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: 4,998 arXiv preprints and 2 OpenAlex records.
Get the few papers worth your time in Artificial Intelligence every morning.
Follow Artificial Intelligence and ReadSearch reads its new papers each day, then brings you the few worth reading, each with its key finding in a sentence.
Newest papers, page 66
Safe Multi-Objective Reinforcement Learning: Zero Constraint Violation and Primal-Averaging Convergence
Many applications of multi-objective reinforcement learning (MORL) require policies that balance conflicting objectives while satisfying safety constraints.Retaining Repair Hypotheses for Cost-Aware Execution
Several corrections of an executable model can fit the same observations while predicting different execution outcomes.Well-Specified and Well-Tested? Assessing SWE-bench-Style Datasets with a Self-Evolving Agent
Large language models (LLMs) have shown strong performance on software engineering (SE) tasks, driving the need for high-quality datasets for reliable training and evaluation.FDSML: Formula-Driven Supervised Metric Learning with Parameter-Aware Triplet Loss
Formula-Driven Supervised Learning (FDSL) enables vision pre-training without real images, but existing methods cast pre-training as classification on discretized labels, discarding continuity in…Verifiable LLM-Guided Focal SMT Solving for Quantified Arrays
Large language models can often identify useful semantic structure in formal artifacts, but their outputs cannot be trusted as proofs or solver decisions.ATLAS-Bench: An Agentic Tool-Use Benchmark for Local Area Search
Reliable local search is essential for LLM agents to support everyday decisions and longer plans by finding places that meet users' needs at given times and locations.Timbre: Reliability-aware harmonic consensus for label-free multimodal test-time rexinforcement learning
Label-free test-time reinforcement learning adapts vision--language models using rewards derived from their own predictions.Kairos: Semantics-Aware Orchestration of Planning Engines across Classical, Numeric, Temporal and Hybrid PDDL
Planning at run time, as in robots that re-plan while they act, needs a planner that reads the PDDL its users write (durative actions, numeric resources, time windows, continuous change), answers…Sparse Fine-Tuning for Parameter-Efficient Adversarial Training
Deep neural networks achieve strong performance across many tasks but remain vulnerable to adversarial perturbations.Integrating Heterogeneous Echocardiography AI Outputs in Language Space: Learnable Late Fusion with a Supervised LLM
View- and target-specific echocardiography AI models capture useful but complementary information and may not achieve sufficient predictive performance in isolation.Soei: Structured Observability of Evidence and Intent for Multi-Agent AI Software Engineering
AI coding agents now do long, multi-step and multi-session software work with little supervision.Does Federated Training Degrade Explainability? A Comparative Analysis of Grad-CAM Faithfulness in Centralized vs. Federated Deepfake Detection
Automated detection has become the essential safeguard against realistic synthetic media, but deploying it at scale requires training on sensitive data from multiple organizations, putting privacy…Does Scaling Toward AGI Depend on What System-1 Lets System-2 Execute?
Progress toward artificial general intelligence (AGI) has largely relied on scaling a resource-intensive executive, System-2, through larger models, more data and compute, longer reasoning, and more…Integrated Feature Selection for classification and Representation Learning with Group-Lasso Siamese Networks
The Least Absolute Shrinkage and Selection Operator (Lasso) has been widely employed for feature selection in linear models and has also been extended to multi-layer perceptron (MLP) networks for…What Breaks When Facts Become Stories? The Distortions Source-Agreement Metrics Miss When Models Retell Real Events
Language models are now widely used to turn recorded facts into media for public consumption, yet evaluation of such output still assumes agreement with the source as its measure of success.Agent-Wise Test-Time Normalization for Camera-Based Cooperative Perception with Corrupted Collaborators
Intermediate-fusion cooperative perception lets a vehicle detect objects beyond its own field of view by fusing features shared by nearby agents.Compile Away Uncertainty: Self-Evolving Agent Skills via Programmatic Compilation
Self-evolving agent skills enable AI agents to accumulate and refine reusable procedural knowledge from interaction experience without updating model parameters.DEG: Diffusion-Enhanced GFlowNet for Solving Vehicle Routing Problems
Traditional neural solvers for solving vehicle routing problems (VRPs) often suffer from limited solution diversity, motivating the development of Generative Flow Network (GFlowNet)-based models.Category-Anchored Large Language Models for Next POI Recommendation
Next point-of-interest (POI) recommendation aims to predict a user's next destination from spatio-temporal check-in data, and serves as the foundation for many location-based services.Judging Before Propagation: Task-Graph Contextual Integrity for Multi-Agent Privacy
Multi-agent LLM systems increasingly rely on persistent memory and inter-agent communication.