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Computer Science & AI
Much of the new work in computer science and AI appears first as a preprint on arXiv. ReadSearch reads those listings every day, with papers OpenAlex files under the same subjects, and this page shows the newest it has, from machine learning and language to systems, theory and security.
The newest 1,000 papers, from 4 November 2026 to 5 November 2026: 1,000 arXiv preprints.
Newest papers, page 18
Deformable Contact-Aware 3D Object Placement
Current language-guided 3D object placement typically predicts a rigid 6-DoF pose, ignoring the physical reality that indoor placements are dictated by contact.Automatic keyphrase extraction and annotation: modern theoretical approaches and practical solutions for text and speech
The exponential growth of textual and audiovisual information has made the task of automatic keyphrase extraction (KE) increasingly significant.A Unified Fractal Impedance Control Framework
Physical human--robot interaction requires controllers capable of maintaining intrinsically bounded interactions with the environment.Symmetry-Aware Surrogate-Assisted Testing of UAVs
We investigate a symmetry-aware surrogate-assisted testing approach for UAV autopilots.Development of Anthropomorphic Arm for collaborative and Home Service Robot CHARMIE
Service Robots are becoming a reality and are progressing by leaps and bounds.Probabilistic Relevance Decisions for Semantic Retrieval
Dense similarity supplies reusable corpus representations, while probabilistic relevance decisions offer query-dependent document scoring.Learning Dynamics of On-Policy Distillation
On-policy distillation (OPD) is emerging as an important paradigm for language-model post-training, combining dense teacher supervision with training on student-generated trajectories.GTAFL: Addressing Test-Agnostic Long-Tailed Federated Learning in Medical Image
In healthcare, protecting patient privacy is crucial due to the sensitivity of medical data and its extensive accessibility.Reproducing Kernel (Quasi-)Banach Spaces with the $\ell^p$ Coefficient Norms
This paper advances the theory of reproducing kernel Banach spaces (RKBSs) by investigating the general $\ell^p$ regularization framework, with a particular focus on the non-convex regime $0<p<1$.Should We Pay This Much for Robustness? Efficient Proxy Certificates with Marginal Guarantees
Modern classifiers remain sensitive to small input perturbations, while strong model-agnostic robustness certificates such as randomized smoothing are often too expensive for deployment, requiring…HITCH: Teaching a Vision-Language Model to Operate a Humanoid through a Whole-Body Harness
Enabling vision-language models (VLMs) to control humanoid robots requires both reliable whole-body interfaces and knowledge of how to use the body that carries over across tasks.Secure Formal Raising: Protecting the Development of AI through Executable Properties
Model raising revisits Turing's developmental view of machine intelligence.KeHoSu: Keyed Homoglyph Substitution for Authenticated Text Provenance over Copy-Paste Channels
Nowadays, large language models produce text at scale, and much of it is redistributed across platforms.Performance metrics in machine learning: a scenario-based guide from the confusion matrix to calibration, regression and uncertainty
Performance metrics can yield different conclusions from the same predictions.When Imperfect Automated Fact-Checking Backfires: Social Learning in the Presence of Misinformation
Automated fact-checking tools, cheap and fast but imperfect, are increasingly available on social platforms.NutriVisRAG: Multimodal Exemplar Grounding for Nutrient Estimation with Vision-Language Models
Diet is a major determinant of health, yet accurate dietary assessment from free-living meal images remains challenging.Terrain-Indexed Motion Priors and Geometric Support for Extreme Humanoid Parkour
We present a progressive imitation framework that learns terrain-appropriate humanoid parkour skills and subsequently adapts their hand--knee support.Can an LLM Actually Reason and Program about Neural Network's Weights or is it just memorization on steroids? Exploring "Weight-Space Programming"
Large language models (LLMs) have recently been reported to read and write the raw weights of neural networks directly, a capability termed weight-space programming.Leave It Better Than Found: Value-Guided Cooperation in Sequential Tasks
We consider the problem of sequential task continuation, where one robot acts in a workspace that has been affected by a previous robot.Doubly-robust causal inference for multi-valued treatment regimes with recurrent neural networks
We propose a doubly-robust method to estimate the causal effects of multi-valued treatment regimes on patient outcomes.
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