Year: 2026
Type: conference-paper
Abstract: The appearance of Large Language Models (LLMs) has transformed the field of agents with new architectures and inference mechanisms. These systems, however, struggle with capabilities such as rationality or coherent behaviour. Stand-alone LLM-based architectures are not capable of reliably performing planning to produce rational behaviour. On the other hand, traditional agents have longstanding knowledge to tackle these capabilities, but struggle with tasks requiring autonomous abstractions and knowledge abduction, making some domains unattainable: these agents do not have full reasoning capabilities due to failing at abduction. While abduction is computationally difficult, Large Language Models (LLMs) excel at producing probable hypotheses without many requirements. Moreover, verifying these hypotheses via deductive knowledge and interaction with the surroundings is within the capacity of traditional agents, and the feedback can establish a virtuous loop with abduction mechanisms. In this paper, we propose to work toward hybrid systems integrating two stand-alone LLM-based abductive modules: an Abductive Reasoner Module to generate hypotheses based on detected discrepancies between agent beliefs and environment behaviour, and an Experiment Designer Module to generate goals testing these hypotheses. These modules could be integrated with cognitive architectures using means-ends reasoning to fulfil goals (tests and system-goals). We illustrate this perspective through a practical case study in Baba Is You, a complex rule-learning environment, empirically showing that abductive-deductive separation is viable even with minimal agent design.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7167306488