Device-directed speech detection (DDSD) is a binary classification task that separates the user's queries to a voice assistant (V A) from background speech or side conversations. This is important for achieving naturalistic user experience. To this end, we propose knowledge distillation (KD) to enhance DDSD accuracy while ensuring efficient deployment. Specifically, we introduce a novel adaptive KD method that transfers knowledge from general representations of an ASR large pre-trained acoustic encoder ( teacher). We apply task-specific adapters, on top of the (frozen) teacher encoder, trained jointly with the student model on DDSD. We demonstrate that the proposed adaptive KD outperforms the student model without distillation in the keyword and keyword-free (follow-up) invocations, with an improvement of +26% and +19% in terms of Equal Error Rate, respectively. We also show that this approach generalizes across the transformer and conformer-based model architectures.

In everyday life, people make countless reasonableness judgments that determine appropriate behavior in various contexts. Predicting these judgments challenges the legal system, as judges' intuitions may not align with broader societal views. This Article investigates whether large language models (LLMs) can learn to identify patterns driving human reasonableness judgments. Using randomized controlled trials comparing humans and models across multiple legal contexts with over 10,000 simulated judgments, we demonstrate that certain models capture not just surface-level responses but potentially their underlying decisional architecture. Strikingly, these systems prioritize social cues over economic efficiency in negligence determinations, mirroring human behavior despite contradicting textbook treatments. These findings suggest practical applications: judges could calibrate intuitions against broader patterns, lawmakers could test policy interpretations, and resource-constrained litigants could preview argument reception. As AI agents increasingly make autonomous real-world decisions, understanding whether they've internalized recognizable ethical frameworks becomes essential for anticipating their behavior.

The accelerated evolution of digital infrastructures and algorithmic systems is reshaping how the humanities engage with knowledge and culture. Rooted in the traditions of Digital Humanities and Digital Humanism, the concept of "Cyber Humanities" proposes a critical reconfiguration of humanistic inquiry for the post-digital era. This Manifesto introduces a flexible framework that integrates ethical design, sustainable digital practices, and participatory knowledge systems grounded in human-centered approaches. By means of a Decalogue of foundational principles, the Manifesto invites the scientific community to critically examine and reimagine the algorithmic infrastructures that influence culture, creativity, and collective memory. Rather than being a simple extension of existing practices, "Cyber Humanities" should be understood as a foundational paradigm for humanistic inquiry in a computationally mediated world. Keywords: Cyber Humanities, Digital Humanities, Transdisciplinary Epistemology, Algorithmic Reflexivity, Human-centered AI, Ethics-by-Design, Knowledge Ecosystems, Digital Sovereignty, Cognitive Infrastructures

This review presents a comprehensive survey and benchmark of pulse shape discrimination (PSD) algorithms for radiation detection, classifying nearly sixty methods into statistical (time-domain, frequency-domain, neural network-based) and prior-knowledge (machine learning, deep learning) paradigms. We implement and evaluate all algorithms on two standardized datasets: an unlabeled set from a 241Am-9Be source and a time-of-flight labeled set from a 238Pu-9Be source, using metrics including Figure of Merit (FOM), F1-score, ROC-AUC, and inter-method correlations. Our analysis reveals that deep learning models, particularly Multi-Layer Perceptrons (MLPs) and hybrid approaches combining statistical features with neural regression, often outperform traditional methods. We discuss architectural suitabilities, the limitations of FOM, alternative evaluation metrics, and performance across energy thresholds. Accompanying this work, we release an open-source toolbox in Python and MATLAB, along with the datasets, to promote reproducibility and advance PSD research.

Evaluating teaching effectiveness at scale remains a persistent challenge for large universities, particularly within engineering programs that enroll tens of thousands of students. Traditional methods, such as manual review of student evaluations, are often impractical, leading to overlooked insights and inconsistent data use. This article presents a scalable, AI-supported framework for synthesizing qualitative student feedback using large language models. The system employs hierarchical summarization, anonymization, and exception handling to extract actionable themes from open-ended comments while upholding ethical safeguards. Visual analytics contextualize numeric scores through percentile-based comparisons, historical trends, and instructional load. The approach supports meaningful evaluation and aligns with best practices in qualitative analysis and educational assessment, incorporating student, peer, and self-reflective inputs without automating personnel decisions. We report on its successful deployment across a large college of engineering. Preliminary validation through comparisons with human reviewers, faculty feedback, and longitudinal analysis suggests that LLM-generated summaries can reliably support formative evaluation and professional development. This work demonstrates how AI systems, when designed with transparency and shared governance, can promote teaching excellence and continuous improvement at scale within academic institutions.

Interview performance assessment is essential for determining candidates' suitability for professional positions. To ensure holistic and fair evaluations, we propose a novel and comprehensive framework that explores ``365'' aspects of interview performance by integrating \textit{three} modalities (video, audio, and text), \textit{six} responses per candidate, and \textit{five} key evaluation dimensions. The framework employs modality-specific feature extractors to encode heterogeneous data streams and subsequently fused via a Shared Compression Multilayer Perceptron. This module compresses multimodal embeddings into a unified latent space, facilitating efficient feature interaction. To enhance prediction robustness, we incorporate a two-level ensemble learning strategy: (1) independent regression heads predict scores for each response, and (2) predictions are aggregated across responses using a mean-pooling mechanism to produce final scores for the five target dimensions. By listening to the unspoken, our approach captures both explicit and implicit cues from multimodal data, enabling comprehensive and unbiased assessments. Achieving a multi-dimensional average MSE of 0.1824, our framework secured first place in the AVI Challenge 2025, demonstrating its effectiveness and robustness in advancing automated and multimodal interview performance assessment. The full implementation is available at https://github.com/MSA-LMC/365Aspects.

The following is an extract from our Lost in Space-Time newsletter. Each month, we hand over the keyboard to a physicist or mathematician to tell you about fascinating ideas from their corner of the universe. You can sign up for Lost in Space-Time here. The Navier-Stokes equations have been used to model the flow of fluids for almost 200 years – but we still don't really understand them. This can often feel a little odd, especially as we rely on these equations every day to help build rockets, design drugs and understand climate change. But here is where you have to think like a mathematician.

Jim Acosta and James Carville speculated whether President Trump will try to rig the 2026 midterms in his favor on "The Jim Acosta Show." Former CNN anchor Jim Acosta was slammed on social media after he posted a clip of his "interview" with an artificially animated avatar of deceased teenager Joaquin Oliver to promote a gun control message on Monday. Working with the gun control group Change the Ref, founded by Oliver's parents, Acosta had a conversation on his Substack with an avatar created by the father of the son, who was killed in the Parkland high school shooting in 2018. Oliver would have turned 25 on Monday. Social media users were shocked by Acosta's "grotesque" interview and slammed the journalist for using the deceased teen's avatar for political content.

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The rise of generative models for scientific research calls for the development of new methods to evaluate their fidelity. A natural framework for addressing this problem is two-sample hypothesis testing, namely the task of determining whether two data sets are drawn from the same distribution. In large-scale and high-dimensional regimes, machine learning offers a set of tools to push beyond the limitations of standard statistical techniques. In this work, we put this claim to the test by comparing a recent proposal from the high-energy physics literature, the New Physics Learning Machine, to perform a classification-based two-sample test against a number of alternative approaches, following the framework presented in Grossi et al. (2025). We highlight the efficiency tradeoffs of the method and the computational costs that come from adopting learning-based approaches. Finally, we discuss the advantages of the different methods for different use cases.