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The 2026 AI Index Report
Stanford HAI describes the 2026 AI Index as a globally sourced report covering research and development, technical performance, responsible AI, the economy, science, medicine, education, policy and governance, and…
Your ‘For You’ Algorithm Disagrees With You
Stanford HAI reports a new Stanford-led study examining whether X's For You algorithm mistakes outrage for interest and consequently fills a user's feed with such content.
A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries
The paper introduces a framework to help healthcare chatbots better understand underspecified patient queries by using a knowledge graph to generate plausible hypotheses and asking targeted follow-up questions. The…
A knowledge-guided agentic framework for mitigating patient-context ambiguity in health queries
The paper introduces a framework to improve the performance of healthcare chatbots by reducing patient-context ambiguity in queries. The framework uses a knowledge graph to interpret queries, create plausible…
A three-dimensional typology of agency for advanced AI systems
The text presents a three-dimensional typology for understanding the agency of advanced artificial intelligence (AI) systems, focusing on non-moral forms of agency. The typology consists of three dimensions: nature…
A three-dimensional typology of agency for advanced AI systems
The paper proposes a three-dimensional typology for understanding different forms of agency in advanced AI systems, beyond just moral or legal considerations. It considers individual and collective modes of agency, and…
AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement
The paper presents AI4AI-Bench, a benchmark for evaluating Large Language Model (LLM) agents in recursive self-improvement. The goal is to determine if an AI system can improve the process that produces future AI…
AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvement
AI4AI-Bench is a new benchmark for assessing the ability of AI agents to design their own training algorithms. The benchmark consists of 10 frozen research repositories covering 10 different algorithm families. In each…
An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction
The study presents a three-stage approach for combining digital data collection, predictive modeling, and weather-sensitive demand forecasting. It uses a chatbot to collect travel mode choices from student commuters…
An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction
The study presents a three-step approach for integrating data collection, processing, and predictive modeling in travel behavior research using conversational surveys, machine learning, and large language models…
An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction
This research proposes a three-step workflow integrating chatbot-administered surveys, data processing, and behavioral prediction for travel behavior modeling and weather-sensitive demand forecasting. The study uses…
An Inclusive and Lightweight Approach to Federated Continual Learning for Cultural Heritage
The authors propose a lightweight approach to Federated Continual Learning (FCL) called FedCurv-DR for supporting artificial intelligence in cultural heritage. This method is designed to learn from distributed and…
Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents
This research study investigates how large language model (LLM) agents can transfer skills they have learned from completed tasks to perform better in new tasks. The authors compare two methods of inducing skills…
Break It Down, Pass It On: Cross-Task Skill Transfer in LLM Agents
Researchers studied how large language model (LLM) agents can learn and apply skills from completed tasks to perform better on future tasks. They found that inducing skills at the subtask level instead of at the task…
Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis
The paper introduces Brain Researcher, a platform that aims to improve the accuracy and reliability of AI agents in scientific analyses, particularly in neuroimaging research. It enhances the selection of analysis…
Catching the Rug: Early Prediction of Fraudulent Memecoins on Solana via Machine Learning
This research focuses on predicting rug pulls, a form of fraud in memecoins traded on the Solana blockchain, using machine learning models. The study assembles a dataset of over 6 million tokens traded over 7 months…
ContractScrub: A benchmark for final review of legal contracts
Researchers introduce ContractScrub, a new benchmark to evaluate the performance of language models in contract scrubbing, which is the final review of legal contracts for errors and inconsistencies. The study finds…
ContractScrub: A benchmark for final review of legal contracts
The paper introduces ContractScrub, a new benchmark for evaluating the performance of Language Models (LLMs) in reviewing legal contracts for errors. The study finds that while LLMs perform well on general benchmarks…
Daedalus-150M: A Convolution-Attention Hybrid Designed for CPU Inference
A research paper titled 'Daedalus-150M: A Convolution-Attention Hybrid Designed for CPU Inference' proposes a new method for creating small language models. Instead of adapting large models to CPUs, the authors…
Designing Human-mediated AI Guidance: Ready Together for Personalized Family Emergency Preparedness
The research introduces a framework for Human-mediated AI Guidance, focusing on a specific AI-supported system called Ready Together, which helps parents mediate emergency preparedness information for their children…
EchoCoT: Extracting Hidden Chain-of-Thought from Large Reasoning Models
Researchers developed EchoCoT, a method that extracts hidden chain-of-thought (CoT) traces from large reasoning models (LRMs), which are valuable but often inaccessible assets. They found that EchoCoT can successfully…
Electronic Navigational Chart Change Classification
The authors propose an automated method to classify changes in Electronic Navigational Charts (ENCs), which are used in maritime navigation systems. The proposed approach involves encoding complex vector data changes…
EnvHarness: Awakening Static Worlds for Agent Learning
The paper proposes a new system called Environment Harness (EnvHarness) to improve static environments used in learning by AI agents. The system can adapt the behavior of these environments without modifying their…
EnvHarness: Awakening Static Worlds for Agent Learning
The authors propose a method called Environment Harness (EnvHarness) to address the issue of static environments in learning agents. This system allows for reshaping behavior without modifying the underlying logic…
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