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17,191 papers

#machine learning Preprint Open access Oct 2026

The Loss or the Architecture? What Matters in Survival Analysis with Competing Risks

Often, \emph{eg} in healthcare and predictive maintenance, what matters is which of several competing causes will strike first, and with what probability: survival analysis with competing risks. Corresponding deep learning methods are developed as bundles: a new architecture together with a new training loss, tested on...

Julie Alberge, Ga\"el Varoquaux, Judith Ab\'ecassis · 0 citations
#machine learning Preprint Oct 2026

Look, Reason, Draw a Region: Recursive Refinement for Spatial Context Prediction

Spatial-TRM is introduced, a compact recursive model with frozen vision-language features, a convex region decoder, and just 11.2M trainable parameters that improves spatial context prediction from pretrained vision-language features at a fixed trainable parameter count on this benchmark.

Wajeeha Tahir, Anna Tegon, Lionnus Kesting et al. · 0 citations
#machine learning Preprint Open access Oct 2026

How the elastic axis folds the transonic flutter boundary: shock energy transfer and conformally calibrated active learning

Isogai's case A, a pitch-plunge aerofoil whose elastic axis lies half a chord ahead of the leading edge, has a folded transonic flutter boundary in inviscid flow: near the bottom of the transonic dip several flutter speeds coexist at one Mach number. We map this boundary, and the family obtained by moving the elastic a...

Ashray Saxena, Sharad Shrivastava · 0 citations
#machine learning Preprint Oct 2026

Learning angles for quantum approximate optimization

This work amortizes a major bottleneck in QAOA by an up-front data generation and training cost and can be amortized over time in settings where many similar problems are optimized on a regular basis, as is often the case in applications for industry.

Johannes Jakubik, Isabelle Wittmann, Zaheed Gaffoor et al. · 0 citations
#machine learning Preprint Open access Oct 2026

The Optimal Ranking Tradeoffs Between Two Ranking Scores in Two-Class Classification

The accuracy, true negative rate (negative class recall), true positive rate (positive class recall), negative predictive value (negative class precision), positive predictive value (positive class precision), class-specific intersections over unions, class-specific F-scores, Cohen's kappa, and balanced accuracy are sc...

S\'ebastien Pi\'erard, Adrien Deli\`ege, Marc Van Droogenbroeck · 0 citations
#machine learning Preprint Open access Oct 2026

Distributed Performance Boosting through Neural Control for Plug-and-Play Networks

We address the problem of improving the performance of pre-stabilized networked systems subject to plug-and-play (PnP) operations, namely additions or removals of subsystems. While distributed baseline controllers can preserve stability after a PnP transition, augmenting them with expressive neural policies can invalid...

Danilo Saccani, Leonardo Massai, Giancarlo Ferrari-Trecate · 0 citations
#machine learning Preprint Oct 2026

NestedVLA: Learning to Consolidate and Generate Skills for Vision-Language-Action Models

Vision-language-action (VLA) models can learn a wide range of robotic skills, but combining separately trained skills remains challenging. The original training data may be unavailable for joint retraining, while directly merging model weights can cause interference. We propose NestedVLA, which consolidates independent...

Guo-Dong Du, Wan-Yu Lin · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Reasoning Models Can Control Their Chains of Thought When They Tell Themselves To

Chain-of-thought (CoT) controllability, the ability of reasoning models to control properties of their CoT, is consequential for both their usefulness and reliable oversight. Recent work evaluates this capability by measuring whether models follow CoT constraints given in the user prompt, and reports very low controlla...

Uriel Dolev, Idan Schwartz, Yoav Goldberg · 0 citations
#machine learning Preprint Open access Oct 2026

Think More or Read More? A Token-Budget Theory of Reasoning with Shared Context

Reasoning systems increasingly improve answers by incorporating additional context into the model's input, from multi-agent debate, where agents read their peers' responses, to self-refinement, where a model revisits its own drafts. In these systems, communication consumes context as one agent's output becomes another'...

Manh Nguyen, Sunil Gupta, Hung Le · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Characterising and Analysing Retrieval-Augmented Generation Techniques

The advent of Large Language Models (LLMs) across many application domains has increased the need for Retrieval-Augmented Generation (RAG) to reduce hallucinations and improve LLM performance. Thus far, several RAG techniques with impressive features and capabilities have been proposed in the literature, making it a he...

Emmanuel Igbekele, Olawande Daramola · 0 citations
#machine learning Preprint Open access Oct 2026

Proportionality in Ranking Compression

Motivated by applications in reinforcement learning from human feedback, we consider a setting where a given collection of rankings needs to be compressed into a smaller collection of rankings. This can also be seen as a combination of two fundamental social choice frameworks: ranking aggregation and multiwinner voting...

Daniel Halpern, Xinyu Liu, Evi Micha et al. · 0 citations

From tech blogs

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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.

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.

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