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Understand new AI research with readable explanations and links to the original papers.
Scaling Large Reasoning Models beyond Human Supervision: A Path toward Superintelligence explores how to continue improving Large Reasoning Models (LRMs) as...
MNIST-PRO is a new benchmark designed to evaluate how AI agents construct and maintain a "perceptual state"—a mental map of their environment—when they canno...
"Measure Before You Manage: Evaluating Agent Working Memory in Coding Agents" investigates how the semantic roles of different data objects—such as instructi...
When Does Bigger Help? A Controlled Study of LLM Scale for Ontology Learning investigates whether increasing the size of Large Language Models (LLMs) systema...
Agentic data cracking is a method designed to reduce the high computational costs of using LLM agents to reason over large, unstructured document collections...
BLOOM-WILT is an automated auditing pipeline designed to identify rare, undesirable behaviors in language models.
Wrong Prediction, Right Answer: Recovering Evidence from Collapsed LLM Sequence Scores investigates why large language models (LLMs) often fail reasoning tas...
OntoAligner-Ensemble is a modular framework designed to reconcile the predictions of diverse ontology alignment (OA) techniques.
Reconciling Process Supervision with Outcome-Based Credit in Agentic Policy Optimization introduces TASPO, a framework designed to improve how language-model...
When Robots Mishear Us: Mapping the Safety Risks of Voice-Controlled Embodied AI investigates how errors in automatic speech recognition (ASR) can compromise...
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