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17,191 papers

#machine learning Preprint Oct 2026

Analog RF Computing for Integrated Data and AI Flows Over Wireless Networks

Many resource-constrained edge devices require artificial intelligence (AI) services but are not powerful enough to store or execute neural networks (NNs). Analog radio frequency (RF) computing addresses this limitation by letting a base station (BS) multicast a waveform encoding NN weights to different clients. Each c...

Wen-Tao Yu, Vincent W. S. Wong · 1 citation
#machine learning Preprint Open access Oct 2026

Linear Independence of Polynomial Compositions with Bounded Input Degree

We prove the weak composition-independence conjectures of Kohn, Marchetti, Massarenti and Mella. The proof uses deformations of rational curves and a single kernel of the linearized equation. Riemann-Roch gives a contradiction from tangent fields with controlled poles. By settling these conjectures, we prove that a neu...

Xiaotian Chang, Yacoub Hendi · 0 citations
#machine learning Preprint Oct 2026

Privacy-Preserving Text Inference for Health-Misinformation Classification and Semantic Retrieval

Health-misinformation analysis presents a dual challenge: misleading claims can affect public well-being, while systems designed to detect them may expose sensitive user-submitted text. This motivates privacy-preserving inference for public-health applications in which user queries may contain confidential medical info...

Jasleen Kaur, Chloé Cachet, Koray Karabina et al. · 0 citations
#machine learning Preprint Oct 2026

Purify Before You Align: Improving Representation Alignment for Diffusion Models

Representation alignment improves and accelerates diffusion training by encouraging intermediate features to match a pretrained visual encoder. However, standard alignment matches pretrained representations without distinguishing what information may be more useful for generation. We introduce local appearance purifica...

Yingheng Wang, Yao Li, Ya-Qin Wu et al. · 0 citations
#machine learning Preprint Oct 2026

Ergodic topology sampling toward the continuum with Stochastic Normalizing Flows

In this work we provide numerical evidence that topological freezing in $\mathrm{SU}(3)$ Yang-Mills theory can be mitigated with a flow-based approach, as tested at lattice spacings as fine as 0.03~fm. We do so by analyzing the scaling of the costs of a computational strategy based on the combination of open boundary c...

C. Bonanno, Andrea Bulgarelli, E. Cellini et al. · 0 citations
#machine learning Preprint Open access Oct 2026

On the Stability and Convergence of Flow Map Training

Flow maps are a recent method of probabilistic generation that amortize diffusion trajectories into one-step samplers, unlocking efficient generation. While progress has been made in improving their empirical performance, there is still a lack of understanding behind the mechanisms underlying their success. Specificall...

Ishan Nath, Tom Stanic, Avishek Joey Bose et al. · 0 citations
#machine learning Preprint Open access Oct 2026

CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades

We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read semantics, binds successful reads to service and cache identities, records availability and component-wise budget transitions, and freezes the event trace before any target...

Miaobo Hu, Shuhao Hu, Xiaobo Guo et al. · 0 citations
#machine learning Preprint Oct 2026

Graph Additive Models disentangling feature, topology, and interactions

Graph Neural Additive Networks (GNANs) have emerged as effective, interpretable-by-design architectures for graph learning. They achieve transparency by combining per-feature shape functions with simple distance-based reweighting. However, GNANs have three limitations. First, node-wise relevance scores aggregate inform...

C. Álvarez-Rodríguez, Steve Azzolin, Vincenzo Marco De Luca et al. · 0 citations
#machine learning Preprint Oct 2026

Latent Annealed Posterior Sampling with a Variance-Preserving Bridge for Sparsely Observed Random Fields

Posterior inference for random fields from sparse observations presents two major challenges: accurately characterizing complex spatial uncertainty and preventing conditional sampling costs from growing substantially as the spatial grid is refined. To address these challenges, we propose latent annealed posterior sampl...

Jian-Kun Lei, Tian-Xi Qi · 0 citations
#machine learning Preprint Open access Oct 2026

A polylogarithmic bound for list replicability

Zhang [Zha26] recently posted a manuscript announcing that the list-replicability number of every class of $M\ge 2$ binary concepts on $N\ge 2$ domain points is $O(\log^2 M\log N)$, which would be the first polylogarithmic upper bound on this quantity. In this note, we present a short, self-contained proof of this boun...

Ari Blondal, Hamed Hatami, Pooya Hatami 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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