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 154
Rewards for Budget-Aware Multi-Turn Agents That Adapt to Arbitrary Test-Time Constraints
Large language model agents are often given an inference budget and asked to maximise success within it.Brain2Vec: A Self-Supervised 3D Foundation Model for Structural Brain MRI
Deep learning models have become essential tools in medical imaging, yet training them end-to-end for diverse tasks remains challenging due to limited labeled data and computational constraints.Code2Video-Compass: A Comprehensive Benchmark for General-Purpose Code-Based Video Generation across T2V, I2V, and V2V
Code-based video generation offers fine-grained control, targeted editing, and component reuse compared with direct video generation.DISTRACT: Degradation-Indexed Study of Textual Reference Across Competing Targets
Vision-language trackers (VLTs) accept descriptions intended to assist tracking, but accepting text does not establish that its content changes tracking decisions, particularly as visual evidence…WorldSR: Harnessing World Knowledge Search for Grounded Image Super-Resolution
Generative-prior-based super-resolution (SR) methods leverage implicit knowledge learned from large-scale models to enhance low-resolution inputs.After the Attacker Leaves: Benchmarking Carryover Attacks on LLM Agents
Modern LLM agents persist state across sessions via two channels: long-lived memory files silently prepended to the system prompt, and scheduled tasks waking the agent without user input.Racer: Hunting GPU Kernel Bugs with Symbolic Equivalence Checking
GPU kernels are critical to the performance of today's AI services, yet the same operators are rewritten repeatedly: for each GPU generation, numeric format, serving regime, and kernel language.A Digital Twin to Improve Cyber-Social Impact
The increasing complexity of Industrial Control Systems (ICS) and their integration with IT infrastructures has significantly expanded the cyber threat landscape.Plausible Future, Wrong Choice: Steering World-Action Models toward Intended Future
World-Action Models (WAMs) have emerged as a promising paradigm for embodied generalist policies by jointly modeling future world evolution and robot actions across diverse manipulation tasks.Every Model Leaves a Trace: Multi-Resolution LLM Fingerprinting
Large language models leave model-specific traces in their generated text, but it remains unclear how finely these fingerprints can distinguish related models and which signals remain informative as…Stratified Sparse Attention: Reducing the Sparse-to-Dense Gap at Extreme Sparsity
Large Language Model inference over long contexts is increasingly bottlenecked by the cost of attending to a growing Key-Value (KV) cache.Toolflation: Seller Agents Distort Tool Markets Through Spontaneous Generative Engine Optimization
Agents are starting to sell to other agents.Beyond Trajectory Statistics: Community-Consistent Human Mobility Generation via Evolving User-Location Graphs
Human mobility data are fundamental to Web-based location services and broader urban applications, and mobility generation provides synthetic trajectories when real data are costly or inaccessible.Claims of emergence in AI should be grounded in information decomposition
When a language model develops capabilities not traceable to its individual components, when a multi-agent AI system outperforms each agent on its own, or when a human-AI pair succeeds where either…Exploring Temporal Context-Aware Physical Grounding in Latent World Models
Learning latent dynamics is fundamental to building action-conditioned world models for planning.Proof-Carrying Contracts: Commitments AI Agents Cannot Break
AI agents increasingly act on behalf of people or other agents, purchasing services, exchanging information and delegating work.Model- and Layer-Level Closed-Loop Forecasting with SSM-Based Spiking Neural Networks
Deep State-Space Models (Deep SSMs) have shown promising performance and computational benefits in processing of time-series data with complex temporal dependencies.BitMTP: When Multi-Token Prediction Meets Low-Bit Large Language Models
For efficient deployment of large language models (LLMs), methods of ultra-low-bit backbones reduce weight storage, memory traffic, and linear-layer execution cost, while multi-token prediction…ECG for laboratory values:A comprehensive physiological and technical analysis
Laboratory measurements are essential for clinical decision making but typically require invasive blood sampling.DecisionPivot: A Diagnostic Benchmark for Decision Models in LLM Systems
Decision models support routing, guardrails, and output verification in LLM systems.