Graph World Models: Concepts, Taxonomy, and Future Directions
Graph World Models: Concepts, Taxonomy, and Future Directions formalizes a new research paradigm in artificial intelligence.
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Graph World Models: Concepts, Taxonomy, and Future Directions formalizes a new research paradigm in artificial intelligence.
Modeling Clinical Concern Trajectories in Language Model Agents Large language model (LLM) agents used in clinical environments often struggle to communicate...
Normativity and Productivism: Ableist Intelligence? A Degrowth Analysis of AI Sign Language Translation Tools for Deaf People This paper examines the develop...
Assumption-Based Argumentation (ABA) is a powerful tool for modeling debates and reasoning under uncertainty, but it often struggles with high computational...
MM-StanceDet: Retrieval-Augmented Multi-modal Multi-agent Stance Detection Multimodal Stance Detection (MSD) is the task of determining whether a person supp...
Agent-Agnostic Evaluation of SQL Accuracy in Production Text-to-SQL Systems Text-to-SQL (T2SQL) systems allow users to query databases using natural language...
Exploring Interaction Paradigms for LLM Agents in Scientific Visualization This paper investigates how different types of artificial intelligence agents perf...
A Pattern Language for Resilient Visual Agents Integrating advanced AI models into enterprise software creates a difficult architectural trade-off.
Understanding how human health evolves and why individuals respond differently to medical interventions is a fundamental challenge in medicine.
This research introduces new techniques for "splitting" complex argumentation frameworks, specifically those that include both collective attacks and support...
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