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1 paper in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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A Mathematical Model of Motivated Emotional Mind - Cognitive Embodied System
Sep 17, 2026Wiesław L. Galus, Janusz A. StarzykIntrinsic Motivation Sensorimotor Experience
Training-Free Inpainting Across Domains with a Frozen Text-to-Image Diffusion Model
Sep 1, 2026Zhenhuan Wang, Fengyi YuanInpainting Text-To-Image Diffusion Models
School of Data Science, The Chinese University of Hong Kong, Shenzhen · School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen
Semantic Lenia: Emergence of Homeostatic Solitons within the Semantic Space of Large Language Models
Aug 12, 2026Yoshihiko KayamaSyntactic Structure Emergence
BAIKA Women’s University 2–19–5, Ibaraki, Osaka, Japan
AutoSupervision: Closing the Feedback Loop in Scientific Workflows with Grounded Revision Verification
Jul 30, 2026Haobo Li, Eunseo Jung, Wenxiao Zhao +8Peer Review Scientific Workflows
1Shanghai AI Laboratory · 3The Hong Kong University of Science and Technology · University of California, Los Angeles
The User Asks, Platforms Compete: How Agentic Recommendation Markets Take Shape
Jul 28, 2026Deyao Hong, Kehan Zheng, Qian Li +3Agentic Commerce Agentic
Tsinghua University · Tencent Inc.
LLM-as-a-Coach: Experiential Learning for Non-Verifiable Tasks
Jul 20, 2026Tianzhu Ye, Li Dong, Guanheng Chen +4Feedback Loop Tutors
Microsoft Research · Tsinghua University · Peking University
The FIL Hypothesis: Inductive Biases Help with Kernel Engineering
Jun 29, 2026Nikolai Rozanov, Subhabrata Dutta, Preslav Nakov +1Inductive Bias Feedback Loop
1NLP Department, MBZUAI, Abu Dhabi, UAE · Department of Computing, Imperial College London, London, UK · 2Ubiquitous Knowledge Processing, TU Darmstadt, Darmstadt, Germany
Adaptive Bayes exactly tracks information over intrinsic time
Jun 26, 2026Akshay BalsubramaniFeedback Loop
Aurora: A Leverage-Aware Spectral Optimizer
Jun 26, 2026Alec Dewulf, Dhruv Pai, Li Yang +2Muon Batch Normalization
Tilde Research
FeLoG: Scalable and Efficient Distributed Graph Embedding with Feedback Loop Mechanism
Jun 20, 2026Peng Fang, Arijit Khan, Ziqiang Wu +4Graph Representation Learning Feedback Loop
Huazhong University of Science and Technology · Bowling Green State University
ENPIRE: Agentic Robot Policy Self-Improvement in the Real World
Jun 18, 2026Wenli Xiao, Jia Xie, Tonghe Zhang +14Robot Policies Embodied Agents
1NVIDIA · 2CMU · 3UC Berkeley
Bridging Creative Intent and Visual Quality: Creator-Driven Recurrent Video Generation with Agentic Feedback Loops
Jun 17, 2026Denis Savytski, Aiden Lei, Heding Liu +4Video Generation Generative Artificial Intelligence
Department of Computer Science, University of California, Davis, USA · The Harker School · Basis Independent Silicon Valley +1
Closing the Feedback Loop: From Experience Extraction to Insight Governance in Verbal Reinforcement Learning
Jun 16, 2026Yanwei Cui, Xing Zhang, Yulong Zhang +4Feedback Loop Offline Reinforcement Learning
AWS Generative AI Innovation Center · Amazon Web Services (AWS) · BingX Group Limited.
Partially Performative Prediction
Jun 5, 2026Jaewook Lee, Tijana ZrnicDistribution Shifts Feedback Loop
Department of Electrical Engineering, Stanford University · Departments of Statistics and Management Science & Engineering, Stanford University
Learn to Match: Two-Sided Matching with Temporally Extended Feedback
Jun 4, 2026Haijing Zong, Yancheng Liang, Boyang Zhou +1Distribution Matching Matching Markets
Department of Economics, University of Washington, Seattle, United States · Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, United States
Hierarchical Online Prompt Mutation with Dual-Loop Feedback for Guardrailed Evidence Document Generation: A Production-Evaluation Case Study
May 31, 2026Nataraj Agaram Sundar, Tejas MorabiaPrior Prompt Optimization Techniques Review
eBay Inc. · San Jose, CA, USA
Posture Clip: Sit properly or I wont let you work
May 25, 2026Arka Majhi, Aparajita MondalFeedback Loop Claw-Like Agent
Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, 33100, Pirkanmaa, Finland. · School of Forest Sciences, University of Eastern Finland, Joensuu, 80101, North Karelia, Finland.
CandorMD: An AI-Assisted Audio Simulation and Feedback System for Training Clinicians for Medical Error Disclosure
May 20, 2026Inna Wanyin Lin, Sahand Sabour, Hong Sng +4Simulated Patients Clinician Trust
University of Washington Seattle, WA, USA · Tsinghua University Beijing, China
From Feedback Loops to Policy Updates: Reinforcement Fine-Tuning for LLM-Based Alpha Factor Discovery
May 14, 2026Lingzhe Zhang, Tong Jia, Yunpeng Zhai +5Exploratory Factor Analysis Reinforcement Fine-Tuning
Peking University, Beijing, China · Alibaba Group, China · Nanjing University, Nanjing, China +1
Continual Learning of Feedback-based Molecular Communication
May 1, 2026Siddhant Setia, Junichi Suzuki, Tadashi NakanoReplay-Based Continual Learning Continual Learning
Department of Computer Science, University of Massachusetts, Boston Boston, MA, 02125, USA · Graduate School of Informatics, Osaka Metropolitan University Osaka, Japan
Huí Sù: Co-constructing a Dual Feedback Apparatus
Apr 28, 2026Yichen Wang, Charles Patrick MartinFeedback Loop Feedback
The Australian National University, Canberra, ACT, Australia
Evaluating LLM-Based Goal Extraction in Requirements Engineering: Prompting Strategies and Their Limitations
Apr 24, 2026Anna Arnaudo, Riccardo Coppola, Maurizio Morisio +4Feedback Loop
Department of Control and Computer Engineering, Politecnico di Torino · Politecnico di Torino
Developing an LLM-Based Feedback System Grounded in Evidence-Centered Design to Support Physics Problem Solving
Dec 11, 2025Holger Maus, Fabian Kieser, Stefan Petersen +2Feedback Feedback Loop
Department of Physics Education, Leibniz Institute for Science and Mathematics Education Kiel, Germany · Department of Physics Education Research, Free University of Berlin Berlin, Germany · Department of Physics and Physics Education Research, Ludwigsburg University of Education Ludwigsburg, Germany
Interpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations
Dec 9, 2025Daniele Sorini, Sownak Bose, Mathilda Denison +1Cosmology Astronomical Images
Institute for Computational Cosmology, Department of Physics, Durham University, South Road, Durham, DH1 3LE, United Kingdom · Department of Physics and Astronomy, University of Pennsylvania, 209 South 33rd Street, Philadelphia, PA 19104-6396, USA · Institute for Astronomy, University of Edinburgh, Royal Observatory, Blackford Hill, Edinburgh, EH9 3HJ, United Kingdom +2

