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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.
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Newest papers, page 69
Spectral Invariant Fusion and Refinement for Domain Generalized Semantic Segmentation
Domain generalized semantic segmentation is challenging because it requires accurate dense prediction on unseen domains without any access to target-domain data during training.Learning Calibrated Confidence for Multi-Agent Collaboration
Complex information seeking on the Web often requires collecting evidence from multiple documents and reasoning across them, as in multi-hop question answering.When to spend privacy: optimal allocation in private learning
Privacy accounting bounds the cumulative privacy cost of Differentially Private Stochastic Gradient Descent but does not determine how to allocate a fixed budget across iterations.Mind The Gap (MTG-AD): Dataset and Fine-grained Evaluation for End-to-End Inline Audio Descriptions
Audio descriptions (AD) are essential for making visual media accessible to blind and low-vision (BLV) users.Accumulating Pruning Constraints: New Exact Polynomial-Space Algorithms for Max 2-SAT
We present deterministic polynomial-space algorithms that solve Max 2-SAT on $n$ variables exactly in time $\Ostar(2^{(1-γ)n})$, for a fixed $γ>0$, on several classes of instances whose clause…Efficient Learning of Mechanism Designs and Compositionality
Learning mechanism behaviour requires a representation of how connected parts jointly constrain motion.Human--AI Interactions as First-Class Data: A Formal Interaction Model and Runtime for Explainable AI Collaboration
AI assistants are increasingly embedded in computational notebooks, scientific workflows, software engineering tools, and learning systems, yet human-AI interactions are often treated as transient…HEMO: Harness Self-Evolution for Multi-Objective Combinatorial Optimization
Automatic operator design (AOD) has increasingly adopted large language models (LLMs) to generate problem-specific search and variation operators for multi-objective combinatorial optimization (MOCO).Cheap Retrieval and Observation Credit Assignment from One Quantity: The Value of Information
Memory in reinforcement learning raises two problems.Hybrid AI Patterns: Thirteen Ways to Couple Language Models with Rules, and How to Choose Among Them
Language models handle text well, and rule engines give conclusions with proofs.UPPAAi: Towards LLM-Assisted Modeling, Verification, Analysis and Synthesis
For more than three decades, UPPAAL has supported the modelling and analysis of real-time and cyber-physical systems.EPC-RawDC: Equivariant Physics Constrained 3D Diffusion with Raw Data Consistency for CBCT to CT Image Synthesis
Cone beam computed tomography (CBCT) is widely used for image guided and adaptive radiotherapy, but scatter, noise, reconstruction artifacts, and inaccurate Hounsfield units limit its quantitative…Rethinking Pre-pretraining
We report that allocating compute to synthetic pre-pretraining (PPT) never outperforms pure natural-language training in our compute-matched experiments.One Vector or Many? Clustered Optimal-Transport Steering under Quantization
Activation steering changes what a language model generates by editing its hidden states at inference time.Localizing Concepts in Visual Autoregressive Models
Understanding the internal routing of visual concepts is crucial for the safe and controllable adaptation of generative models.Agentic process evidence
The way software gets built is changing dramatically.Learning Safe Flow Matching Robot Policies from Binary Feasibility Oracles
Robot foundation models increasingly rely on flow-based action generation for general-purpose manipulation, yet the generated actions are not guaranteed to satisfy physical constraints such as joint…Upcycling Language Models for Multiple Specialized Domains
Effective multi-domain adaptation of large language models requires prioritizing relevant domain knowledge while minimizing interference from irrelevant domains.Reward-Adaptive Co-Evolution of Policy and Rubric
Rubric-based rewards provide fine-grained, interpretable supervision for open-ended reinforcement learning, where holistic scalar rewards struggle to capture multidimensional response quality.Escaping Path Mirages in Offline Goal-Conditioned Reinforcement Learning
Offline goal-conditioned reinforcement learning remains challenging in stochastic long-horizon settings, where compounding value estimation errors hinder reliable goal reaching.