Skip to content

Category

machine learning

17,191 papers

#machine learning Preprint Oct 2026

Similar Models, Broken Odds: Where Luce's Axiom Fails in Chatbot Arena

Chatbot Arena ranks language models with the Bradley-Terry model, which gives each model one score and so assumes Luce's choice axiom. Choice theory predicts where this fails: near-copies are told apart more decisively than their scores imply. On 1.8 million Arena votes, votes between versions of one model are 1.37 tim...

S. Bakshi, Apurba Bose, Aishwarya Bose et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Revisiting Generative vs. Dense Retrieval: A Static Value Prior Reverses the Gap

Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers have improved. However, GR is not a uniformly stronger replacement of dense retrieval (DR) in online retail candidate generation. We examine their differences on two Amazo...

Ren-Bo Tu, Yang Liu, Shu-Ying Sun · 0 citations
#machine learning Preprint Open access Oct 2026

Conformalized Physics-Informed Weak Regression for Parameter Estimation in Epidemic Models

Compartment models of infectious diseases are often linear in coefficients that are physical parameters or products or ratios of them. We exploit this with closed-form least squares and equip the estimates with distribution-free uncertainty. Physics-informed regression (PIR) fits the parameters to finite-difference der...

Allan Peter Engsig-Karup · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Smart Alone, Blind Together: Why Accuracy Fails to CertifyTheory of Mind in Multi-Agent Teams

Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences. We argue that accuracy is the wrong certificate, for two reasons. First, the accuracy needed to act depends on what a wrong action costs, so the bar belongs to the deployment, not the module. Second, a mo...

Will Beaumaster, Paul Schrater · 0 citations
#machine learning Preprint Open access Oct 2026

Optimization for One, Feasibility for Most: Decoupling Optimization from Feasibility in Chance-Constrained Programs

Chance-constrained stochastic programs (CCSPs) require solutions that satisfy uncertain constraints with high probability. Standard approaches either embed scenarios directly into optimization by considering sample average approximation (SAA)-based reformulations, growing prohibitively expensive and often failing to fi...

Justin Dumouchelle, Shunyu Yao, Beste Basciftci et al. · 0 citations
#machine learning Preprint Oct 2026

How Far Can Coding Agents Go in Visual Generation?

Modern image generation largely relies on specialized generative models that leave the process of image construction implicit in learned parameters. We explore an alternative: general-purpose coding agents that construct images through executable programs. Our study of zero-shot drawing on AgentDrawBench, a 200-prompt...

Heng-Guang Zhou, Yuan-Hao Ban, Jia-Qi Feng et al. · 0 citations
#machine learning Preprint Oct 2026

Discrete Diffusion Models are Minimax Optimal Under Sparsity

Discrete diffusion models have emerged as a leading alternative to autoregressive models for generating text and other categorical data, yet their theoretical foundations remain less developed than those of their continuous counterparts. For continuous diffusion models, a prominent line of theory suggests that these mo...

Luca Blunda, Tyler Farghly, Umut Simsekli et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Discovering Undisclosed Concepts in LoRA Adapters for Diffusion-Based AIGC Models

Low-Rank Adaptation (LoRA) has made diffusion model personalization affordable and widely shareable, but it also creates moderation risks when harmful or problematic content is not faithfully disclosed. Hence, it is desired to develop a mechanism that, given a LoRA adapter, discovers the concept it was trained to gener...

Yupeng Qiu, Han Fang, Dhananjai Sharma et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Virtual-Alloy Physics-Aware Semantic System for extrapolative prediction of perovskite solar cells

Existing machine-learning models for perovskite solar cells struggle to predict devices made from new materials. Most treat device layers as arbitrary categorical labels and represent perovskite absorbers either through featureless composition vectors or through rigid crystal graphs that require a pre-solved unit cell....

Qiwei Han, Chi Zhou · 0 citations
#machine learning Preprint Oct 2026

Limits of Statistical Assurance for Embodied and Agentic AI under Adaptive Adversaries

Finite safety assessments cannot exclude an adversary that preserves unattacked behavior until certification and activates afterward. We characterize this limitation for embodied and tool-use AI. An adversary that matches the complete observation law until observable, irrevocable certification and retains intervention...

Inderjeet Singh · 0 citations
#machine learning Preprint Oct 2026

Physical Layer Anomaly Detection via Adaptive Gaussian Mixture Model

Physical-layer spoofing attacks pose a critical threat to wireless localization systems by manipulating channel state information (CSI). Detecting such attacks becomes particularly challenging when the attacker and the legitimate node are spatially proximate, as their CSI characteristics become increasingly similar. To...

Mamady Delamou, Luan Chen, A. Chorti · 0 citations
#machine learning Preprint Oct 2026

Semi-Amortized Bayesian Inference with Reusable Local Posteriors

A semi-amortized method is introduced that divides parameters into smaller blocks, and uses conditional independence to identify the information needed to infer each block, and trains neural estimators for blocks whose posterior updates are computationally expensive or frequently repeated.

Niek Stevenson, Paul-Christian Burkner, Andrew Heathcote et al. · 0 citations

From tech blogs

See all →
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.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.