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15,964 papers

#machine learning Preprint Open access Oct 2026

Does Financial Speech Need Continual Learning Regularization? A Study of EWC and mini-KFAC on Earnings Call ASR

Earnings call transcription presents a realistic continual learning scenario where Automatic Speech Recognition (ASR) systems are updated with new recordings but risk catastrophic forgetting of previously learned financial speech patterns. Adapting Whisper-small from a general-domain speech corpus to the Earnings22 ben...

Thai Quoc On, Homayoon Beigi · 0 citations
#machine learning Preprint Open access Oct 2026

One Thing Leads to Another: The Essentials of Time Series Causal Discovery

Time series causal discovery --- the process of detecting causation between time series --- has its roots in signal processing research from over a half century ago. It has since received active and widespread interest, evolving into different forms across domains such as statistics, dynamical systems and machine learn...

Kurt Butler, Damian Machlanski, Michael P. J. Camilleri et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Desertification Forecasting in Semi-Arid Regions: Using Deep Learning to Probe the Observability Limits of Land Degradation Dynamics

Forecasting desertification has received far less attention than mapping it. We use a convolutional LSTM (ConvLSTM) trained on ten annual Earth Observation (EO) variables (2000-2023) over the semi-arid state of Cear\'a, Brazil, on a 256x206 grid at roughly 2.2 km resolution, to measure how much of the year-to-year evol...

Filipe Moura Da Silva, Cl\'aubio Landney Lima Bandeira · 0 citations
#machine learning Preprint Oct 2026

Model-free Bayesian Clustering and Multimodal Posterior Summarisation

We develop a model-free, noise-aware Bayesian clustering framework that constructs a martingale posterior over clusterings from a predictive Bayesian perspective. This avoids explicit likelihood-prior model specification, expensive MCMC sampling, and mode-seeking variational approximations, while accommodating irregula...

Qing Li, Sara Wade · 0 citations
#machine learning Preprint Oct 2026

Overcoming Data Imbalances with Expert Selection Biases

Mixture-of-Experts (MoE) models have become the standard architecture for frontier LLMs. The multiple parallel experts in each transformer block theoretically enable expert specialization according to categories such as domains, languages, or modalities during pretraining. However, when data is imbalanced across catego...

A. Tarun, Clara Meister, Antoine Bosselut · 0 citations
#machine learning Preprint Oct 2026

EmoGavel: Grounding-Aware Reinforcement Learning with Verifiable Rewards for Multimodal Emotion Preference Learning

Multimodal emotion preference learning uses pairwise comparisons to identify which open-ended emotion descriptions align more closely with human judgments. However, existing methods primarily focus on the final preference decision and provide little explicit supervision for localizing and correcting erroneous content....

Zi-Long Huang, K. Lee, Ze-Zhong Jin et al. · 0 citations
#machine learning Preprint Oct 2026

Taking Tokenization off the Host for Agentic LLM Serving

GraphTok is presented, a GPU tokenizer that runs the entire encoding pipeline, from pre-tokenization through byte-pair encoding (BPE) merging to token-ID assembly, as one replayable CUDA graph.

Mohammad Siavashi, Pascal Schwinte, A. Banaei et al. · 1 citation
#machine learning Preprint Open access Oct 2026

JEPA-kMMVAE: CSI Prediction for Time-Varying Wireless Channels

This paper considers channel state information (CSI) prediction using neural networks for time-varying multi-antenna channels from noisy historical observations. The main challenge is to exploit temporal correlation while distinguishing channel evolution from observation noise. We propose JEPA-kMMVAE, which combines a...

Efstathios Chatziloizos, Konstantinos Vandikas, Aneta Vulgarakis Feljan et al. · 0 citations
#machine learning Preprint Oct 2026

Sonic-AMG: Streaming Human Motion Generation from Spatial Audio

Modeling human reactions to spatial audio requires understanding both what is heard and where it comes from. Prior work generates motion offline from complete acoustic and spatial input sequences. Humans, however, react to sounds without knowing what they will hear next. We present Sonic-AMG, to our knowledge the first...

J. Ni, Ze-Kun Li, Zhiyang Dou et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Decoding Register Tokens Preserves Global Scene Details

Pretrained vision transformers learn structured image representations spanning spatial patch tokens and non-spatial tokens. Recent analyses suggest that non-spatial register tokens encode global scene properties such as illumination, blur, and style. Yet patch-based image decoders discard these outputs, leaving the dec...

David Jacob Drexlin, Julius Hense, Jonas Dippel · 0 citations
#machine learning Preprint Open access Oct 2026

Do Prompt Optimizers Break Requirements They Are Not Scored On? A Preregistered Study

Prompt optimizers rewrite a prompt to raise one target metric, but prompts also state requirements the metric does not score, such as an output format, citations, or abstention. In a preregistered study, we run GEPA, MIPROv2, and TextGrad on HotpotQA, SQuAD 2.0, and IFEval and count per-item violations of unscored requ...

Sangdo Han, Moonsoo Kim · 0 citations

From tech blogs

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MIT News · Artificial Intelligence Oct 28, 2026

Making machines that can listen

Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.

MIT News · Artificial Intelligence Oct 23, 2026

Q&A: How AI came to model the “messy” human mind

Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.

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