Hugging Face Trending Papers
The paper introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems, describing how the system learns to maintain homeostasis via motivated learning—a reinforcement‑learning variant driven by internal motivations. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, and details how exteroceptive and interoceptive signals, bodily context, and memory traces form associative structures called semblions that compete for processing and reconstruction. The model incorporates need thresholds, goal dynamics, bodily state, resource constraints, and action uncertainty, and posits global affect as a central control signal modulating learning rate, representational valence, and exploration‑exploitation balance.
The article introduces a mathematical model of the Motivated Emotional Mind cognitive architecture for embodied intelligent systems. It formalizes a re‑entrant loop that integrates feedforward processing, lateral interactions, and feedback pathways, along with representational selection mechanisms that govern adaptive responses. The model binds exteroceptive and interoceptive signals, bodily-motivational context, and memory traces into associative structures called semblions, enabling motivated learning that incorporates need thresholds, goal generation, and regulatory constraints.
By Wies{\l}aw L. Galus, Janusz A. Starzyk
arXiv:2607. 13560v1 Announce Type: cross Abstract: Recent advances in generative and embodied AI have been driven by large-scale predictive learning over multimodal data.
By Giovanni Pezzulo, Davide Nuzzi, Marco D'Alessandro, Riccardo Proietti, Roberto Bottini, Paul Cisek
arXiv:2609.17325v1 Announce Type: new
Abstract: Biological cells can be viewed as individual, interacting agents whose collective dynamics give rise to adaptive behaviour at multiple levels of organi...
By Anatoly Belikov
The paper proposes a developmental framework for autonomous artificial agents that emphasizes learning social norms and alignment through direct interaction with dynamic environments. It argues that intrinsic motivations such as curiosity and competence can guide exploration, but also complicate alignment with human goals. By drawing parallels to child development, the authors suggest that regulatory sandboxes serve as pedagogical spaces where agents gradually acquire moral agency and adapt their behaviors through experience and cooperation.
By Marica Notte, Ludovica Marinucci, Vieri Giuliano Santucci
arXiv:2609.22691v1 Announce Type: new
Abstract: An enduring and richly elaborated dichotomy in cognitive neuroscience is that of human behavior control mechanisms, divided into habitual versus goal-d...
By Chongyu Bao, Haokai Yang, Yuhan Wang, Zhaochong An, Kunpeng Liu, Xiaolan Liu
arXiv:2609.06654v1 Announce Type: new
Abstract: Computational modeling of emotion has long faced a tension between descriptive, "snapshot-based" appraisal models and granular, signal-driven architect...
By Bernhard Hilpert, Tam\'as Sz\H{u}cs, Joost Broekens, Agnes Moors
arXiv:2605.13872v2 Announce Type: replace-cross
Abstract: This article introduces S-AI-Recursive, a bio-inspired Sparse Artificial Intelligence architecture in which reasoning is implemented as a hor...
By Said Slaoui
arXiv:2609.17064v1 Announce Type: new
Abstract: Assistive autonomous systems must anticipate human goals before an observed behavior is complete. This article formulates anticipation as goal inferenc...
By Farnaz Soleimani (LISSI), Abdelghani Chibani (LISSI), Yacine Amirat (LISSI), Ghazaleh Khodabandelou (LISSI)
arXiv:2511. 10119v4 Announce Type: replace Abstract: We propose a new perspective for approaching artificial general intelligence (AGI) through an intelligence foundation model (IFM).
By Borui Cai, Yao Zhao
arXiv:2606. 20858v2 Announce Type: replace Abstract: The temporal structure of reward composition in reinforcement learning (RL) is typically hand-designed and held fixed throughout training, leaving the progression of motivational priorities largely unexplored.
By Alan Nadelsticher Ruvalcaba
arXiv:2606. 00133v1 Announce Type: new Abstract: World models, internal simulators that learn the structure and dynamics of an environment, have emerged as a central paradigm in the pursuit of artificial general intelligence, enabling agents to predict, plan, and reason within learned representations.
By Arif Hassan Zidan, Yi Pan, Hanqi Jiang, Ruiyu Yan, Wei Ruan, Zihao Wu, Lifeng Chen, Weihang You, Xinliang Li, Bowen Chen, Huawen Hu, Peilong Wang, Sizhuang Liu, Jing Zhang, Siyuan Li, Zhengliang Liu, Yu Bao, Lin Zhao, Lichao Sun, Dajiang Zhu, Xiang Li, Jinglei Lv, Quanzheng Li, Wei Liu, Tianming Liu, Wei Zhang
Assistive autonomous systems must anticipate human goals before an observed behavior is complete. This article formulates anticipation as goal inference from a partially observed multimodal episode to...