3 papers
cs.AI2026
Explaining is Harder Than Predicting Alone: Evaluating Concept-based Explanations of MLLMs as ICL Visual Classifiers
Carmen Quiles-RamÃrez, Leticia L. RodrÃguez, Nicolás Martorell +1
In-context learning (ICL) enables multimodal large language models (MLLMs) to classify images from a few labelled examples. Yet, how these models use the provided context remains o…
cs.AI2026
Quantitative Introspection in Language Models: Tracking Emotive States Across Conversation
Nicolas Martorell, Bruno Bianchi
Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited. Linear pro…
cs.AI2025
From Text to Space: Mapping Abstract Spatial Models in LLMs during a Grid-World Navigation Task
Nicolas Martorell
Understanding how large language models (LLMs) represent and reason about spatial information is crucial for building robust agentic systems that can navigate real and simulated en…