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18,068 papers

#machine learning Preprint Oct 2026

Target-Token Asymmetry Can Masquerade As Depth Specialization

Layer-ablation studies often perturb early or late layers of a language model, compare the damage to prediction on two corpora, and read a larger early-layer effect on one corpus as early-layer specialization for its task. Such a comparison averages over the tokens that each corpus asks the model to predict, and these...

Dan-Lin Li, Miao Li, Xu Han et al. · 0 citations
#machine learning Open access Oct 2026

TopicMerge: A Divide and Conquer Approach to Topic Modeling

Recent advancements in topic modeling have split into two main paths: efficient embedding-based methods that offer scalability but are often outperformed on standard clustering metrics, and Large Language Model (LLM) approaches that achieve state-of-the-art clustering performance but at the cost of prohibitive computat...

Matthew Raffel, Li-Zhong Chen, Sai Munikoti et al. · 0 citations
#machine learning Preprint Oct 2026

Multi-Sensitive Distributional Fairness: Joint Wasserstein Audits and Shapley Allocation

Fairness audits with several sensitive attributes are often performed one coordinate at a time. This can miss purely intersectional disparities: a score may be independent of each sensitive coordinate while depending on their joint profile. We study distributional demographic parity for a real-valued score using one-di...

Agathe Fernandes Machado, Charlotte Jamotton, Arthur Charpentier · 0 citations
#machine learning Preprint Open access Oct 2026

From Detection To Anticipation: Continuous Onset Forecasting For Stuttered Speech

Automatic stuttering systems primarily detect dysfluencies after they have occurred, limiting their use for anticipatory intervention. We address this gap by formulating stuttering prediction as continuous causal time-to-event forecasting, where a model estimates the risk of an upcoming stuttering onset every 100 ms us...

Tampu Ravi Kumar, Ayoub Daliri, Vivek Gupta · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Generalizing Identifiability Results in Causal Discovery from Complete Data to Missing Data Settings

While causal discovery methods have matured for complete data, such algorithms for handling missing values remain limited. We establish general sufficient conditions under which causal graphs remain identifiable (i.e., uniquely determined from the available data distribution) under various missingness categories, inclu...

Matteo Ceriscioli, Shohei Shimizu, Mallika Gupta et al. · 0 citations
#machine learning Preprint Oct 2026

Structure-aware Bayesian full waveform inversion via diffusion-regularized Stein variational gradient descent

Numerical experiments demonstrate that the diffusion-regularized ASVGD-FWI produces more geologically consistent mean models, suppresses nonphysical artifacts, and yields uncertainty estimates that are more concentrated around structurally complex and poorly constrained regions.

Chao Li, Mrinal K. Sen, Sergey Fomel et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Into the Unknown: Benchmarking AI agents on open scientific research problems

Can AI agents carry out research on open scientific problems? We introduce Into the Unknown, a benchmark that provides an agent with a science paper along with a research direction to pursue and tasks it to write a follow-up paper. We work with scientists at US Department of Energy (DOE) national laboratories, who each...

Seungone Kim, Jia-Yi Geng, Young-Jun Lee et al. · 3 citations · ⚡1
#machine learning Preprint Oct 2026

Online Tokenization with Optimal Delay in Linear Time

Tokenization is an essential preprocessing step in almost all large language models (LLMs). One of the most popular frameworks is BPE tokenization, which consists of special token segmentation, pretokenization, and byte-pair encoding (BPE). However, existing algorithms are sequential, with each stage only processing co...

Mina Dalirrooyfard, Christopher Ye · 0 citations
#machine learning Preprint Open access Oct 2026

Student Copula Markov Networks

This paper extends the applicability of probabilistic graphical models to non Gaussian data. It establishes a characterization of the pairwise Markov property in undirected probabilistic graphical models in terms of copula densities. Building on this characterization, we derive a necessary condition for the pairwise Ma...

Salha Mamane, Cheikh Tidiane Seck · 0 citations

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Microsoft Research Blog Nov 3, 2026

Learn the rules: The hacker mindset

Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.

Google DeepMind Blog Nov 3, 2026

Agentic cheminformatics with Google Antigravity

Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.

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