A field on ReadSearch
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 6
Fast and Reliable Open-Vocabulary Object Search with Absence Detection from Robot Memory
Mobile manipulators deployed in everyday human environments, such as homes for assistive care or flexible automation, often need to locate specific objects on demand from accumulated observations.Learning Multi-Agent Orchestration:Higher-Order Interactions in Policy Improvement
Learned orchestration has emerged as a promising paradigm for large language model (LLM)-based multi-agent systems, in which a central orchestrator selects agents and controls the information they…ZYFA: Speed and Accuracy of FP8 FlashAttention on AMD CDNA4
Attention is a core building block of frontier models and often dominates compute-bound prefill at long context.Degree-Equalized View-Graph Selection under Tight Pair Budgets
Fully matching all image pairs in Structure-from-Motion is quadratic in the number of images, so practical pipelines pass a budgeted subset to the final matching and reconstruction backend.Coverage Hides Churn: Auditing and Preserving Conformal Decisions as VLM Label Registries Grow
Vision-language classifiers can learn new labels from text alone, so the label lists they serve keep growing.DelegateEval: Evaluating Platforms for Personal External Service Agents
AI agents that work for an individual over time may also help that individual serve external clients.Enhancing Repository-Level Code Completion with Reinforcement Learning in Real-World Scenarios
The rapid development of large language models (LLMs) for code, which closely aligns with the practical needs of real-world software development, has sparked growing attention in repository-level…RLGPA: A Reinforcement Learning-Guided Granularity Planning Attack for Decision-Based Sparse Adversarial Examples
Decision-based sparse attacks must reduce perturbation support using only the victim's predicted label.Concertina: Embodying Manipulation and Cognition in One Diffusion Vision-Language Model
Most vision-language-action (VLA) policies decouple manipulation from cognition by bolting an external action mechanism onto a vision-language model (VLM)--either quantizing actions into discrete…Using Transformers to Generate 3-Manifold Triangulations
Using dehydration signatures as an encoding, we train GPT-2-style transformers to generate triangulations of 3-dimensional manifolds.LMGN: A Unified Policy Learning Model for Last-Mile Grasp-Navigation
Legged robots equipped with manipulators have shown significant potential in the fields of daily life and industry in complex environments.Training-Independent Validation of a Learning-Based Controller for Stunt Maneuvers
Learned controllers for vehicle maneuvers are usually validated under the conditions and objective used to train them, where they appear highly reliable, yet the parameters that change on real roads…Embodied RAG++: Retrieval-Augmented Generation From What Exists to What Works in Embodied Scenes
To serve a request in a human environment, a robot system must find an object that satisfies a functional requirement and a robot that can feasibly reach it.Graph learning-assisted logic-based Benders decomposition for distributionally robust in-station train rescheduling under disruptions
Uncertainty in resource reopening times complicates train holding and rerouting decisions when resource closures coincide with arrival delays and processing-time extensions.OptST: Optimizing Semantic Tokenization for LLM-Enhanced Recommendation
Semantic information can enrich the identifiers and structured fields used by recommender systems.Understanding Data Influence in Transportation Networks: A Physics-Constrained Influence-Function Framework
Understanding how individual training observations influence model predictions is important for developing reliable and interpretable data-driven transportation systems.Goal-Branching LaCAM: Temporary Goals as Search Branches in Dense Multi-Agent Pathfinding
LaCAM is a search-based solver for multi-agent pathfinding (MAPF) that runs a depth-first search over joint configurations, using PIBT to generate successors and lazily adding single-agent…Adaptive Experimentation Can Hide Model Error:Detection Limits and Falsification Budget
An experiment can be informative about the parameters of a working model and uninformative about whether that model is wrong.Semantics-to-Contact: A Stagewise Framework for Robust Contact-Rich Manipulation
Contact-rich manipulation requires precise control under complex contact dynamics while remaining robust to diverse visual conditions.Identification and Bounding of Joint Expectation over Potential Outcomes
The joint expectation of functions of potential outcomes $(Y_1,Y_0)$ is a fundamental quantity in causal inference, particularly when investigating the variation or heterogeneity of causal effects.
Get the few papers worth your time in Computer Science & AI every morning.
Choose your topics and ReadSearch reads the new papers in them each day, then brings you the few worth reading, each with its key finding in a sentence.