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Artificial Intelligence
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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 116
Multitask Interactions in Recurrent Dynamics across 100 Cognitive Tasks
The brain continually learns tasks with very different computational demands.WebViSE: Scaling Visual Design Intelligence from the Open Web for LLM Website Generation
Large language models have made remarkable strides in website development, enabling complete and interactive web experiences to be built from natural-language instructions with far less effort.What Makes a Question Curious? Measuring Model Curiosity in Human-LLM Interactions
Large language models increasingly act as partners in everyday communication.Curriculum-Driven Deep Reinforcement Learning for Autonomous Far-Range Rocket Landing
Autonomous vertical landing of reusable launch vehicles is a critical yet formidable challenge in aerospace engineering, primarily due to highly nonlinear dynamics, stringent precision require-…Network Topology Does Not Matter: The Breakdown of Classical Consensus in LLM Populations
As multi-agent language model networks become widely deployed, predicting their collective behavior is a critical challenge.MI-MIDI 2: From Observation to Control with Sparse Autoencoders in Text-to-MIDI Generation
Sparse autoencoders (SAEs) can expose features of a generative model without predefined labels, but in language models the evidence used to interpret these features has proven an unreliable guide to…CEMIR vs. FISER: a comparative study of reasoning-based and enrichment-based approaches to pragmatic intent recognition
Intent recognition in human-robot interaction requires agents to resolve ambiguous natural language instructions by inferring the speaker's underlying goals.BARC-Bench: Benign Asks, Risky Consequences
Task instructions from users for agents are inherently underspecified, especially regarding safety boundaries--creating behavioral risks.RSI-Opt: Recursive Self-Improvement Optimizer Agents For Model Training
Conventional neural-network training typically follows a predefined optimization recipe, motivating a LLM agent that monitors training dynamics and adjusts optimizer settings as training progresses…PPO vs. SAC: A Comparative Study for Cooperative Motion Control of Dual-Arm Space Robots
The growing population of orbital debris has intensified interest in active debris removal and on-orbit servicing, for which dual-arm space manipulators offer improved grasping stability and load…Rethinking Representation Geometry in Supervised Contrastive Learning under Long-Tailed Data
Supervised contrastive learning and its balanced variants have demonstrated their effectiveness for representation learning under long-tailed distributions.Do AI-for-Science Agents Preserve Model Safety?
AI-for-Science agents are increasingly being used for formulating scientific hypotheses, running autonomous experiments, and interpreting evidence.Can Test-Time Memory Evolution Outperform Test-Time Training for Scientific Discovery?
Recent \emph{test-time training} (TTT) frameworks have enabled open-weight large language models (LLMs) to outperform inference-only frameworks powered by frontier models on scientific discovery…HyScale: Scaling Long-Context Training for Learned Sparse Attention
We present HyScale, a long-context training system for learned sparse attention.ACVA-Bench: Beyond Prompt Following in Abstract Concept Visualization Ability for Text-to-Image Models
Text-to-image (T2I) models have achieved remarkable progress in image quality and instruction following, prompting growing research interest in their capacity for deep understanding that goes beyond…Concept Acupuncturist: An Efficient and Precise Concept Erasure Method for Diffusion Models via Sparse Autoencoders
Diffusion-based image generation models produce high-quality images but may generate harmful or policy-violating content, motivating machine unlearning (MU) to selectively remove target concepts…Understanding Communication Failures in Multi-Agent LLM Systems
Multi-agent LLM systems aim to improve reasoning through debate, evidence sharing, and mutual correction, but interaction can also introduce failures that final accuracy alone cannot explain.Controlled Evaluation of EEG Foundation Models: Random Initialisation, Band-Power and Validation-Selected Probes
EEG foundation models report downstream gains, yet their protocols seldom include three controls.DriveMind: Mind-Evolving Belief Tracking for Closed-Loop Autonomous Driving
Recent multimodal large language models (MLLMs) can perceive a traffic scene, reason about it, and propose a driving action within a single model.Guided Zero-Shot Anomaly Explanation for Anonymized Crowd Video Monitoring: Context, Categorization, and Spatial Localization
Despite the promising development of deep learning for video anomaly detection (VAD), explainability (XAI) of these models remains a challenge.