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artificial intelligence

14,515 papers

#artificial intelligence Preprint Open access Oct 2026

Characterize Then Distill: Mechanistic Reasoning in Large Output Spaces

Reasoning-trained language models can perform, zero-shot, multi-label tasks that require selecting a small set of relevant labels from a universe of thousands to hundreds of thousands of candidates. We ask how they do it mechanistically, and whether the mechanism can be distilled. We make the question measurable by tre...

Debjyoti Saha Roy · 0 citations
#artificial intelligence Preprint Open access Oct 2026

FHRFormer: A Self-Supervised Masked Transformer Framework for Fetal Heart Rate Time-Series Inpainting and Forecasting

Approximately 10% of newborns require assistance to initiate breathing at birth, and around 5% need ventilation support. Fetal heart rate (FHR) monitoring plays a crucial role in assessing fetal well-being during prenatal care, enabling the detection of abnormal patterns and supporting timely obstetric interventions to...

Kjersti Engan, Neel Kanwal, Anita Yeconia et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Voice "Cloning" is Style Transfer

Artificially generated speech is increasingly embedded in everyday life. Voice cloning in particular enables applications where identity preservation is important, such as completing a recording, dubbing in a new language, or preserving the voices of individuals with speech loss. However, in our work, we find that desp...

Kaitlyn Zhou, Federico Bianchi, Martijn Bartelds et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Learning Visual Feature-Based World Models via Residual Latent Action

World models predict future transitions from observations and actions. Existing works predominantly focus on image generation only. Visual feature-based world models, on the other hand, predict future visual features instead of raw video pixels, offering a promising alternative that is more efficient and less prone to...

Xinyu Zhang, Zhengtong Xu, Yutian Tao et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Rethinking Adapter Placement: A Dominant Adaptation Module Perspective

Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning method that places trainable low-rank adapters into frozen pre-trained models. Recent studies show that using fewer LoRA adapters may still maintain or even improve performance, but existing methods still distribute adapters broadly, leaving \e...

Suoxin Zhang, Run He, Di Fang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

XDecomposer: Learning Prior-Free Set Decomposition for Multiphase X-ray Diffraction

Multiphase powder X-ray diffraction (PXRD) analysis remains a fundamental bottleneck in structure identification, as real-world synthesis often produces complex mixtures whose constituent phases (components) cannot be reliably disentangled. While recent advances in representation-based crystal retrieval and generation...

Hanyu Gao, Bin Cao, Yunyue Su et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Von Neumann Networks

In the mid-twentieth century, mathematician and polymath John von Neumann created a computational system on an array of cells as a simple model of the human brain, where each cell had one of a finite set of roles or states that he predicted would be modelled by a diffusion process. In this work, we show that such a sys...

Shekhar S. Chandra · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Algorithm Selection with Zero Domain Knowledge via Text Embeddings

We propose ZeroFolio, a feature-free approach to algorithm selection that uses pretrained text embeddings instead of hand-crafted instance features. It reads the raw instance file as plain text, embeds it with a pretrained embedding model, and selects an algorithm via weighted k-nearest neighbors. Our approach is based...

Stefan Szeider · 0 citations
#artificial intelligence Preprint Open access Oct 2026

World Properties without World Models: Distributional Associations and the Interpretation of Decoding Results from Language Models

A growing literature shows that variables can be linearly decoded from the activations of large language models (LLMs). These range from properties of the world, such as the locations of cities and the lifetimes of historical figures, to emotions and pain. Such findings are often taken as evidence that language models...

Elan Barenholtz · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Qubit-centric Transformer for Surface Code Decoding

For reliable large-scale quantum computation, quantum error correction (QEC) is essential to protect logical information distributed across multiple physical qubits. Taking advantage of recent advances in deep learning, neural network-based decoders have emerged as a promising approach to improve the reliability of QEC...

Seong-Joon Park, Hee-Youl Kwak, Yongjune Kim · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Breaking the Mirror: Activation-Based Mitigation of Self-Preference in LLM Evaluators

Large language models (LLMs) increasingly serve as automated evaluators, yet they suffer from "self-preference bias": a tendency to favor their own outputs over those of other models. This bias undermines fairness and reliability in evaluation pipelines, particularly for tasks like preference tuning and model routing....

Dani Roytburg, Matthew Bozoukov, Matthew Nguyen et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Practical Feasibility of Gradient Inversion Attacks in Federated Learning

Gradient inversion attacks are often presented as a serious privacy threat in federated learning, with recent work reporting increasingly strong reconstructions under favorable experimental settings. However, it remains unclear whether such attacks are feasible in modern, performance-optimized systems deployed in pract...

Viktor Valadi, Lucas Beerens, Mattias {\AA}kesson et al. · 0 citations

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MIT News · Artificial Intelligence Sep 29, 2026

Who we become when we talk to machines

Professor Sherry Turkle’s new book, “Artificial Intimacy,” offers a withering critique of chatbots and the antisocial dynamics she believes they encourage.

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