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 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 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 194
Uncertainty Gates the Gradient: Adaptive Updates for Generalized Few-Shot 3D Segmentation
In generalized few-shot 3D point cloud segmentation, support annotations are often compromised by sparsity and occlusion.MedPAO-Assist: Edge-Deployable, Modality-Aware Agentic AI for Radiology Report Structuring
The adoption of Large Language Models for radiology report structuring has been largely dominated by either proprietary cloud-based models or large open models requiring high-end GPUs for…Steerable Pluralistic Policy Alignment from Anonymized Crowd Feedback
Pluralistic alignment requires AI systems to learn from diverse populations whose preferences may reflect heterogeneous or even conflicting objectives, and to personalize their behavior to…Enhancing analytical photon transport in turbid media through physics-guided neural network corrections
Analytical methods provide rapid and computationally efficient predictions of light transport in turbid media, making them attractive for real-time and inverse applications.DataPraxis: Scaling Analytics Tasks via Verified Workspace Evolution
General-purpose agents are increasingly expected to perform professional analytics work, which requires sustained reasoning, iterative investigation, and reliable evidence.Multilingual Brain Decoding Reveals Shared Cortical Semantic Geometry
Human languages differ markedly in lexical and syntactic form yet can convey similar semantic content.Expert-Guided Rubrics as Dense Reward Signals for Reinforcement Learning in Non-Verifiable Domains
Large language models (LLMs) are increasingly deployed as autonomous agents in non-verifiable domains such as financial services, where they must interpret user intent, rewrite queries, and retrieve…How Intervention History Changes AI Agent Sabotage on Subsequent Tasks
AI control evaluations often reset agents' memory between tasks, although deployed agents may retain records of prior interventions.GRaSP: Latency-Aware Optimization for Geo-Distributed Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) is increasingly served across geo-distributed cloud and edge infrastructures, where wide-area latency, cross-region egress cost, and data-residency rules compete…Benign Overfitting Property of the Minimum $\ell_q$ -norm Interpolant Estimator in Regression Problem via Feature Space Decomposition
This paper establishes high-probability, non-asymptotic upper bounds for the population excess risk of minimum $\ell_q$-norm interpolating estimators in linear regression, valid for all $1Between Spoilers and Guesswork: Avoiding Over-Specification in Coding Benchmarks
Benchmarks drive AI progress, but are hampered by task under-specification and over-specification.StoBasket: Joint Upsell and Cross-Sell Optimization for Contribution Margin
Retail recommenders rank relevance, but retailers earn contribution margin: an upsell earns the margin difference over the item it replaces, whereas a cross-sell earns its full margin.When Can You Trust an Agent's Own Verifier? Stress-Testing Counterfactual Rationale Verification Under Search Pressure and Oracle Access
Automated agent design increasingly trusts a single score to decide whether a change is an improvement -- but a score optimized aggressively enough ceases to measure what it was meant to.Anytime Contracts for AI Services
AI services are prone to moral hazard, as model providers can silently route requests to cheaper, lower-quality models, and the inherent stochasticity of model outputs makes this degradation hard to…Next Token or Vector? Learning How Far to Decode
Standard autoregressive decoding advances one token at a time, even when several upcoming tokens can be predicted together.Self-Evolving Coding Agents via Harness-Policy Optimization
Large language model (LLM) agents have shown promising potential in coding tasks, and their performance is jointly determined by the policy model and the harness that mediates their interaction with…Prune the Leaky Neurons: Mitigating Private Data Leakage in Fine-Tuned LLMs via Joint-Embedding-Guided Pruning
Fine-tuning large language models (LLMs) on proprietary records enables domain adaptation but introduces two challenges: (i) privacy leakage of memorized private data, such as personally…STRIDE: Evaluating Spatiotemporal Reasoning in Driving Edge Cases
In autonomous driving, perceiving surrounding objects is not enough: failing to understand how they move and interact over time can lead to dangerous decisions.Compression Precedes, Content Decides: Emergent Misalignment in Reward Models
Emergent Misalignment - the out of domain generalization of narrow harmful fine-tuning first shown by Betley et al. [2025] has been widely studied in language models but not in reward models that…World Action Critic (WAC) Model: Joint Foresight and Criticism for Policy Learning
World models have shown considerable promise in sequential decision-making by anticipating the consequences of actions and providing predictive foresight for policy learning.