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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 58
KEEPER: Learning Control-Sufficient Textual Memory for Long-Horizon LLM Agents
Recursive textual memory can carry earlier errors into later updates, yet task performance alone does not establish memory reliability.Multi-Test Nation Bias Benchmarking of LLMs with Explicit Unbiased Baselines
While prior work has highlighted that systematic nation-level bias in large language models (LLMs) can pose operational risks for international relations (IR) applications, many existing evaluations…Sparse Text Diffusion under Dual Budgets: Context Connectivity and Expert Activation
Long-context diffusion repeatedly pays for both token interaction and feed-forward computation.Specification-Guided Generation for Large Language Models
Large language models (LLMs) can generate structured outputs, but existing constrained-decoding methods mainly enforce syntax and cannot directly enforce satisfaction of a formal specification.ORION: An Outcome-Driven LLM Advisor for RL-Based Multi-Domain 6G Slice Placement
Deploying 6G network slices across administrative domains requires an online decision for each arriving request: which domain hosts each virtual network function, how traffic crosses domain…Verify--Backtrack Loop: Structured Precondition Recovery for Long-Horizon Language Agents
Language agents increasingly tackle long-horizon tasks in interactive environments, where actions often depend on latent preconditions.Connectivity-Aware Client Selection in Decentralized Federated Learning
Decentralized federated learning (DFL) has emerged as a scalable alternative to centralized federated learning, removing the single point of failure and communication bottleneck at the server.Patch-Based Deep Kernel Learning for Data Assimilation and Bias Correction with Extremely Sparse Observations
Data Assimilation (DA) reconstructs the state of a spatio-temporal system by combining sensor observations with the output of a numerical model.Real Computation or Merely Naming? A Pre-Registered Mechanism Audit of 25 Cognitive-Architecture Modules in an LLM-Based Agent System
Agent systems built on local large language models are increasingly marketed through the vocabulary of cognitive architecture: modules named global workspace, theory of mind, causal reasoning, or…VLA Policies Do Not Know When Not to Act: Unintended Task Execution and Start Suppression
A robot should act only on an instruction from a person or on an intent that the robot explicitly represents and generates.What Should LLMs Forget: Reinforcement Unlearning of Knowledge Associations
Large language models (LLMs) acquire extensive factual knowledge during pre-training, which may include sensitive, private, or copyrighted information, motivating the development of unlearning…Attention Maps Know When to Think: Training-Free Difficulty-Aware Chain-of-Thought for Reasoning Segmentation
Chain-of-Thought (CoT) prompting has become a standard reasoning strategy in Multi-modal Large Language Models (MLLMs), showing strong performance in complex vision-language tasks such as reasoning…GRIT: Gradient Masking for Visually Misgrounded Tokens in Text-Rich Reasoning
Reinforcement learning with verifiable rewards (RLVR) has recently been extended to vision-language models (VLMs), yet its effectiveness remains limited for text-rich reasoning tasks.Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning
Autonomous inspection of a target spacecraft is typically the initial step in on-orbit servicing.Risk-Sensitive Mission Planning for Multi-Target On-Orbit Inspection via Distributional Reinforcement Learning
Multi-target on-orbit servicing missions require planning tools that can adapt to evolving operational conditions while accounting for the outcome variability inherent to safety-critical spaceflight.When Steering Shifts Discourse: Identifying and Intervening on Discourse-Level Changes due to Activation Steering in LLMs
Activation steering is an inference-time technique used to control or modify the behavior, tone, or safety properties of a large language model (LLM) without retraining the model's weights or…Breaking Information Islands in Sparse Tuning via Small-World Connectivity
Sparse tuning is widely used to adapt large language models due to its parameter efficiency.Radial Kernel Attention for Global Feature Exchange in Polymer Machine Learning Force Fields
We introduce Radial Kernel Attention (RKA) for long-range modeling in polymer machine learning force fields.OT-CAL: Optimal-Transport-Guided Adaptive Sample Selection for Efficient Visual Learning
Deep neural networks for image recognition are typically trained using mini-batches, with a forward pass to compute predictions and losses followed by a more computationally expensive backward pass…When the Past Shapes Action: Understanding Embodied Memory
Recent advances in generative foundation models have expanded the capabilities of embodied AI, enabling increasingly general navigation, manipulation, and long-horizon task execution.