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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 125
Impacts of generative AI skill development on academic adaptation and learning outcomes among university students
Understanding how university students perceive the development of their generative artificial intelligence (GenAI) skills over time can clarify how GenAI use relates to learning and academic outcomes.Better Reasoning, Not Just Bigger Models: A Structure-Grounded Framework for Efficient and Reliable Inference
Scaling large language models has substantially improved linguistic fluency, yet reliable autonomous agents require more than fluent generation.Future of Time Series Anomaly Detection: From Historical Review to Unified Outlook
Time series anomaly detection (TSAD) is a foundational problem in data mining, aiming to identify observations that deviate significantly from nominal temporal patterns.Interpreting Model Reasoning through Behavioral Counterfactuals
Faithfully understanding the internal process that occurs as models form their chain of thought is an important problem with the rise in model capabilities.Large language models show systematic failures in incorporating evolving medical knowledge
Medical knowledge evolves as clinical guidelines revise recommendations, yet whether large language models (LLMs) faithfully incorporate such changes remains unclear.Asking Less, Adapting Better: Feedback-Efficient Test-Time Adaptation for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) requires agents to follow instructions and navigate to target locations.Speculative Acceptance Does Not Decay with Position: A Full-Length Diagnostic on Long Chain-of-Thought with Independent Drafts
The claim that speculative-decoding acceptance rate decays as a chain-of-thought grows longer has become a de facto industry narrative, motivating draft-model co-design and position-aware training.One-shot federated unsupervised domain adaptation with Smoothed Knowledge Distillation and teacher refinement
Federated unsupervised domain adaptation (FUDA) addresses the challenge of adapting models to an unlabeled target domain using decentralized source domains while preserving data privacy.Anticipating Social Consequences with Distributional World Models
Social world models predict how interactions develop after an action, helping agents negotiate, cooperate and mediate.A Topology-Guided GeoAI Agentic Framework for Urban Flood Screening
Open-world geographic artificial intelligence (GeoAI) agents can transform public geospatial data into accessible decision support for real-world challenges, but must balance autonomous reasoning…Group Relative Policy Optimisation Improves Multilingual Speech Recognition in Low-Resource Languages
Autoregressive encoder-decoder speech recognition models trained with teacher forcing can suffer from exposure bias, limiting their ability to recover from their own prediction errors at inference…Spectral Smoothing and Spiking Perturbations Reconfigure Capability Profiles and Selective Task Performance in Large Language Models
Singular-value spectra encode important structure in transformer feed-forward projections, yet their causal role in downstream behavior remains underexplored.Scaling Laws for Safety Probes
Safety probes trained on a model's activations are a standard tool for monitoring language models in real time.From Clinical Judgment to Checkable Rules: Clinician-Led Guardrails for Mental-Health Chatbots
Large language models (LLMs) deployed in mental-health contexts can be evaluated for safety failures, but the resulting judgments are often difficult to inspect or explain.LoRA Superposition across Pretrained Models
Low-rank adaptation (LoRA) is usually tied to the pretrained backbone on which it is learned.Disentangling Robustness and Sycophancy in Multilingual LLMs
When a multilingual LLM agrees with a user's false premise, is it being sycophantic, or does it simply not know better in that language?Governance-State Corruption in Long-Term Memory for LLM Agents: A Cross-Model Stress Test
An LLM agent can retrieve a memory correctly yet use it incorrectly after its validity state changes.ULME: Predictive Coupling for Mutually Reinforcing Embedding and Language Modeling
Text embedding and text generation are two of the most useful capabilities of modern large language models, yet they are typically provided by separate specialized models (an embedder and a…Meaning Contracts: Local Semantic Geometry in Large Language Models
This paper studies the local geometry of semantic meaning in large language models.Learning from Next-Frame Prediction: Towards Unified and Scalable Visual World Modeling
Video provides a natural source of supervision: what happens next serves as a learning signal for visual world modeling.