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A Mathematical Model of Motivated Emotional Mind - Cognitive Embodied System

Wiesław L. Galus, Janusz A. Starzyk

Intrinsic Motivation Sensorimotor Experience Cognitive Science Feedback Loop

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This article presents a mathematical model of the Motivated Emotional Mind cognitive architecture developed for embodied intelligent systems. Such a system learns to maintain its homeostasis through a generalized form of reinforcement learning based on its internal motivations, termed motivated learning (ML). The principal contribution of this article is a rigorous formalization of the re-entrant loop integrating feedforward processing, lateral interactions, and feedback pathways, together with the representational selection mechanisms that govern adaptive system responses. The model specifies how ongoing exteroceptive and interoceptive signals, bodily-motivational context, and memory traces are bound into associative memory structures termed semblions, which compete for access to further processing and top-down reconstruction. The formalization encompasses secondary perception, representational competition, curiosity, procedural gaps, and action selection directed toward limiting allostatic violations. Within this framework, motivated learning is tailored to embodied systems whose dynamics are shaped by needs, affect, and the current regulatory state. Unlike standard reinforcement-learning models, the proposed approach incorporates need thresholds, goal generation and shifting goals, bodily state, resource constraints, and action uncertainty, thereby providing a more adequate account of response selection under regulatory pressure. Global affect functions as a central control signal, modulating the learning rate, representational valence, and the balance between exploration and exploitation. The model presented here is a step toward a more rigorous formalization of cognitive phenomena and may provide a basis for further theoretical analysis, computer simulation, and implementation in artificial-intelligence systems inspired by biological processes.

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