Tootfinder

Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns
Hanqi Xiao, Vaidehi Patil, Hyunji Lee, Elias Stengel-Eskin, Mohit Bansal
https://arxiv.org/abs/2509.24988

Generalized Correctness Models: Learning Calibrated and Model-Agnostic Correctness Predictors from Historical Patterns
Generating accurate and calibrated confidence estimates is critical for deploying LLMs in high-stakes or user-facing applications, and remains an open challenge. Prior research has often framed confidence as a problem of eliciting a model's "self-knowledge", i.e., the ability of an LLM to judge whether its own answers are correct; this approach implicitly assumes that there is some privileged information about the answer's correctness that is accessible to the model itself. However, our experim…

This is gonna be great! This company has punched above its weight for years. A small team of like 6 talented people. My recommendation: Hire them to do some work OR support their Patreon, but definitely send them a good question.
https://iconfactory.world/@Iconfactory/1150516380960…

The Iconfactory (@Iconfactory@iconfactory.world)
Attached: 1 image Inspired by a suggestion from Mastodon, we’re going to try something new - Ask the Iconfactory! Patreon supporters can ask us the answer to that burning Iconfactory-related question and we’ll answer our favorites in an upcoming podcast. Supporters will get early access to the public audio ep and if it’s successful we may do more! Leave your questions in the comments. Now’s your chance, ask away! https://www.patreon.com/posts/ask-iconfactory-136788695

Here’s a healthcare lawyer answering some common questions including if it is ethical to get the COVID vaccine, are you taking a dose away from someone who really needs it, etc
(free and shareable link)
https://www.patreon.com/posts/138476876/

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[2/5]:
- PromptGAR: Flexible Promptive Group Activity Recognition
Zhangyu Jin, Andrew Feng, Ankur Chemburkar, Celso M. De Melo

On subshifts with low maximal pattern complexity
Anh N. Le, Ronnie Pavlov, Casey Schlortt
https://arxiv.org/abs/2508.13420 https://arxiv.org/pdf/2508.13420…

On subshifts with low maximal pattern complexity
For a finite alphabet $\mathcal{A}$ and a sequence $x \in \mathcal{A}^{\mathbb{N}}$, Kamae and Zamboni defined the maximal pattern complexity function $p^*_x(n)$ as a natural generalization of usual word complexity. They defined a nonperiodic sequence $x$ to be pattern Sturmian if it achieves the minimal growth rate $p^*_x(n) = 2n$, and asked the question of whether one could classify recurrent pattern Sturmian sequences. We answer their question by characterizing recurrent pattern Sturmian s…

KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling
Yangfan Wang, Jie Liu, Chen Tang, Lian Yan, Jingchi Jiang
https://arxiv.org/abs/2508.20567 https…

KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling
Multi-hop question answering faces substantial challenges due to data sparsity, which increases the likelihood of language models learning spurious patterns. To address this issue, prior research has focused on diversifying question generation through content planning and varied expression. However, these approaches often emphasize generating simple questions and neglect the integration of essential knowledge, such as relevant sentences within documents. This paper introduces the Knowledge Comp…

Multi-channel, multi-template event reconstruction for SuperCDMS data using machine learning
M. F. Albakry, I. Alkhatib, D. Alonso-Gonzalez, J. Anczarski, T. Aralis, T. Aramaki, I. Ataee Langroudy, C. Bathurst, R. Bhattacharyya, A. J. Biff, P. L. Brink, M. Buchanan, R. Bunker, B. Cabrera, R. Calkins, R. A. Cameron, C. Cartaro, D. G. Cerdeno, Y. -Y. Chang, M. Chaudhuri, J. H. Chen, R. Chen, N. Chott, J. Cooley, H. Coombes, P. Cushman, R. Cyna, S. Das, S. Dharani, M. L. di Vacri, M. D. D…

Multi-channel, multi-template event reconstruction for SuperCDMS data using machine learning
SuperCDMS SNOLAB uses kilogram-scale germanium and silicon detectors to search for dark matter. Each detector has Transition Edge Sensors (TESs) patterned on the top and bottom faces of a large crystal substrate, with the TESs electrically grouped into six phonon readout channels per face. Noise correlations are expected among a detector's readout channels, in part because the channels and their readout electronics are located in close proximity to one another. Moreover, owing to the large size…

Steerable Invariant Beamformer Using a Differential Line Array of Omnidirectional and Directional Microphones with Null Constraints
Yankai Zhang, Jiafeng Ding, Jingjing Ning, Qiaoxi Zhu
https://arxiv.org/abs/2508.17607

Steerable Invariant Beamformer Using a Differential Line Array of Omnidirectional and Directional Microphones with Null Constraints
Line differential microphone arrays have attracted attention for their ability to achieve frequency-invariant beampatterns and high directivity. Recently, the Jacobi-Anger expansion-based approach has enabled the design of fully steerable-invariant differential beamformers for line arrays combining omnidirectional and directional microphones. However, this approach relies on the analytical expression of the ideal beam pattern and the proper selection of truncation order, which is not always pra…

MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering
Hikaru Asano, Hiroki Ouchi, Akira Kasuga, Ryo Yonetani
https://arxiv.org/abs/2508.11163

MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering
This paper presents MobQA, a benchmark dataset designed to evaluate the semantic understanding capabilities of large language models (LLMs) for human mobility data through natural language question answering. While existing models excel at predicting human movement patterns, it remains unobvious how much they can interpret the underlying reasons or semantic meaning of those patterns. MobQA provides a comprehensive evaluation framework for LLMs to answer questions about diverse human GPS traje…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/7]:
- ETVA: Evaluation of Text-to-Video Alignment via Fine-grained Question Generation and Answering
Kaisi Guan, Zhengfeng Lai, Yuchong Sun, Peng Zhang, Wei Liu, Kieran Liu, Meng Cao, Ruihua Song