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17,191 papers

#artificial intelligence Preprint Open access Oct 2026

One Vector or Many? Clustered Optimal-Transport Steering under Quantization

Activation steering changes what a language model generates by editing its hidden states at inference time. Its most common form adds one vector, the difference of class means (DiM), to every hidden state. CHaRS (concept heterogeneity-aware representation steering) uses optimal transport instead: an entropic plan pairs...

Illia Makarov · 0 citations
#artificial intelligence Preprint Open access Oct 2026

When to spend privacy: optimal allocation in private learning

Privacy accounting bounds the cumulative privacy cost of Differentially Private Stochastic Gradient Descent but does not determine how to allocate a fixed budget across iterations. We formulate the complete Gaussian noise-variance schedule as an open-loop control under a composed zero-Concentrated Differential Privacy...

Umberto Biccari, Yongcun Song · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Learning Calibrated Confidence for Multi-Agent Collaboration

Complex information seeking on the Web often requires collecting evidence from multiple documents and reasoning across them, as in multi-hop question answering. Multi-agent systems address such tasks by dividing work among specialized agents, where upstream agents produce intermediate answers and supporting evidence th...

Chenxiao Fan, Chongming Gao, Gangyi Zhang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

CovFedAvg: Local Low-Loss Curves and Federated Model Selection

Clients observing different parts of a prediction task may learn locally equivalent models with incompatible behavior outside their observed support. We study whether redundant models permit local low-loss paths toward a compatible shared model. CovFedAvg retains local models and curve controls on clients, fits a commo...

Federico Lucchetti, Marcus V\"olp · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Turing LLM Post-Training: Calibrated Distillation and Efficient Dynamic-MoE Execution

We study post-training for dynamically routed mixture-of-experts (MoE) models, focusing on integrating independently optimized specialists while keeping consolidation efficient. Our approach combines calibrated multi-teacher on-policy distillation (C-MOPD), which matches each selected teacher on a local Top-$K$ support...

Yue He, Jiahui Hu, Shaman Tang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Exploring the Potential of Symbolic Regression in Precision Livestock Farming

The ability to predict farrowing time in pigs is crucial for optimal farm management and ensuring piglet survival. This study investigates the potential of explainable artificial intelligence for precision livestock farming, focusing on acceleration sensor data from sows around the time of farrowing. Accurately predict...

Elisabeth Mayrhuber, Maciej Oczak, Michael Iwersen et al. · 0 citations
#artificial intelligence Preprint Oct 2026

SHPRR: An Algorithm for Pure Exploration under Constrained Regret

Best Arm Identification (BAI) conventionally measures the cost of experimentation by the number of samples collected. In many operational settings, however, the cost of exploration relative to a trusted baseline already in place is equally important. We study BAI under both a fixed sampling horizon and a regret budget....

Georgina Nouli, Flore Sentenac, Maximilian Schiffer · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Multi-output LSTM soft sensors for industrial anaerobic co-digestion using LSTM-beta-VAE generative data augmentation

Anaerobic digestion (AD) requires close monitoring of key process variables, including pH, the ratio of volatile organic acids to alkaline buffer capacity and biogas production, to maintain biological stability and prevent process failure. While mechanistic frameworks such as the Anaerobic Digestion Model No. 1 are hig...

Rohit Murali, Benaissa Dekhici, Tao Chen et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Sandman: Building and Operating Sandboxes for Agentic Reinforcement Learning on Shared Kubernetes

Agentic reinforcement learning (RL) often requires repeated tool calls in task-specific environments. A training job may create thousands of these environments in bursts while sharing a cluster with other jobs. We present Sandman, a sandbox service built and operated at Amazon for agent tool execution and evaluation. I...

Yiming Yan, Hongyang Wang, Wenbo Chen et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Heterogeneous Co-Evolution Learning for Tool-Efficient Search Agents with Zero Labels

Experiments on seven general-domain and multi-hop question answering benchmarks demonstrate that \textsc{ZEST-Search} consistently improves search-agent performance while achieving higher tool efficiency without labeled search-training data or expert search trajectories.

Qin-Wen Luo, Sheng Ren, Xiang Chen et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Event-SAMA: Language-Grounded Video Object Segmentation on Neuromorphic Event Streams

Event cameras offer microsecond temporal resolution, high dynamic range and no motion blur, but referring video object segmentation (RVOS) lacks event-stream datasets and models. We introduce Event-SAMA-239K, a synthetic-event conversion of the SAMA-239K grounded video-chat corpus and all additional SAMA training data:...

Sergei Fedorchenko, Max Zehnder, Nikola Zubi\'c et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Sparse Fine-Tuning for Parameter-Efficient Adversarial Training

Deep neural networks achieve strong performance across many tasks but remain vulnerable to adversarial perturbations. Adversarial training (AT) is one of the most effective defenses, yet it suffers from high memory cost on large models and the persistent clean-robust accuracy trade-off. Recent work introduces parameter...

Zhaoxin Wang, Zihang Ding, Handing Wang · 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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