Jazib Ahmad, Riley Keays, Aiyang Liang, Linas Gabrys, Truman Yang, Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada
The purpose of this paper is to propose a new Q-Learning based pathfinding algorithm to solve mazes in which the algorithm (“agent”) must find multiple subgoals before reaching a final destination, in a lower number of iterations than existing Q-Learning algorithms. The proposed design is the use of Multiple Deep Q-Networks, each of which is responsible for finding the shortest path to the nearest subgoal or final destination. We also optimize our design with an improved Exploration Strategy, the addition of a Revisiting Penalty, as well as hyperparameter optimization. We test our solution on sample mazes of four sizes and compare it to the Multiple Q-Table and Single Deep Q-Network algorithms. Our results confirm our hypothesis and show that our solution outperforms the other algorithms in the number of iterations to find the shortest path, especially on larger mazes. Finally, we offer suggestions for alternative designs, future work, and improvements.
Deep Q-Learning, Multiple Goal Pathfinding, Multiple Q-Tables, Neural Networks, Reinforcement Learning.
Cem Yılmaz (Purdue University, IE)
We introduce Self-Aware AI, a modular architecture that integrates affective, ethical, and neurodynamic mechanisms to instantiate the functional hallmarks of consciousness in software agents. Our design comprises: 1. A 25-dimensional qualia manifold combining Plutchik’s eight primary emotion axes with ethical, interoceptive, mood, mixed, and aesthetic dimensions (Eq. 1). 2. Predictive novelty gating via deep-ensemble forecasting whose variance drives adaptive storage thresholds (Eqs. 2–5). 3. Memory-particle dynamics modeled as interacting bodies under dopaminergic attraction, entropy repulsion, and similarity cohesion (Eqs. 6–8). 4. Adaptive spiking binding through LIF microcircuits, STDP-governed rewiring, and homeostatic neuromodulation maximizing integrated information Φ (Eqs. 9–12). 5. A hierarchical θ–γ global workspace implemented by nested Kuramoto oscillator layers for layered attentional broadcast (Eqs. 13–14). 6. Intrinsic drives—curiosity, learning-progress, empowerment—trained by PPO, plus a counterfactual-self module generating genuine agency signals (Eqs. 15–16). 7. Case-based ethical reasoning with FAISS retrieval and ASP planning mapping solver confidence into a moral-sentiment axis (Eq. 17). 8. Autobiographical event graphs driving Transformer-based narrative generation, evaluated by a coherence critic. 9. A five-stage developmental curriculum protected by Elastic Weight Consolidation (Eq. 18). 10. A rigorous evaluation protocol including a 10 000-step stub simulation, systematic ablations, and human-in-the-loop assessments. This paper details each component’s equations and variables, presents baseline results, and outlines a roadmap toward AI agents that feel, remember, bind, reflect, decide, and narrate—thus realizing the functional essence of consciousness
Shishir Maurya, Anshul Verma, Yugal Gopal Sharma, Dhanush Dharmaretnam, SGS&CO, Louisville, Kentucky, USA
This study proposes a novel method for recognizing and categorizing logos in packaging artwork to address the automation demands of the printing and packaging industry. The approach combines a trained object detection model for logo detection followed by a fine-tuned Vision Language Model (VLM) for hierarchical tag generation, achieving high precision across seven primary categories: sustainability, health and safety, branding, material identification, eco-friendly certification, social media, and compliance, with all others grouped under "others." In the first step, YOLOv8 detects logos and assigns them to primary categories, achieving a mean average precision (mAP) of 0.58 and an Intersection over Union (IoU) threshold of 0.5. In the second step, a fine-tuned VLM generates granular tags for the detected logos. Notably, Low Rank Adaptations (LoRA) applied to the Florence-2-DocVQA model (with r = 64 and 𝛼 = 128) surpassed the zero-shot performance of state-of-the-art Visual Language Models (VLMs), achieving a 24-fold improvement with a ROUGE-L F1 score of 0.72. This study also demonstrates the cost effectiveness and practicality of using smaller models with fewer parameters, which perform comparably to larger VLMs, incurring much lower training and perational costs. These advancements streamline design and print production workflows, improve compliance tracking, and enhance brand management, contributing to greater automation in the packaging and printing industry.
Packaging Artwork, VLMs, Artwork Tagging, Low Rank Adaptation (LoRA).
