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Artificial Intelligence
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The newest 5,000 papers, from 12 October 2026 to 6 November 2026: 4,992 arXiv preprints and 8 OpenAlex records.
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Newest papers, page 229
When Reality Outpaces Cognition: A New Scaling Principle for Reliable Intelligence
This paper challenges the understanding capabilities of existing AI paradigms and emerging world models by asking a fundamental question: How quickly can reality change before an intelligent system…INFUSE: A Unified Surface Benchmark for Indirect Prompt Injection in Agentic Systems
LLM-based agents increasingly execute long-horizon, end-to-end workflows involving shell commands, code modification, dependency installation, and interaction with remote services.Verdict: Encode Once, Decide Many
Automating organizational work means answering many small typed questions about one piece of state (does this email need a reply, how urgent is it, who owns it) with probabilities good enough to act…ARIA: Runtime Inspection and Amendment for Multi-Agent Systems
In LLM-based multi-agent systems (MAS), a local mistake can propagate as other agents build on it, turning a correctable error into a failed task.ReasonUni: Reasoning-Augmented Reconstruction Advances Unified Multimodal Models
Image-to-text (I2T) understanding and text-to-image (T2I) generation are dual tasks in unified multimodal models (UMMs).Open-world AI: a Tale of Three Requirements
The closed world asks AI for specialized competence and lets it manufacture the iid data its theory assumes: a fixed criterion licenses collecting examples until the task is covered.EvolvingBrain: An Adaptive Skill System for Multi-modal Agents Self-Evolution in Open Worlds
Multi-modal agents have made significant progress in interacting with open worlds.When Reasoning Traces Conflict with Evidence: How Answer Configuration Shapes Evidence Following
A reasoning trace retained at answer time plays two roles: it can raise accuracy, and it can compete with the current evidence when the two disagree.A Self-Improving Agent that Grows its Own DSL: Oracle-Free Operator Induction for Exact Question Answering
A self-improving agent has to decide which new skills to keep, and in practice it keeps whatever earns a high reward, convinces a judge model, or compresses its library, even when the skill is wrong.Temporal Specification Guidance for Text-to-Video Generation
Text-to-video models often generate the visual concepts requested in natural language prompts but fail to satisfy their intended ordering, timing, and persistence requirements.Wolof-Adapted Industry-Ready Open-Weight Language Models
Wolof is spoken by 23M people, mostly in Senegal, but remains weakly supported by open-weight language models, while API models raise cost, governance, and sovereignty constraints for local…CoTranslator: Reverse to Forward Chain-of-Thought Translator for Reasoning Distillation
Reasoning distillation requires chain-of-thought (CoT) data with correct answers, but obtaining such data efficiently remains challenging.Amortizing Sequential Experimental Design in Reasoning LLMs
Despite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs) struggle to adaptively gather information in experimental design tasks.Confidence-Calibrated Inference Expansion for Evaluator-Guided Test-Time Reasoning
Evaluator-guided reasoning presents a local control problem at every step: trust the current answer, return a fallback, or spend more inference.Evolving Together, Failing Apart: The Skill-Policy Co-evolution Trap in Self-Improving Agents
Skill-policy co-evolution, in which an agent's policy model and its skill bank improve each other during training, is a promising paradigm for self-improving agents.Probing Negation Understanding in Vision-Language Models With Object Attention
Negation is a fundamental aspect of language understanding and it is important vision-language models (VLMs) are able to process it effectively.SHRED: Self-Play for Heterogeneous Road-Users without Expert Demonstrations
Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints, and behaviors differ fundamentally.Anticipatory Commonsense with Moving Objects: An ASP-Based Framework for Predictive Reasoning with Interacting Trajectories
We present a general declarative framework for anticipatory reasoning about interactive motion in complex dynamic environments.Wrong Votes, Right Gradients: Why Majority-Vote Rewards Track Verifiable RL
Reinforcement learning with self-generated labels, such as majority vote over a model's own samples, often recovers most of the gain of training with verifiable rewards.TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping
Task-Oriented Grasping (TOG) has emerged as an important skill for purposeful robotic manipulation.