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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 12
Dual-Arm Tabletop Rearrangement with Movable Stacking and Gap-Aware Push Transfers
Object rearrangement in cluttered tabletop scenes is challenging, especially in wide workspaces where dual-arm systems improve coverage and efficiency.Seeing Beyond the Next Step: World-Model-Guided Human-Like Navigation in Multi-Agent Scenes
Selecting a next motion in a populated scene is not just a matter of avoiding what is currently in the way: an action can be locally feasible yet drift onto the road instead of the crosswalk, cut…O$^2$PD: Overlap-Gated On-Policy Distillation on Reliable Prefix
On-policy distillation (OPD) supplies dense token-level supervision by aligning a student to the teacher's next-token distribution on its own generated rollouts.Over-the-Scene Computation: Illumination-Programmed Functional Sensing of Unresolvable Populations
Decisions about dense populations depend on how much will cross a boundary by a deadline and when the first member arrives.Evaluating Token Probabilities as Confidence Estimators in Human Behavior Simulations: A Case Study in Luxembourgish Next Like Prediction
As large language models (LLMs) are increasingly used to simulate human behavior, their operational validity is typically assessed through accuracy alone, implicitly treating human behavior as…Extending Reasoning in Typed Decision Models via Looping
Typed decision models choose among a fixed set of answer options in a single forward pass, so unlike generative language models they cannot spend more test-time compute on hard inputs by generating…Fix One, Miss Another: Leveraging Historical Bug Fixes to Uncover Residual Bugs in Database Systems
Database management systems (DBMSs) implement SQL constructs through grammar-driven dispatch handlers.Comparing Classification and Feature Learning by Analyzing Their Loss Functions in Normalized Spaces with Different Scale Factors
Classification and feature learning are two closely interrelated fundamental tasks.Ordering Multivariate Time Series by Volatility Memory: A Temporal Convolutional Approach to Volatility Heredity
Volatility dynamics can carry information about temporal relationships that is not apparent in the conditional mean.Wedge-Jamming Mechanism for String Configuration
Variable stiffness mechanisms that achieve both flexibility and rigidity are important technologies for robotic systems.ConvPE: Convolutional Position Embedding Improves Transformers
ConvPE (Convolutional Position Embedding) improves both the performance and efficiency of Transformer models in language modeling, long-context extension, and vision.Foresee to Trade: Forecast-Augmented Policy Optimization for Portfolio Management
Online investment services must translate asset opportunities into investment outcomes for users' accounts, yet the same market opportunity can warrant different account actions.Neural Collapse on Imbalanced Datasets and Open-Set Scenario
Neural collapse (NC) describes the elegant geometric structure that sample features and classifier vectors exhibit when a classification model's loss reaches its minimum.Uncovering Systematic Blind Spots in Graph Database Testing through Integration and Process-Level Analysis
Graph databases are becoming foundational infrastructure for connected data, and a growing body of automated testing techniques has uncovered many bugs across mature graph database systems.Automated Video-to-QA Pipeline: End-to-End Question Generation and Verification Using Large Language Models
The exponential growth of educational and informational video content on platforms like YouTube presents a challenge for efficient knowledge retrieval and assessment.Scaling Laws for SAE Training Data
Activation storage is often a bottleneck in sparse autoencoder (SAE) training.RIVER: Evidence-guided multi-agent retrieval for enzyme--reaction discovery
Enzyme function annotation and the identification of candidate enzymes for a given reaction are essential steps in connecting genomic information to biochemical reaction networks.ReConMem: A Graph-Based Dual Memory System for Repository-Oriented Conversations
LLM-based code assistants are increasingly used in iterative repository-level development, where effective assistance requires jointly leveraging evolving conversational history and repository…Fast Unsupervised Anomaly Detection Methods on Software Logs - An Empirical Study
Anomaly detection on software logs ensures system reliability through failure identification, root cause analysis and fault localization.BiGrounder: Bidirectional Training Unlocks the Potential of GUI Grounding
Graphical User Interface (GUI) grounding aims to map natural language instructions to precise operational coordinates, serving as a critical task for autonomous agent interaction.
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