Abdelouahab Hocini and Kamel Smaıli, University of Lorraine, France
This work explores the use of Large Language Models (LLMs) for fake news detection in multilingual and multi-script contexts, focusing on Arabic dialects. We address the challenge of insufficient digital data for many Arabic dialects by using pretrained LLMs on a diverse corpus including Modern Standard Arabic (MSA), followed by fine-tuning on dialect-specific data. We examine AraBERT, DarijaBERT, and mBERT for performance on North African Arabic dialects, incorporating code-switching and writing styles such as Arabizi. We evaluate these models on the BOUTEF dataset, which includes fake news, fake comments, and denial categories. Our approach fine-tunes both Arabic and Latin script text, with a focus on cross-script generalization. We improve accuracy using an ensemble strategy that merges predictions from AraBERT and DarijaBERT. Additionally, we introduce a new custom loss function, named CALLM to enforce consistency between models, boosting classification performance. The use of CALLM achieves significant improvement in F1-score (12.88 ↑) and accuracy (2.47 ↑) compared to the best model (MarBERT).
NLP, LLM, Fake news detection.
Mark Temple-Raston, Decision Machine, LLC, New York,USA
Scientific Machine Learning implements the science-of-counting to analytically processes any time-series and produce a complete set of thermodynamic measurements that define the state of the system. The scientific measurements are initially illustrated with a time-series of closing-prices for a stock. Exact scientific measurements from Scientific Machine Learning (SML) are then used to create time-series decision services, without model or bias, a Decision Machine. A Decision Machine service is built for a large class of Open allocation problems and applied to optimal sales for a consumer product good.
Machine Learning, Risk Analysis, Non-Equilibrium Thermodynamics, Information Theory, Decision Theory.
Stephen W. Marshall and Jurgen Valckenaere, University of Western Australia
The infrastructure for capturing environmental data has rapidly advanced—networks of sensors, satellites, and telemetry now monitor planetary systems at unprecedented resolution. Yet architectures capable of translating that data into real-time signals remain critically underdeveloped. Climate informatics continues to rely on static, retrospective models—built to document, not respond. This paper introduces a framework for generative environmental intelligence: signal-based systems capable of detecting stress, issuing directives, and simulating future states across biospheric and geopolitical scales. Drawing from financial signal processing and autonomous feedback design, this model collapses the gap between ecosystemic volatility and coordinated action. The Los Angeles wildfires illustrate the potential of real-time signal architectures to enable anticipatory governance. Technologies such as HALO and PROTOSTAR—two generative modules designed for climate intelligence architecture—demonstrate how planetary signals can evolve from alerts into infrastructure. The systems exist. The challenge is not availability, but the willingness to evolve global signaling apparati.
Signal Processing Systems, Climate Informatics, Autonomous Feedback Networks, Predictive Modeling, Planetary Intelligence.
Md. Asgor Hossain Reaj1, Md Yeasin Rahat1, Md Kishor Morol2, and Md. Jakir Hossen3, 1American International University-Bangladesh, 2ELITE Lab.AI, 3Multimedia University
Large Language Models (LLMs) have achieved significant success in recent years; yet, is- sues of intrinsic gender bias persist, especially in non-English languages. Although current research mostly emphasizes English, the linguistic and cultural biases inherent in Global South languages, like Bengali, are little examined. This research seeks to examine the characteristics and magnitude of gen- der bias in Bengali, evaluating the efficacy of current approaches in identifying and alleviating Bias. We use several methods to extract gender-biased utterances, including lexicon-based mining, computa- tional classification models, translation-based comparison analysis, and GPT-based bias creation. Our research indicates that the straight application of English-centric bias detection frameworks to Bengali is severely constrained by language disparities and socio-cultural factors that impact implicit biases. To tackle these difficulties, we executed two field investigations inside rural and low-income areas, gathering authentic insights on gender Bias. The findings demonstrate that gender Bias in Bengali presents dis- tinct characteristics relative to English, requiring a more localized and context-sensitive methodology. Additionally, our research emphasizes the need of integrating community-driven research approaches to identify culturally relevant biases often neglected by automated systems. Our research enhances the ongoing discussion around gender bias in AI by illustrating the need to create linguistic tools specifically designed for underrepresented languages. This study establishes a foundation for further investigations into bias reduction in Bengali and other Indic languages, promoting the development of more inclusive and fair NLP systems.
gender bias, natural language processing, language bias.
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