. In this work, we considered a large cohort of psychiatric patients in the ED from the Mount Sinai Health System (MSHS) in New York City to in…

Programmable Cognitive Bias in Social Agents
Xuan Liu, Haoyang Shang, Haojian Jin
https://arxiv.org/abs/2509.13588 https://arxiv.org/pdf/2509.13588

Programmable Cognitive Bias in Social Agents
This paper introduces CoBRA, a novel toolkit for systematically specifying agent behavior in LLM-based social simulation. We found that conventional approaches that specify agent behaviors through implicit natural language descriptions cannot yield consistent behaviors across models, and the produced agent behaviors do not capture the nuances of the descriptions. In contrast, CoBRA presents a new approach to program agents' cognitive biases explicitly, by grounding agents' expected behaviors us…

High-Frequency First: A Two-Stage Approach for Improving Image INR
Sumit Kumar Dam, Mrityunjoy Gain, Eui-Nam Huh, Choong Seon Hong
https://arxiv.org/abs/2508.15582 https://

High-Frequency First: A Two-Stage Approach for Improving Image INR
Implicit Neural Representations (INRs) have emerged as a powerful alternative to traditional pixel-based formats by modeling images as continuous functions over spatial coordinates. A key challenge, however, lies in the spectral bias of neural networks, which tend to favor low-frequency components while struggling to capture high-frequency (HF) details such as sharp edges and fine textures. While prior approaches have addressed this limitation through architectural modifications or specialized …

Sharpness of Minima in Deep Matrix Factorization: Exact Expressions
Anil Kamber, Rahul Parhi
https://arxiv.org/abs/2509.25783 https://arxiv.org/pdf/2509.25…

Sharpness of Minima in Deep Matrix Factorization: Exact Expressions
Understanding the geometry of the loss landscape near a minimum is key to explaining the implicit bias of gradient-based methods in non-convex optimization problems such as deep neural network training and deep matrix factorization. A central quantity to characterize this geometry is the maximum eigenvalue of the Hessian of the loss, which measures the sharpness of the landscape. Currently, its precise role has been obfuscated because no exact expressions for this sharpness measure were known i…

The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Konrad L\"ohr, Shuzhou Yuan, Michael F\"arber
https://arxiv.org/abs/2510.08236

The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models
Large Language Models (LLMs) are increas- ingly integral to information dissemination and decision-making processes. Given their grow- ing societal influence, understanding potential biases, particularly within the political domain, is crucial to prevent undue influence on public opinion and democratic processes. This work investigates political bias and stereotype propa- gation across eight prominent LLMs using the two-dimensional Political Compass Test (PCT). Initially, the PCT is employed to…

Simulation-Based Inference for Direction Reconstruction of Ultra-High-Energy Cosmic Rays with Radio Arrays
Oscar Macias, Zachary Mason, Matthew Ho, Ars\`ene Ferri\`ere, Aur\'elien Benoit-L\'evy, Mat\'ias Tueros
https://arxiv.org/abs/2508.15991

Simulation-Based Inference for Direction Reconstruction of Ultra-High-Energy Cosmic Rays with Radio Arrays
Ultra-high-energy cosmic-ray (UHECR) observatories require unbiased direction reconstruction to enable multi-messenger astronomy with sparse, nanosecond-scale radio pulses. Explicit likelihood methods often rely on simplified models, which may bias results and understate uncertainties. We introduce a simulation-based inference pipeline that couples a physics-informed graph neural network (GNN) to a normalizing-flow posterior within the \textit{Learning the Universe Implicit Likelihood Inference…

No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks
Yehonatan Refael, Guy Smorodinsky, Ofir Lindenbaum, Itay Safran
https://arxiv.org/abs/2509.21296

No Prior, No Leakage: Revisiting Reconstruction Attacks in Trained Neural Networks
The memorization of training data by neural networks raises pressing concerns for privacy and security. Recent work has shown that, under certain conditions, portions of the training set can be reconstructed directly from model parameters. Some of these methods exploit implicit bias toward margin maximization, suggesting that properties often regarded as beneficial for generalization may actually compromise privacy. Yet despite striking empirical demonstrations, the reliability of these attacks…

Random-effects meta-analysis via generalized linear mixed models: A Bartlett-corrected approach for few studies
Keisuke Hanada, Tomoyuki Sugimoto
https://arxiv.org/abs/2508.08758

Random-effects meta-analysis via generalized linear mixed models: A Bartlett-corrected approach for few studies
Random-effects meta-analysis is widely used for synthesizing results across studies, but the implicit assumption of normality in study-specific aggregate data is often violated. Such violations can lead to biased estimates and misleading conclusions, especially in meta-analyses with small studies or rare events. A prominent example occurs with the log-odds ratio, which exhibits bias that depends on the within-study sample sizes. We first show that conventional methods assuming normality fail to…

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
Qihu Xie, Yuan Li, Yi Kang
https://arxiv.org/abs/2509.07373 https…

SBS: Enhancing Parameter-Efficiency of Neural Representations for Neural Networks via Spectral Bias Suppression
Implicit neural representations have recently been extended to represent convolutional neural network weights via neural representation for neural networks, offering promising parameter compression benefits. However, standard multi-layer perceptrons used in neural representation for neural networks exhibit a pronounced spectral bias, hampering their ability to reconstruct high-frequency details effectively. In this paper, we propose SBS, a parameter-efficient enhancement to neural representatio…

Very glad to finally see this issue being addressed in a major publication. The headline is a little confusing. It’s not about engineering, it’s about all disciplines.
Even as we have made gains getting women into post secondary now young men are not coming, aside from a few specific traditional areas. Especially in Arts and Humanities, there is a huge gap. I see it every day at my university.
We need to reverse this. We need men and women in every field and discipline. We need men and women learning critical thinking, learning history, learning biology, learning engineering, together, collaboratively, in ways that speak to all genders.
I blame a large part of the gap on a notion that is still perpetuated by both regular people and government, that “trades gets u a job” with implicit and explicit bias toward those trades being male dominated. The manosphere often degrades traditional “thinking” degrees as not masculine enough.. or have been “woke” and a threat to stereotypical male roles. Plus there remain biases against teaching and healthcare as “women’s” work in those influential spheres, and not helped by general society either.
TLDR: we just need to stop devaluing post-secondary every other moment of the day and make it free and easy for absolutely anyone to walk in and expand their minds!
#viu #university #postsecondary #canada #education #gendergap #men #manosphere
https://archive.ph/NhuKo

Intrinsic training dynamics of deep neural networks
Sibylle Marcotte, Gabriel Peyr\'e, R\'emi Gribonval
https://arxiv.org/abs/2508.07370 https://ar…

Intrinsic training dynamics of deep neural networks
A fundamental challenge in the theory of deep learning is to understand whether gradient-based training in high-dimensional parameter spaces can be captured by simpler, lower-dimensional structures, leading to so-called implicit bias. As a stepping stone, we study when a gradient flow on a high-dimensional variable $θ$ implies an intrinsic gradient flow on a lower-dimensional variable $z = ϕ(θ)$, for an architecture-related function $ϕ$. We express a so-called intrinsic dynamic property and…