large language models

AI models as consciousness attributors: how LLMs ascribe consciousness to other agents

This work introduces model-generated consciousness attribution as an object of empirical operationalization and diagnosis, defining an attribution rule as the recurring relationship between features of a target and an evaluator’s ratings, without implying subjective belief, intention, or experience.

Benchmarking Large Language Models on Long-Tail Plant Taxonomic Knowledge with PTTB-600

Plant taxonomic knowledge contains a long tail of infrequently encountered names, diagnostic characters, and nomenclatural decisions, yet model reliability across this distribution remains unclear. We developed the Chinese-language PTTB-600, comprising 200 general, 300 ordinary specialized, and 100 long-tail fill-in qu...

Can Urban Planners Rely on Large Language Models? An Exploratory Evaluation of Four Frontier Models on Two Contested Greek Masterplans

Urban planners are increasingly using Large Language Models (LLMs) to draft project appraisals, raising the question of whether such systems can act as independent evaluators. Here we test this question on two contested urban regeneration projects in Greece: Hellinikon in Athens and TIF-HELEXPO in Thessaloniki. A valid...

Hierarchical Relational Ontologies of Form: A contingent theoretical framework

This body of work develops a unified relational ontology of form that reinterprets metaphysics, physics, biology, and AI as different expressions of the same underlying problem: how stable forms emerge, persist, and transform within a world constituted not by independent objects, but by relations. Beginning with the fo...

Gödel's Incompleteness Theorem and the Power of Context

Gödel's incompleteness theorem and Turing's halting problem are conventionally read as fundamental limits of formal reason. We argue the opposite: they are not limits of reason, but the cost of practicing reason context-free. Incompleteness (Gödel), the undecidability of semantic properties (Rice), the undefinability o...

LLM-RUL-CMAPSS: an evaluation protocol for large language models on turbofan RUL prediction

Large Language Models as Implicit Classifiers in Predictive Maintenance: A Diagnostic Study on Turbofan Remaining Useful Life

Towards Trust Calibration in Socially Interactive Agents: Investigating Gendered Multimodal Behaviors Generation with LLMs

As Socially Interactive Agents (SIAs) become increasingly integrated into daily life, the ability to calibrate user trust to an agent’s actual capabilities would help ensure appropriate usage of these agents. In this paper, we explore the capacity of Large Language Models (LLMs) to generate multimodal behaviors (verbal...

Large Language Models to Support Qualitative Analyses: An Extended Systematic Mapping Study

Design and Evaluation of a Culturally Adapted Multimodal Virtual Agent for PTSD Screening

Post-traumatic stress disorder (PTSD) is prevalent yet underreported among combat-exposed military personnel. We present Molhim, a culturally adapted multimodal conversational platform, and examine its PTSD screening configuration in a Saudi military healthcare context. The system pairs a system-controlled virtual agen...

Towards intelligent geospatial data discovery: a knowledge graph-driven multi-agent framework powered by large language models

The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Existing data catalogs, portals, and infrastructures rely largely on keyword-based search with limited semantic support, which often fails to ca...

The illusion of clinical reasoning: a benchmark reveals the pervasive gap in vision-language models for clinical competency

The rapid integration of foundation models into clinical practice and their use for public health inquiries necessitates a rigorous evaluation of their true clinical reasoning capabilities, which extends beyond success on narrow examinations. Current benchmarks, often based on medical licensing exams or curated vignett...

Music Recommendation with Large Language Models: Challenges, Opportunities, and Evaluation

Music Recommender Systems (MRSs) have long relied on an information retrieval framing, where progress is measured mainly through accuracy on retrieval-oriented subtasks. While effective, this reductionist paradigm struggles to address the deeper question of what makes a good recommendation. Attempts to broaden evaluati...