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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 53
Robust Reward Machine Learning and Execution in Noisy Environments
Reward machines (RMs) represent non-Markovian reward functions as finite-state machines over high-level propositional events.Label Smoothing in Disguise? Gated Knowledge Distillation for Low-Resource Bangla Generation
Gated knowledge distillation (KD) weights teacher signals per token or example, aiming to trust an imperfect teacher only where it seems reliable.Ripple: Deadline-Aware CPU-GPU Sharing for Embodied Inference
Unified-memory edge devices increasingly run LLM/VLM inference alongside time-critical CPU tasks for perception and control.Auriga Technical Report: A Task-Agnostic Agent Harness for Zero-Shot Embodied Manipulation
Vision-language models can interpret instructions and plan manipulation tasks, yet struggle when acting directly as robot policies.Zero-Gated Cross-Attention
Injecting non-text modalities into frozen language models often introduces early optimization instability, making it difficult to separate architectural gains from transient training dynamics.What Does a Safety Label Measure? Auditing Situational-Safety Benchmarks for Multimodal LLMs
Situational-safety benchmarks label each scene SAFE or UNSAFE and score a multimodal large language model (MLLM) against that label, usually by judging its free-form answer, and these scores guide…HiGenTrans: Hierarchical LLM Knowledge Transfer for Generative Recommendation
Generative recommendation promises to simplify conventional multi-stage recommender systems by generating item Semantic IDs (SIDs) autoregressively.Compile as Hypothesis: A Headroom Typology for Prompt Optimization on Real Reasoning Models
Automated prompt compilers search for or rewrite prompts under the implicit assumption that optimization consistently improves downstream task performance.SIDRec: End-to-End Semantic ID Representation and Modeling for Recommendation
Semantic IDs (SIDs) have emerged as a promising approach for discriminative recommendation by encoding continuous item rep- resentations into compact discrete identifiers.World Models Need Not Be Right to Rank Policies Right: Relative Bounds and Audit Limits
Selecting a reinforcement-learning (RL) policy usually requires additional environment interaction, while learned world models offer cheap but potentially biased evaluations.EMG-Hybrid: A Dual-Engine Elite Memory--Guided Adaptive Optimization Framework for High-Dimensional Feature Selection
Feature selection in ultra-high-dimensional, noisy datasets remains a critical challenge due to exponential search space growth, where conventional binary metaheuristics often suffer from premature…CivicBench: A Benchmark and Memory Framework for Web-Based Civic Data Analytics
Municipalities publish service requests, geographic boundaries, weather observations, demographic estimates, and operational guidance through independently maintained Web portals.Capacity Planning for Resource-Constrained Clinical LLM Serving
Deploying large language models (LLMs) under a fixed accelerator budget requires jointly deciding how computational capacity is allocated across model sizes and how incoming requests are routed…Discussing politics with a chatbot: How political beliefs shape prompt formulation for generative AI
By generating comprehensive and highly personalized answers to user prompts, AI chatbots such as ChatGPT and Gemini can serve as alternatives to search engines and substantially transform online…Lightweight Representation Learning for Low Tucker Rank Tensors
Low Tucker rank tensors provide compact representations of multidimensional dependencies, but standard learning pipelines can incur substantial computational and memory overhead from redundant…Concept-Guided Prompting Strategies Increase Diversity in LLM-Generated Recipe Populations
Novelty in solution generation supports automated discovery, but LLM outputs can remain repetitive.Satisfied, Not Solving: Wireheading of Bounded Homeostatic Rewards in Reinforcement Learning
Bounded intrinsic rewards are often assumed to be safe from exploitation: if a signal can yield at most a fixed amount, gaming it should not pay.ArSciKids: Arabic Curriculum-Aligned Datasets for Scientific Experiment and Multiple-Choice Question Generation in Primary Education
Despite growing interest in Arabic natural language processing, curriculum-aligned educational resources remain scarce compared with English, and few public datasets for Arabic curriculum content…Vision-Language Models Are Joint Denoisers in Robotic Action Generation
Diffusion-based vision-language-action (VLA) models cascade pretrained vision-language models (VLMs) with diffusion action experts, but typically restrict the VLM to providing static conditioning.STLT: A Novel System for Energy-Aware Distributed Inference in the Edge-Cloud-Space Continuum
Low Earth Orbit satellites are pivotal for Earth observation tasks such as wildfire detection, capturing imagery for analysis by Deep Neural Networks (DNNs).