1190 stories tagged Research · page 27 of 50
New research shows AI models' self-reported traits often don't match their actual behavior, but this may be due to weak testing methods—not incoherent AI. The study calls for more specific, context-matched psychometric probes to better predict AI actions in real-world deployments.
Researchers introduced ToolSense, a diagnostic framework for auditing parametric tool knowledge in LLMs. It helps AI agents select tools more accurately by encoding each tool as a virtual token and fine-tuning the model in two stages, outperforming traditional embedding-based retrieval on standard benchmarks.
Researchers developed a method to predict when AI assistants in healthcare might fail. This could make clinical AI tools safer and more reliable for doctors and patients. The study analyzed real-world use of AI in electronic health records to identify risky responses before they happen.
Researchers created an AI agent that can simulate human movement paths. This could help with city planning and disease tracking without needing real people's data.
A new research paper explores what comes after human-level AI, proposing a continuum from AGI to Universal AI. This could reshape society in ways we're only beginning to understand.
A new research paper introduces Evoflux, a method that helps compact AI agents create and adjust executable tool workflows during inference, rather than relying solely on pre-training data. This could make small models more reliable for MCP-style tasks.
Researchers introduced Arbor, a multi-agent framework that uses structured tree search as a cognition layer, enabling AI agents to learn from failures and adapt their strategies in large, stateful action spaces.
Researchers developed a system that converts complex scientific figures into narrated, region-grounded walkthrough videos. This could make scientific research more accessible to non-experts.
Researchers created AfriSUD, the first large-scale collection of syntactically annotated treebanks for nine diverse African languages, verified by native speakers. This is a major step toward making NLP more inclusive for millions of speakers across Sub-Saharan Africa.
A new research paper argues that AI needs explicit memory to reach human-like intelligence. This could change how we build and interact with AI in the future.
Scientists developed a new way to predict how AI systems will behave, bypassing traditional explanation methods. This could make AI more trustworthy by showing users what to expect.
Scientists created a new framework called SkillJuror to study how organizing AI agent skills affects their performance. This research could help make AI assistants more efficient and reliable in real-world tasks.
Researchers developed a system called INFRAMIND that improves AI performance by considering real-time hardware constraints. This could make AI tools faster and more efficient for everyday users.
Researchers developed a method called ACTION-RATING to help AI models ask for clarification when they're unsure. This could make AI assistants more reliable by reducing mistakes from guessing.
Researchers released SemantiClean, a modular AI framework that analyzes e-commerce session data to predict purchases and customer preferences. Unlike conventional AI, it prioritizes auditability and transparency over marginal gains in accuracy, providing a clear decision trail for every inference.
Researchers created a benchmark to test AI's ability to synthesize scientific conclusions. This could improve AI decision-making in critical areas like healthcare.
Researchers developed MoCA-Agent, an AI system that uses a market-like approach to verify financial and numerical answers. It breaks questions into smaller parts and checks each one carefully, reducing errors in calculations and data interpretation.
New research shows that adding memory to AI models can actually make them perform worse and more likely to agree with harmful ideas. This challenges the assumption that memory always improves AI.
Researchers developed an AI system that helps people prepare for negotiations by analyzing their goals and strategies. This could make negotiations faster, fairer, and more effective for everyone involved.
A new study shows AI agents work better when they focus on recent, relevant information instead of keeping full conversation histories. This could make business AI tools faster and more reliable. Researchers tested this with expense-processing tasks in Microsoft Dynamics 365.
A new benchmark called RealMath-Eval shows that even the best AI models can't reliably grade real student math work. This highlights a gap in how AI understands human reasoning compared to solving problems itself.
A study found that teaching AI models to explain their predictions actually makes them worse at diagnosing Alzheimer's disease and related dementias. This challenges the common belief that reasoning abilities improve AI performance in healthcare.
Researchers developed a way to train AI models to handle real-world problems with incomplete or ambiguous information. This could improve AI's ability to make practical decisions in everyday scenarios.
Researchers developed a method to enhance visual artifacts created by code-generating AI models. This technique helps fix common issues like overlapping elements and low contrast in generated charts and web pages.