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The newest 5,000 papers, from 12 October 2026 to 5 November 2026: 5,000 arXiv preprints.
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Newest papers, page 204
Designing Experiments for External Decision Makers
Standard Bayesian experimental design (BED) methods assume the experimenter alone will act on the data, choosing the best action given their own beliefs and goals.ETH: Data-Balanced Heterogeneous Pipeline Parallelism for Training Large Models
Current heterogeneity-aware training systems for large models balance workloads by assigning unequal numbers of layers across pipeline stages, a strategy aimed at equalizing execution time through a…Momentum, Commentary Tone, and Earnings Surprise in LLM Financial Forecasts
Which inputs drive a language model's financial forecast?Effectiveness of Hidden-State Probes for Localizing Reasoning Failures
Verifying the final answer of a long reasoning trace may show that the trace is wrong but it does not distinguish three cases.Decoding Naturalistic Speech from fMRI with Temporal Modeling and Multimodal Alignment
Reconstructing what a person perceives from their brain activity is a stringent test of whether neural recordings carry meaningful information.Missed Detections and False Alarms in a CNN Technosignature Detector on Breakthrough Listen Data
Radio technosignature searches rely on machine learning detectors to shrink the candidate pool before a human looks at it.Rethinking Successive Rejects Algorithms
In the best-arm identification problem, an algorithm adaptively selects arms to find the arm of the largest mean reward.ABNAT: Attention-Based Noise-Aware Training for Robust Transformers on Analog Compute-in-Memory Systems
Transformer-based models have become the backbone of a wide range of tasks, but their intensive computation and data movement incur substantial energy overhead.Memory as Weights: Internalizing Long-Term History for Streaming Videos
Streaming vision-language models (VLMs) must continuously integrate information from the past while remaining responsive to current observations and user queries.The Right Byte First: How UTF-16 Helps Multilingual Boundary Prediction
Many hierarchical byte-level language models group bytes into patches using a learned boundary predictor whose view of the next codepoint depends on encoding and lookahead.Can AI Forecast the Extremes? Evaluating Probabilistic Weather Models on Tropical Cyclones
Weather prediction is undergoing an important shift, with data-driven AI models outperforming leading numerical weather prediction systems on average at a fraction of the computational cost.Learning Changes the World and Then What Was Learned has Changed: Training and Deploying Neural Networks in the Control Loop
Neural networks offer critical adaptability for controlling complex systems where nominal modeling proves intractable.Beyond the Dominant Label: Recovering Coexisting Lesion Evidence with Multiple Instance Learning
Whole-slide images may contain coexisting lesions, yet weak supervision typically provides only the most severe diagnosis.TIPS: A Tiny Inference-time Policy for Scaling in Diffusion Language Models
Masked diffusion language models expose an inference-time choice: which positions become visible after each denoising pass.Patch where the text is new: a compression view of where byte-level models should spend compute
Byte-level language models such as the Byte Latent Transformer (BLT) run their large global model once per patch and start a patch where a small model's next-byte entropy is high.Scene Reconstruction Using Ubiquitous Wireless Signals via Diffusion Posterior Inference Over physics-Grounded Representations
Knowing the layout of the physical world matters for many applications, yet the sensors we rely on for it (e.g., cameras and LiDAR) can fail in darkness, in adverse weather, or behind walls.Beyond Fixed Acquisition Functions: Temporal Difference Active Learning for Perturbation Discovery
Genetic and drug perturbation screens are important tools for elucidating disease mechanisms and validating therapeutic targets, but experimental costs limit the number of candidates that can be…Causal Integrated Gradients: Scalable Feature Attribution Along Causal Paths
Existing feature attribution methods that account for feature dependencies do not scale to high dimensions.Specification Gaming and a Constrained Reinforcement Learning Mitigation in a Cleaning Gridworld
Reinforcement learning agents optimize exactly the reward signal they are given, which can diverge sharply from an intended true objective when that signal is only a proxy for it; this failure mode…The importance of tensor handling and implementation of physics-informed DeepONets
Physics-informed neural networks (PINNs) reduce or eliminate the need for large labelled datasets by enforcing governing equations during training.