This final project examines how adaptive neurocognitive mechanisms related to threat detection, uncertainty processing, anticipation, reward learning, social comparison, belonging, metacognition, and behavioral regulation interact with characteristics of contemporary environments. The study adopts a theoretical and integrative literature review approach and proposes the Adaptive Mechanism–Environmental Amplification Model (AMEA), a hypothesis-generating conceptual framework describing how repeated environmental signals may increase the frequency, persistence, or regulatory importance of otherwise adaptive neurocognitive mechanisms. Rather than assuming that contemporary technologies or environments are intrinsically harmful, the model emphasizes the interaction among environmental architecture, perceived relevance, learning and reinforcement, individual differences, perceived control, metacognitive capacity, and behavioral flexibility. The study discusses potential pathways involving threat amplification, uncertainty-driven information seeking, reward-based checking, social comparison, external validation, belief reinforcement, and decision-making under uncertainty, while explicitly considering alternative explanations, limitations, and conditions under which the proposed model could be empirically challenged. Keywords: Neuroscience; Human Behavior; Uncertainty; Stress; Reward; Social Comparison; Belonging; Metacognition; Decision-Making; Behavioral Regulation; Digital Environments; AMEA.
Thiago Campos Silva· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to Neural Architecture Search (NAS) termed Dynamic Topological Adaptive NAS (DTANAS), which leverages concepts from topological data analysis and physical systems to achieve more efficient and robust architecture optimization. The core idea is to model the search space as a complex topological network, allowing for dynamic changes in architecture during the training process. We introduce a framework where architectural modifications are viewed as topological transformations, induced by random perturbations and feedback mechanisms. These mechanisms mimic the adaptive behavior observed in physical systems undergoing evolution and self-organization. The system employs an evolutionary or reinforcement learning strategy to evaluate and refine the 'topological quality' of the network architecture. Unlike traditional NAS methods that rely on static search spaces and gradient information, DTANAS offers a fundamentally different perspective, potentially leading to architectures that are more resilient to noise and better suited for complex, evolving tasks. This work presents a theoretical framework and a computational approach to explore this paradigm, demonstrating the potential for significant improvements in NAS efficiency and architectural quality.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the development of adaptive quantum algorithms designed to optimize the performance of quantum algorithms. The core concept involves dynamically adjusting parameters such as qubit coupling strength and measurement time, based on the algorithm's runtime behavior. This is achieved through the application of reinforcement learning, where an agent learns to optimize these parameters in response to metrics like decoherence and measurement error. The research addresses a significant challenge in quantum algorithm design – the difficulty in determining optimal parameter values – and presents a novel approach for achieving superior performance. The framework outlined herein provides a pathway for creating more robust and efficient quantum algorithms across a range of applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to neural network design utilizing self-organizing principles to dynamically learn network topology. The core idea involves constructing a neural network architecture capable of adapting its structure in response to incoming data and its internal state. This adaptation is achieved through a feedback loop mechanism governed by a reinforcement learning paradigm. Neurons adjust their connections based on activation patterns and error signals, iteratively refining the network's structure to optimize performance. The resultant networks exhibit data-driven learning of topology, moving beyond static, pre-defined architectures. This approach offers significant potential in scenarios where the underlying data structure is unknown or constantly evolving, leading to more efficient and robust neural networks. The primary mathematical framework focuses on the recurrent dynamics of the network, expressed through differential equations and Markov chain analysis, to model the evolving connection weights and neuron activation states. Key performance metrics, such as the mean squared error (MSE) and the convergence rate, are employed to evaluate the effectiveness of the learning process.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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Deep Reinforcement Learning (DRL) has demonstrated remarkable success in solving complex control problems. However, deploying DRL agents in real-world scenarios, particularly in safety-critical applications, presents significant challenges. The inherent stochasticity and complex decision-making processes of DRL policies often lead to unpredictable and potentially unsafe behavior. This work addresses this challenge by formally verifying the safety and correctness of DRL policies using temporal logic. We construct a temporal logic model that precisely captures the desired behavior of the policy and employ model checking techniques to rigorously determine whether the policy satisfies the specified temporal logic constraints. This approach offers a novel and rigorous method for ensuring the reliability and trustworthiness of DRL systems, bridging the gap between the empirical successes of DRL and the formal guarantees provided by verification techniques. The core contribution lies in applying temporal logic as a precise specification language to define the desired behavior of a DRL policy, enabling automated verification of its adherence to safety and correctness properties. This process moves beyond simply observing policy performance to providing a mathematically sound basis for confidence in the policy's operation.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The efficient scheduling of distributed machine learning (ML) jobs presents a significant challenge due to the complex interplay of factors such as varying job resource requirements, heterogeneous computing environments, and dynamic workload fluctuations. Traditional scheduling approaches often rely on heuristics or simple optimization techniques, which may not effectively address the inherent complexities of ML workflows. This paper proposes a novel approach leveraging Markov Decision Processes (MDPs) to model and solve this scheduling problem. We formulate an MDP where states represent the current job queue and resource availability, and actions represent scheduling decisions, such as assigning a job to a specific worker or delaying its execution. A reinforcement learning (RL) algorithm is then employed to learn an optimal scheduling policy through interaction with the MDP. This approach offers a more principled and potentially more effective solution compared to traditional methods, leading to improved resource utilization, reduced job completion times, and overall enhanced performance of distributed ML systems. The core contribution lies in the formalization of the scheduling problem within an MDP framework and the subsequent application of RL to discover optimal scheduling strategies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel