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2,348 notes, page 19 of 98
Most biomedical publications show signs of LLM-assisted writing
A study suggests that by the end of 2025, approximately 89% of biomedical papers on PubMed Central showed signs of LLM-assisted writing, with a higher usage in the Discussion section compared to the Methods section.
Most biomedical publications show signs of LLM-assisted writing
A study suggests that by the end of 2025, 89% of biomedical papers on PubMed Central showed an excess of language associated with LLM-assisted writing. The authors argue that this could impact future guidelines and…
Multiclass Sentiment Analysis for Identifying Political Viewpoints
Researchers developed two machine-learning models to analyze political viewpoints from social media posts. The XGBoost model achieved an F1-score of 0.2835, and the BERT-based model achieved an F1-score of 0.2806 in a…
MVTrack: Ultrafast Appearance-Free Moving Object Tracking from Compressed Bitstreams
The researchers present MVTrack, a fast moving object tracker that works directly on H.264 bitstreams without needing to reconstruct pixels. It outperforms YOLO26n in terms of parameters, FLOPs, and CPU latency when…
Policy Convergence and Divergence Across National and Within Regional AI Strategies: A Policy Design Element Analysis
The paper analyzes national and regional AI strategies of various countries, finding strong horizontal convergence in economic competitiveness, research support, and ethical AI use. However, there is persistent…
Quantum Coordination Advantages in AI State-Tracking Tasks: Semantic Compilation and Latent Memory
The paper presents a proof of quantum coordination advantages in AI state-tracking tasks, specifically in tasks involving semantic compilation and latent memory. It demonstrates that quantum computers can compress…
R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video
Researchers present R4DSG, a relative 4D scene graph memory for long egocentric videos that helps answer object-centric questions more effectively. Unlike previous methods, R4DSG preserves persistent object identity…
R4DSG: Relative 4D Scene Graph Memory for Object-Centric Question Answering in Long Egocentric Video
The paper introduces R4DSG, a method to create a compact and queryable memory for long egocentric video, which is useful for wearable AI assistants. It focuses on preserving persistent object identity and structured…
ReLTEx: Reliable LLM-based Taxonomy Expansion
The paper discusses a new framework called ReLTEx for improving the reliability of Large Language Model (LLM)-based taxonomy expansion. The framework combines LLM-driven candidate generation with structure-aware…
Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
The paper presents a new method called 'Rule of Thumb' (RoT) for explaining how Artificial Intelligence systems make decisions. RoT identifies the most relevant features of an AI system to predict its behavior for a…
Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information
Researchers propose a new approach called 'Rule of Thumb' (RoT) explanations for Explainable Artificial Intelligence (XAI). This method identifies the most relevant features that influence an AI system's behavior for a…
SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation
Researchers introduce Visual Skill Cards (VSCs), a new method for improving the decision-making abilities of computer agents in GUI environments. VSCs are memory representations that bind reusable procedures with…
SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation
The paper introduces Visual Skill Cards (VSCs), a memory representation that helps computer agents make decisions by binding reusable procedures with applicability cues, visual evidence, and verification signals. The…
SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure
SkillZip is an evaluation-free method that helps compress skills by identifying their shortest faithful structural explanations, making it easier to reuse common action sequences without needing to repeat them. This…
SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure
The paper presents SkillZip, an evaluation-free method for compressing skills used by self-evolving agents. SkillZip aims to make skill compression more efficient by finding the shortest faithful structural explanation…
SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure
The paper presents SkillZip, an evaluation-free method for compressing skills by finding their shortest faithful structural explanations. It aims to reduce the repetition and expense of injecting and maintaining skills…
sLTN: Structural Logic Tensor Networks
The paper introduces sLTN, an extension of Logic Tensor Networks (LTN), which is a neurosymbolic framework that combines first-order logic with tensor operations for differentiable learning. The new version can handle…
Smart Enough to Go Extinct? An Evolutionary Challenge to the Value of General Intelligence and Its Ethical Implications for AGI
This research paper questions the inherent value of general artificial intelligence (AGI) by comparing its adaptive value to that of simpler life forms like cyanobacteria and horseshoe crabs. The author argues that the…
Surgical WAM: A World-Action Model for Data-Efficient Surgical Robot Learning
The study introduces Surgical WAM, a model that uses unlabeled surgical videos for pre-training and then fine-tunes on a fixed number of labeled demonstrations. The goal is to improve the success rate in closed-loop…
Test-Time Self-Evolving GUI Visual Grounding via Reflection-Guided On-Policy Self-Distillation
The authors propose a new framework called Test-Time Self-Evolving for GUI (Graphical User Interface) Visual Grounding. This method allows models to improve after deployment without the need for human-annotated ground…
Test-Time Self-Evolving GUI Visual Grounding via Reflection-Guided On-Policy Self-Distillation
The paper proposes a Test-Time Self-Evolving framework that allows GUI agents to adapt and improve after deployment without requiring human-annotated ground truth data. The framework includes four components…
The GenAI Catch-22: Use of Generative Artificial Intelligence in Norwegian Newsrooms During the 2025 Parliamentary Election
The study examines the use of Generative Artificial Intelligence (GenAI) in Norwegian newsrooms during the 2025 parliamentary election and identifies potential risks and challenges associated with it.
Tree-of-Ideas: Automated Research Ideation via Cross-Trajectory Reasoning over Scholarly Evolution
The paper introduces Tree-of-Ideas (ToI), a framework to help with research ideation by tracking and analyzing how research problems and solutions evolve across different scholarly trajectories. ToI outperforms…
Two-stage Odd Residual Flows for Mean-Preserving Probabilistic Time Series Forecasting
Researchers propose a new method called Two-stage Odd Residual Flows (TORF) for probabilistic time series forecasting. The method aims to address the trade-off between distributional flexibility and accurate mean…
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