This paper explores the application of adaptive chaos theory, leveraging reinforcement learning to dynamically adjust parameters of chaotic systems. Traditional approaches often rely on fixed, static parameter sets, limiting the system's flexibility and potential for robust behavior. This research introduces a novel system that employs a reinforcement learning agent to iteratively refine parameters based on real-time feedback, offering a paradigm shift towards intelligent parameter control. The core mechanism centers around a feedback loop that continuously evaluates the system's response to changing conditions, optimizing the parameters to achieve desired outcomes. The goal is to move beyond static parameter sets and towards a system capable of adapting to complex, evolving environments.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to digital circuit synthesis leveraging the power of Evolutionary Reinforcement Learning (ERL). Traditional circuit synthesis methods often rely on deterministic algorithms that struggle to effectively manage the complexity and dynamism inherent in modern design requirements. This research addresses this limitation by implementing an ERL system capable of iteratively generating and refining circuit designs. The core of the system involves an evolutionary algorithm that explores the design space, guided by a reinforcement learning agent that dynamically learns a reward function based on design rules and constraints. This dynamic reward function enables the system to adapt to evolving design priorities and optimize for performance metrics such as delay, power consumption, and area. The resulting system demonstrates the potential to overcome the drawbacks of conventional synthesis techniques, offering a more robust and adaptive solution for complex circuit design problems. The key innovation lies in the synergistic combination of evolutionary search and reinforcement learning, creating a system that can autonomously learn and refine circuit designs within highly constrained and dynamic environments. The system is designed for adaptability and efficiency, and the potential for its application in various digital design domains is significant.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Neuro-hacking, understood here as the deliberate application of neuroscience-informed techniques to regulate attention, memory formation and cognitive load, has begun to migrate from clinical and self-improvement contexts into mainstream higher-education pedagogy. This review synthesises evidence from five primary studies retrieved through a structured literature search, supplemented by nine additional peer-reviewed sources on blended learning and neuroeducation, to examine how neuro-hacking models can be embedded across online and face-to-face components of blended courses, and what effectiveness data currently support them. Three recurring model families are identified: technology-mediated brain monitoring (e.g., brain–computer interfaces, eye-tracking, neuro-feedback), brain-compatible pedagogical design (short task intervals, immediate feedback, multisensory cues), and cognitive training techniques (brain gymnastics, neuro-linguistic programming, spaced repetition). Across 1,109 undergraduate participants in technical disciplines, statistically significant gains of three to nine percentage points in academic performance were reported, although the most rigorous randomised design produced the smallest effect size, suggesting that weaker methodologies may inflate apparent benefits. The review concludes that blending is not incidental but functionally necessary: neuro-hacking techniques appear to work best when short, feedback-rich online activities are paired with face-to-face reinforcement rather than deployed in either modality alone. Implementation barriers, equity concerns and the risk of conflating neuroscience with popular neuromyths are discussed, and directions for methodologically stronger future research are proposed.
