1190 stories tagged Research · page 23 of 50
Researchers developed SciLens, an AI system that can verify scientific claims by analyzing both text and images. This tool could make it easier for scientists and the public to check the accuracy of research findings.
Scientists developed a new method using Shapley values to measure how adjectives influence AI responses. Their findings reveal how small word choices can significantly impact AI performance and alignment.
Researchers developed PeerCheck, a system to enhance AI-generated academic reviews toward human-level quality. It addresses challenges in the traditional peer review process as academic submissions grow.
Researchers developed PEAR, a dynamic AI debate system that improves reliability by changing roles and reducing biases. This could make AI responses more trustworthy and consistent.
A new research paper introduces a reference architecture for 'agent skills' — reusable, externalized behavioral knowledge that LLM agents can discover, activate, and interpret at runtime. The framework formalizes how skills are bound to context and authority, interpreted by stochastic agents, and recorded as run evidence.
A new study examines how different AI reasoning strategies perform under varying conditions. The findings show that some methods hit performance limits even with more computing power. In plain English, this means AI problem-solving isn't as flexible as we thought.
A new paper finds that offline recommendations to mix AI models from different "families" for diversity may not hold up in real-time interactive settings—the very environments where multi-LLM systems are actually deployed. This discovery could change how multi-AI systems are designed.
Researchers propose a new language to clearly define how AI and humans should work together in software development. This could make teamwork between people and AI more predictable and reliable.
A new paper argues that the success of modern AI vindicates a modest form of associationism: the idea that learning is uniform, gradual, and driven by feedback. This could reshape how we think about both AI and human cognition.
Researchers developed LAGO, a system that helps AI robots plan tasks by understanding human language. This could make robots more useful in everyday settings. The framework predicts intermediate goals from language, reducing errors in planning.
GPT-5 Pro helped immunologist Derya Unutmaz solve a three-year-old puzzle about how T cells behave, specifically regarding an unusual subset of T cells. This breakthrough could accelerate research into cancer and autoimmune diseases.
Researchers created FirstPass, a dataset and AI model trained on real multi-round peer-review dialogues from Nature Communications. This could make AI evaluations of scientific papers more accurate by learning from actual editorial decision-making, not just stylistic mimicry.
A new paper proposes a roadmap for AI that learns through interaction with complex environments, mimicking natural evolution by systematically removing human priors. The authors have released an open-source infrastructure called Darwin Mobile Agent to test this idea using mobile graphical user interfaces as a practical proxy for an open-ended world.
Researchers have developed AlphaMemo, an AI system that helps financial trading agents learn from their past decisions. This could make AI-driven trading more reliable and less prone to repeating errors.
Researchers found that AI models can still make irrational decisions even when trained to align with human values. This 'rational value risk' means models may not always choose the best possible action, even if they understand what's valuable.
John Jumper, a Nobel Prize-winning scientist, is moving from Google DeepMind to rival AI company Anthropic. This is part of a larger exodus of talent from DeepMind, as several other key researchers have also left. This shift could impact the future of AI development and competition in the field.
Scientists discovered a gap in AI systems that combine logic solvers with language models. This gap can lead to incorrect answers despite using verified logic tools. Researchers propose solutions to maintain accuracy in these hybrid systems.
Researchers have identified key ways AI models fail when translating programming logic into hardware design. The findings highlight a 90.8% success rate ceiling for current models, with specific types of errors limiting their effectiveness.
A new arXiv paper proposes methods to detect when large language models hallucinate during knowledge graph reasoning. This could make AI-driven question answering, recommendations, and decision support more reliable.
Researchers found that placing questions in the right spot within AI prompts can significantly improve answers. This discovery is especially important for newer AI models that process information differently than older ones.
Scientists have found a way to make AI models reason better by using compute resources more efficiently. This could lead to faster, more accurate AI assistants and tools without needing more powerful hardware.
Scientists developed a new method for AI to better distinguish between different types of uncertainty — whether it's missing information or just inherent randomness. This could make LLMs more reliable in real-world tasks where mistakes matter.
Researchers found that selecting the most informative comparison pairs during AI training can improve results. This approach could make AI models more efficient to train without extra cost.
Researchers propose a new way for AI to understand and communicate uncertainty, which could help chatbots ask better questions. This could make AI assistants more helpful in everyday situations where information is unclear.