AI & Machine Learning · Computer Science & AI
Artificial Intelligence
The newest papers in Artificial Intelligence that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
The newest 5,000 papers, from 11 October 2026 to 5 November 2026: 4,993 arXiv preprints and 7 OpenAlex records.
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Newest papers, page 12
Learning Pseudo-Riemannian Manifolds for Heterophilic Graphs via Graph Signature
Several widely used families of Graph Neural Networks carry a positive-definite geometric prior.NextGAN: Towards Simple and Scalable GAN Training for Image and Video Generation
Adversarial training enables one- and few-step image and video generation, but conventional adversarial losses supervise critic values without directly specifying the gradients used to update the…Quo Vadis, Driving Scene Reconstruction? Occlusion-Aware Gaussian Densification
While recent feed-forward 3D Gaussian Splatting methods enable efficient driving-scene reconstruction from surround-view images, their reliance on a single observation fundamentally hinders…DyPSI: Dynamic Physics Sensing via Joint Field and Sensor-Trajectory Generation
Physics sensing in evolving environments requires reconstructing dense spatiotemporal fields from sparse observations while adapting where future observations should be collected.MonoLaDN: Language-Guided DeNoising for Monocular 3D Object Detection
Monocular 3D object detection offers a low-cost route to 3D perception, but it remains fundamentally ill-posed, since depth must be inferred from a single view.The Split Protocol Decides the Result: Re-Evaluating Encrypted Traffic Classification
Encrypted traffic classifiers are routinely reported at 95-99% accuracy.A.S.E 2.0: Understanding How Repository Context Impacts the Security of LLM-Generated Code
Large language model (LLM) coding assistants and agents construct a repository context before generating code.Multi-Resident Data Association in Smart Homes via MDP Modeling and Behavior Cloning
Ambient sensors offer a privacy-preserving solution for activity recognition in smart homes, yet they face a critical challenge in multi-resident environments known as Data Association, which aims…STEK: Structural-Terrain-Enhanced Koopman-Inspired Cross-Channel Regularization for Retrospective Battery Multi-Target Estimation
Retrospective battery characterization across heterogeneous cells is challenging because cycling records vary substantially with chemistry, protocol, cell format, capacity, and operating conditions.An Engineering Reading of the Bounded Halting Problem and Observable Properties of a Universal Machine
The halting problem asks whether a program eventually halts, with unrestricted resources.When Does Risk-Controlled Throughput Forecasting Actually Control Risk? A Reproduction Study on LEO Satellite Link
Risk-budgeted throughput forecasting converts a point forecast of LEO satellite capacity into a conservative bound whose overestimation rate is held at a user-specified budget, so that admission…Conformal Prediction for Distribution-to-Distribution Regression
Conformal Prediction (CP) provides a model-agnostic framework for constructing finite-sample prediction sets with coverage guarantees and has become a tool for reliable decision-making in high-risk…EnzymeRSI: A dual-loop recursive self-improvement system for enzyme design
Improvement in enzyme engineering is usually pursued by optimising which candidates to assay within a fixed model and workflow; when the bottleneck arises from a mismatched method or a failing…Mass Producing Training Data with Surprising Side Effects
Training data can have unexpected side effects on language model behavior outside the training domain: insecure code data can induce broad misalignment, and outdated bird names can induce a…Learning to Tune with Biased Gradients: Iterative Feedback Tuning of Model-Free Control on Throttle Valves
Iterative feedback tuning (IFT) learns the parameters of a fixed-structure controller by stochastic gradient descent.Prism: Bidirectional Self-Teaching for Unified Multimodal Models
Unified Multimodal Models (UMMs) aim to support visual understanding and generation within a single model.Today's Work, Tomorrow's Workforce: Incentivizing Learning in AI-Assisted Work
At work, people develop skills through experience: novices acquire skills by completing tasks, while experts retain theirs through continued practice.Traffic-VL: Reinforced Vision-Language Models for Generalizable End-to-End Traffic Signal Control
Existing traffic signal control methods typically rely on structured traffic states produced by dedicated perception pipelines.Corralling AI Risks with Grassroots Contracts
AI's inflection point is a fountain of alarming risks.Does Context Compression Preserve Refusal Alignment?
Context compression reduces inference cost by encoding inputs into compact representations while preserving semantic content.