recursive learning system (BRRLS) that leverages biofeedback to enable adaptive learning. The core idea is to utilize real-time monitoring of physiological signals, such as heart rate and electroencephalography (EEG), to construct a system capable of dynamically adjusting its learning strategy. The system employs reinforcement learning algorithms, where the biofeedback signals serve as both learning objectives and feedback signals. This allows for the continuous optimization of learning parameters and strategies, leading to improved performance on complex tasks. The novelty of BRLS lies in its direct integration of biological feedback mechanisms to imbue the learning system with self-regulatory capabilities, mirroring biological learning processes. This approach has significant potential applications in areas such as robotic control and human-computer interaction. The system's architecture incorporates a feedback loop designed for iterative improvement, fundamentally distinguishing it from traditional, static learning models. Mathematical formulations detail the key components and operational principles of the BRRLS, emphasizing the role of state estimation, reward function design, and policy optimization within the reinforcement learning framework. The system is designed for modularity, allowing for the integration of diverse biofeedback modalities and reinforcement learning algorithms. Future research will focus on scaling the system to handle more complex tasks and exploring the potential for transferring learned strategies to different environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel hybrid framework integrating the Simulated Evolution Algorithm (SEA) and Deep Reinforcement Learning (DRL) to tackle complex optimization problems. The core idea is to leverage SEA's global search capability for initial exploration and DRL's local optimization prowess for refining solutions. The framework operates through an iterative process: SEA generates a diverse population of potential solutions, and DRL is then employed to optimize individual solutions or subsets of the population. Crucially, the parameters of both algorithms are iteratively updated based on their performance, enabling a synergistic evolution. We demonstrate the framework's effectiveness through theoretical analysis and a detailed explanation of the mechanisms involved. The key contribution lies in establishing a robust and adaptable method for combining these two powerful techniques, promising improved efficiency and solution quality compared to using them independently. This work provides a foundational approach for future research exploring the synergy between evolutionary and reinforcement learning methods.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to program understanding and optimization, termed Dynamic Semantic Topology Learning and Program Reasoning. The core claim is that program behavior is not solely determined by its source code, but significantly influenced by the dynamic semantic relationship network within its runtime environment, which evolves during execution. We propose a mechanism utilizing Graph Neural Networks (GNNs) to construct and continuously update a program semantic topology. Nodes in this topology represent program elements such as functions, variables, and data structures, while edges represent their semantic dependencies – including call relationships, data flow relationships, and control flow relationships. A reinforcement learning-based GNN learning algorithm leverages feedback signals from program execution (e.g., results, errors, resource consumption) to adjust the topology, enhancing its accuracy in reflecting program behavior. This dynamic approach overcomes limitations of static semantic representations like Abstract Syntax Trees (ASTs) and Control Flow Graphs (CFGs) by capturing the complex, runtime-dependent relationships, ultimately leading to improved program understanding and optimization. The methodology offers a new paradigm for analyzing and reasoning about program logic, particularly in dynamic and complex systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of probabilistic programming to the field of model-based reinforcement learning (MBRL). Traditional MBRL approaches often rely on deterministic models, which can be brittle and fail to adequately represent the inherent uncertainty in real-world environments. We argue that leveraging probabilistic programming languages allows for the creation of more robust, interpretable, and adaptable RL agents. The core concept involves representing both the environment dynamics and the agent's policy as probabilistic models. This enables the agent to explicitly reason about uncertainty, quantify its confidence in predictions, and ultimately, make more informed decisions. We demonstrate the potential of this approach through a theoretical framework, focusing on the formulation of probabilistic models for state transition and reward functions. The resulting agent can dynamically update its understanding of the environment, leading to improved performance and increased resilience to unforeseen circumstances. This work provides a foundation for future research in probabilistic MBRL and highlights the importance of incorporating uncertainty into the design of intelligent agents.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the development of an algorithmic chaos theory system designed to generate complex, multi-dimensional chaos patterns without relying on predefined, fixed parameters. Leveraging a reinforcement learning-inspired approach, the algorithm continuously adjusts its internal parameters based on observed system behavior, fostering emergent chaos generation. We propose a novel framework that moves beyond traditional chaotic systems, emphasizing dynamic adaptation and self-governing behavior, ultimately aiming to create systems exhibiting a higher degree of unpredictability. The core mechanism centers around a feedback loop where the algorithm's output is evaluated, and adjustments are made to its internal state, driving the creation of intricate chaotic configurations. This research addresses a key challenge in chaos theory – the requirement for adaptive systems – and offers a new methodology for exploring and generating complex dynamic systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the potential of quantum computing to revolutionize Monte Carlo Tree Search (MCTS) algorithms, a cornerstone technique in reinforcement learning and game AI. The core claim is that by harnessing quantum superposition and interference, we can significantly accelerate MCTS's exploration of complex optimization landscapes. The proposed approach utilizes quantum circuits to represent and evaluate game states, leveraging quantum parallelism to concurrently assess multiple branches of the search tree. We demonstrate, through theoretical analysis and algorithmic design, how this quantum-enhanced MCTS can outperform classical MCTS in scenarios with high computational complexity and vast search spaces. The resulting system offers a novel approach to solving complex optimization problems, particularly those found in areas such as game playing, portfolio optimization, and drug discovery. The presented methodology focuses on the conceptual framework and provides a roadmap for future research and development, emphasizing the integration of quantum hardware with sophisticated reinforcement learning strategies. The key innovation lies in the efficient mapping of the MCTS search process onto a quantum computing architecture, exploiting quantum mechanics to dramatically reduce the search time.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
