Eduardo Rocón

Iñaki Dellibarda Varela, R. Sendra-Arranz, Pablo Romero-Sorozabal et al.

Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and hallucinations that resist characterisation block their deployment in safety-critical domains -- a gap made legally untenable by emerging AI regulation. Existing evaluation paradigms share a common flaw: centralised judgment creates single points of failure and demands domain-specific expertise. Here we present POIROT, a protocol that repurposes a system's own agents as its diagnostic layer, leveraging the epistemic diversity already present in the architecture. Across evaluated settings, POIROT outperforms single-LLM evaluator baselines, with gains that scale with problem complexity (OR = 1.60, $p = 0.008$), agent count, and fault dimensionality, persisting under compound fault conditions. These results demonstrate that safety oversight need not be externalised: the agents executing a role carry sufficient collective intelligence to audit it. We release POIROT as an open-source library alongside BLAME, a benchmark for fault attribution in safety-critical multi-agent systems.

Rafael Sendra-Arranz, Iñaki Dellibarda Varela, Eduardo Rocon et al.

Populations of autonomous agents deployed in unknown environments (e.g. planetary or deep-sea exploration) must develop shared vocabularies to refer to entities that have no name in any human language. We propose the Neuro-Symbolic Lexical Discovery (NSLD) framework, in which a population of LLM-based agents plays a referential game over out-of-distribution visual referents, autonomously self-organising a shared alien lexicon. Each agent combines a frozen CLIP vision encoder with a private FAISS vector index and a text-only LLM. Crucially, discovered alien words are anchored to natural language via semantic proximity in the embedding space, enlarging the human vocabulary with new perceptually grounded words. Consensus is reached in simulations with populations of up to twenty agents and ten visual referents. Convergence dynamics are characterised through three analytical models achieving R^2 > 0.95, representing a first step towards pre-deployment planning in autonomous exploration missions.

Iñaki Dellibarda Varela, Pablo Romero-Sorozabal, Eduardo Rocon et al.

Earlier this year, Apple ignited controversy by publishing "The Illusion of Thinking," prompting heated debate within the AI community. Critics seized upon the findings as conclusive evidence that Large Reasoning Models (LRMs) lack genuine reasoning capabilities, branding them as mere stochastic parrots. Meanwhile, defenders-spearheaded by Lawsen et al. (2025)-fired back, condemning the experimental setup as flawed and the conclusions overstated. We clarify this debate by replicating and refining two of the original study's most contentious benchmarks: Towers of Hanoi and River Crossing. By introducing incremental stepwise prompting and agentic collaborative dialogue, we show that previously reported failures solving the Towers of Hanoi were not purely result of output constraints, but also partly a result of cognition limitations: LRMs still stumble when complexity rises moderately (around 8 disks). Moreover, the River Crossing results initially heralded as catastrophic failures turn out to hinge upon testing unsolvable configurations. Once we limit tests strictly to solvable problems-LRMs effortlessly solve large instances involving over 100 agent pairs. Our findings ultimately defy simplistic narratives: today's LRMs are stochastic, RL-tuned searchers in a discrete state space we barely understand. Real progress in symbolic, long-horizon reasoning demands mapping that terrain through fine-grained ablations like those introduced here.

Iñaki Dellibarda Varela, Pablo Romero-Sorozabal, Diego Torricelli et al.

Self-awareness - the ability to distinguish oneself from the surrounding environment - underpins intelligent, autonomous behavior. Recent advances in AI achieve human-like performance in tasks integrating multimodal information, particularly in large language models, raising interest in the embodiment capabilities of AI agents on nonhuman platforms such as robots. Here, we explore whether multimodal LLMs can develop self-awareness solely through sensorimotor experiences. By integrating a multimodal LLM into an autonomous mobile robot, we test its ability to achieve this capacity. We find that the system exhibits robust environmental awareness, self-recognition and predictive awareness, allowing it to infer its robotic nature and motion characteristics. Structural equation modeling reveals how sensory integration influences distinct dimensions of self-awareness and its coordination with past-present memory, as well as the hierarchical internal associations that drive self-identification. Ablation tests of sensory inputs identify critical modalities for each dimension, demonstrate compensatory interactions among sensors and confirm the essential role of structured and episodic memory in coherent reasoning. These findings demonstrate that, given appropriate sensory information about the world and itself, multimodal LLMs exhibit emergent self-awareness, opening the door to artificial embodied cognitive systems.