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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: 4,993 arXiv preprints and 7 OpenAlex records.
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Newest papers, page 11
Drift-adaptive Representation Preservation for Domain-Incremental Learning
Pretrained vision models provide strong general-purpose representations, but in Domain-Incremental Learning (DIL), where the input distribution changes sequentially, adapting to new domains often…Reasoning-Trace Overflow in Code Generation: An Empirical Study of Explicit Reasoning Models
Explicit reasoning can help language models generate code, but it also introduces a practical risk: the model may spend so much of its output budget reasoning that it never produces usable code.Reconciling Privacy with Provenance for Language Model Watermarks
Watermarking has great potential for regulating the use of language models (LMs), but its deployment has been marred by controversy, as many users argue that fine-grained tracking of LM usage…Beyond Risk Cues: A Mechanism-Level Benchmark and Verifiable Debate for Scam Video Detection on the Web
Online scam videos pose significant threats to individuals and society.The Harness Is the Model: Agent Scaffolding Changes a Fixed LLM's Compliance with Banking Conduct Rules, Unevenly Across Models
Banks validate language models, but they deploy systems: a model inside a harness that decides what it sees, which tools it can call and who checks its actions.Ab Initio Accuracy Beyond the Cutoff: Distributed Message-Passing Equivariant Attention at Tens-of-Millions-of-Atom Scale
Molecular dynamics predicts molecular motion from atomic forces and trajectory integration.From Handcrafted to Generative: RLVR-Optimized Icon Generation for Alexa+
Alexa+ devices with screens display animated icons during conversations to visually reinforce what is being said.ModalSearcher: Weaving Visual Evidence into Autonomous Cross-Modal Deep Search
Static and incomplete parametric knowledge limits the performance of multimodal large language models on novel-knowledge and knowledge-intensive tasks, especially those requiring multi-hop evidence…SafeStart: Mitigating the Overlooked Safety Cost of Active Expert Reduction in MoE LLM Serving
Mixture-of-Experts (MoE) language models send each token through a few of many expert subnetworks.Grounding in the Dark: Text-Only Training for Zero-Shot Video Temporal Localization
Video-LLMs are typically trained on paired video-text data for temporal tasks, yet collecting precise temporal annotations and training on large-scale video datasets are both costly.CARM: Conformal Adaptive Risk Masking for Reliable Scientific Super-Resolution
Deep learning-based super-resolution (SR) can reconstruct fine-scale Earth-observation fields from coarse observations, but reconstruction accuracy alone does not provide a calibrated rule for…Reasoning-Aware Cross-Modal Association Unlearning in Multimodal Large Reasoning Models
Multimodal large reasoning models (MLRMs) integrate visual inputs with textual and parametric knowledge, allowing sensitive information to be accessed through cross-modal associations during…AssameseLM-E2B: An Open-Weight Assamese Language Model for Efficient Consumer-Grade Deployment
While Large Language Models (LLMs) excel in high-resource languages, their performance often degrades in low-resource languages due to tokenization inefficiencies, generative instability, and…Editing Large Language Models with Geometry-Aware Regularization
Knowledge editing has become a key technique for updating knowledge in large language models.CoSafe: Cooperative Runtime Safety Assurance for Mixed-Traffic Autonomous Driving
With the rapid development of autonomous driving systems (ADSes), real-world roads are evolving into mixed-traffic environments where autonomous vehicles (AVs) and other traffic participants (e.g…A Definition of Catastrophic Performance
A central challenge in AI is specifying objectives that align with human preferences.Rubric Curriculum RL: Exploiting the Generation-Verification Gap in Non-Verifiable Domains
Reinforcement learning with verifiable rewards (RLVR) has led to significant gains in math and code generation.HarnessFlow: MCTS-Guided Harness Optimization for LLM Agents
The performance of large language model (LLM) agents depends on both the underlying model and the harness that governs interaction with tools and the environment.Learning Noise Optimization for Reward Alignment in Few-Step Generative Models
Reward alignment for diffusion- and flow-based generative models is often formulated over stochastic trajectories, making it less suitable for few-step deterministic generators.A Systematic Benchmark for Evaluating Multimodal Personalized Reasoning of Vision-Language Models
People often make decisions by holistically reasoning over heterogeneous forms of personal information.