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Understand new AI research with readable explanations and links to the original papers.
Beyond Fixed Representations: The Vocabulary and Verifier Gaps in Open-Ended AI Modern AI systems have become remarkably skilled at solving complex problems...
SAGEAgent: A Self-Evolving Agent for Cost-Aware Modality Acquisition in Multimodal Survival Prediction In clinical oncology, patients often undergo a series...
Concept-based explainable AI (C-XAI) aims to make complex machine learning models more transparent by using human-understandable concepts—such as clinical bi...
ProofCouncil is an AI agent designed to tackle complex, open-ended mathematical problems autonomously.
Shared Selective Persistent Memory for Agentic LLM Systems Agentic LLM systems—AI that can autonomously use tools and write code—often struggle with a "blank...
Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation Large language model (LLM) agents often struggle to solve complex problems because the...
Ceci n'est pas une pipe: AI systems as semantic abstractions This paper argues that we should stop viewing AI systems as "magic" or "oracles" that inherently...
TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems The rapid rise of agentic AI—systems capable of autonomo...
Knowledge Graphs and Explainable AI as Complementary Resources for Urban Mining This paper addresses the challenge of performing pre-demolition assessments (...
Drift-Aware Temporal Graph Rewiring (DATGR) for Adaptive Semantic Modeling in Biomedical Text Biomedical language is constantly changing as new research, dru...
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