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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 45
Scythe: Semantic-Aware Rewriting for Fast,Effective Program Reduction
Automated program reduction shrinks a program into a smaller one that preserves a property of interest.Dialect Matters: A Dialect-Aware Baseline for Igala-English Machine Translation
The massive permeability of intelligent systems in every sphere of human activities have greatly enhanced clarity, ease of work, and increased general global understanding which has significantly…EmoReward: Self-Evolving Reward Calibration for Emotional Video Generation RL
Group Relative Policy Optimization (GRPO) has advanced generative model post-training in verifiable domains such as code and mathematics, but evaluating video quality, especially emotional…IDSISA: Influence-Distilled Sharded Training for Efficient Machine Unlearning
Machine unlearning removes the influence of specific training examples to comply with privacy regulations.Deliberate Latent Reasoning for Sequential Recommendation
Sequential recommenders are widely applied in real-world scenarios to model user preferences and behavior patterns based on user interactions.From Admission to Retention: Predicting Durable Offspring in Quality-Diversity Search
Automated failure discovery can be framed as a diversity problem: the objective is to expose distinct ways a system fails rather than a single critical adversarial case.H^2MARL: Hierarchical Heterogeneous Multi-agent Reinforcement Learning for Humanoid Robot Control
Coordinated whole-body control of humanoid robots remains challenging due to high-dimensional coupled dynamics and substantial physical heterogeneity across joint degrees of freedom (JDoFs).Distributed Consensus, Optimization, and Learning under Communication Constraints
Many modern engineering applications require distributed problem-solving in which multiple nodes in a network cooperate to achieve a common optimization objective.Construction Contracts for Generated Embedded Domain-Specific Language Programs: A Measurement Protocol and a Conditional Effect
A generated domain-specific program can be accepted by its host and still miss the reference construction.Spatially and Compositionally Controlled Multimodal Glioblastoma MRI Synthesis with Latent Diffusion
Glioblastoma exhibits substantial heterogeneity in tumour morphology and composition across MRI modalities, often making available clinical datasets highly unbalanced in terms of patient pathologies.EvoOPD: Evolving Privileged Information Strategies for Black-Box On-Policy Distillation
Proprietary language models exhibit strong reasoning and coding capabilities, but their output distributions are inaccessible, making fine-grained distillation methods that rely on teacher logits…Using Experience Twice: Dynamic Process Rubrics for Privileged Token-Level Credit Assignment
Agent trajectories often contain both successful decisions and failed attempts, yet optimization with sparse trajectory-level rewards struggles to identify which critical decisions should be…MKD2World: A Typed Spatial MarkDown World Model For Web Agents
Graphical user interface (GUI) agents can improve decision-making with world-model foresight, provided that predicted states accurately reflect post-action interfaces.When Do Summaries Become Instructions? Unsupported Policy in LLM Agents' Self-Written Handoff Summaries
Long-running LLM agents compact their context, replacing most history with a summary.Adapting Sleep Foundation Models for Dense Sleep Spindle Localization
Adapting sleep foundation models to dense electroencephalography (EEG) event localization is challenging because multi-second representations may not preserve sufficient temporal detail for precise…Who Gets Counted? Large Language Models, Informative Refusal, and Downstream Bias in Name-Based Demographic Classification
Researchers routinely infer gender, race, and ethnicity from names, yet the standard tools used for this task misclassify the groups that diversity research most needs to measure.Addition by Subtraction: Toward Mitigating Fragile Safety Alignment in Large Language Models
Large language models (LLMs) undergo safety alignment to prevent harmful generations, yet they remain vulnerable to jailbreak attacks.RAGAM-AI: A Hybrid Reinforcement Learning based Agentic approach powered by Generative AI for Edge Resource Management
Resource management for distributed edge networks hosting different services has been handled efficiently using Reinforcement learning (RL).LungMend: A Healthy-Only Latent Diffusion Shape Prior for Lung Mask Correction in Severe Tuberculosis CT
Lung segmentation models such as R231 are widely used, but they fail on severe tuberculosis (TB) CT: cavities and consolidations look like non-lung tissue and are left out of the mask.From Fixed Defaults to Calibrated Activation Policies for Long-Term Time-Series Forecasting
Long-term time-series forecasting supports decisions in energy, transportation, and other applications where reliable predictions are needed beyond the observed data.
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