Arumugam Sivasankar· International Journal of Dig...· 0 citations
This research investigates the application of reinforcement learning to automatically generate and optimize geometric patterns, focusing on identifying novel patterns exhibiting complex, self-organizing behavior. Traditional geometric pattern generation often relies on predefined rules, limiting the potential for truly creative and responsive designs. We propose a novel approach leveraging reinforcement learning to explore the vast space of possible patterns, rewarding patterns that demonstrate emergent complexity and self-organization. The core mechanism centers on using a reinforcement learning agent to iteratively refine patterns based on feedback, leading to the discovery of aesthetically pleasing and functionally relevant geometric forms. This work aims to advance computational geometry by providing a method for automated pattern design, pushing the boundaries of what is possible with algorithmic creativity.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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This paper explores the application of reinforcement learning to dynamically optimize topology in parallel computing systems. The core claim is to develop a system capable of automatically adjusting the topology of parallel environments—specifically, the number of threads and processes—to maximize throughput and minimize latency, driven by workload characteristics. We propose a novel reinforcement learning agent trained to learn optimal topology configurations based on real-time workload analysis. This system aims to overcome the limitations of traditional manual tuning methods, offering a more adaptive and responsive approach to parallel system design. The research will detail the architecture of the agent, the training process, and preliminary results demonstrating its effectiveness in a simulated environment.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces the Neuro-Symbolic Symbiosis Architecture (NSSA), a novel framework designed to enhance reasoning and learning capabilities by dynamically adjusting the interaction between neural networks and symbolic knowledge bases. The core claim is that by modulating the interaction strength between these two components, we can achieve more efficient and robust inference. NSSA employs a multi-layered architecture comprising a neural inference layer for initial pattern recognition and representation learning, a symbolic knowledge base layer for storing structured knowledge, and a symbiotic engine—a reinforcement learning-based controller—to dynamically adjust the flow of information between these layers. The engine's objective is to minimize inference error while maximizing knowledge base utilization. Unlike existing neuro-symbolic methods that often rely on static structures or fixed interaction methods, NSSA leverages reinforcement learning for a highly dynamic and adaptive symbiotic relationship, potentially leading to improvements in generalization, interpretability, and efficiency. The presented architecture offers a shift away from the traditional "neural network vs. symbolic knowledge" dichotomy, emphasizing the mutual dependence and synergistic interaction between the two representation paradigms.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of reinforcement learning to automatically optimize lattice parameterization for complex systems, particularly in materials science and molecular modeling. The core claim is to develop a system that dynamically adjusts lattice parameters based on real-time feedback, aiming to improve a desired physical property through optimization. Traditional lattice parameterization methods are static and require manual adjustment, offering a dynamic optimization approach. This research explores the implementation of a reinforcement learning agent to learn optimal parameter settings, leading to improved physical property performance. The paper details the system architecture, the reinforcement learning agent's training process, and the evaluation of its performance on a representative dataset.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Traditional cognitive computing systems often struggle with robust reasoning and adaptability, particularly in dynamic and uncertain environments. This research proposes a novel approach to cognitive computing by integrating Bayesian belief networks (BBNs) and reinforcement learning (RL). BBNs provide a framework for representing prior knowledge, incorporating uncertainty, and performing deductive reasoning, while RL enables the system to learn optimal policies through interaction and reward maximization. The combined system leverages the strengths of both paradigms, allowing for a more adaptive, intelligent, and robust cognitive agent. We outline the core mechanisms, highlighting the synergy between knowledge representation via BBNs and policy learning through RL. The proposed architecture offers a pathway towards creating cognitive systems capable of handling complex, real-world scenarios where traditional methods fall short.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The design of effective feedback loops is a fundamental challenge in many engineering and scientific disciplines. Traditional approaches often involve manual tuning of parameters to achieve desired outcomes, which is a time-consuming and potentially error-prone process. This paper introduces the Self-Organizing Feedback Loop (SOFL) algorithm, a novel approach that leverages reinforcement learning to automatically optimize system parameters based on observed feedback. The core mechanism centers around a continuous reinforcement learning process that dynamically adjusts parameters to maximize desired outcomes, eliminating the need for manual tuning. This offers a significant advancement in system parameter control, reducing reliance on human intervention and enhancing system performance. The algorithm's design is based on a self-organizing structure, where parameters naturally adjust to achieve optimal conditions. We explore the theoretical foundations and practical implementation details of the SOFL, demonstrating its potential for automating parameter optimization and improving system performance.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to large-scale code analysis leveraging hierarchical abstraction learning. The core idea is to accelerate code analysis by learning hierarchical representations of code structures, effectively capturing semantic relationships across multiple levels of granularity. A reinforcement learning agent is employed to iteratively refine a hierarchical tree representation of the code, guided by metrics encompassing code complexity, data flow, and control flow. Unlike traditional static analysis methods, this approach dynamically adapts to the code's structure through learned abstractions, leading to improved efficiency and accuracy in code understanding. The proposed method offers a significant advancement in code analysis techniques, particularly for large and complex software systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to program code optimization leveraging reinforcement learning (RL). Traditional static code analysis techniques often fall short in achieving optimal performance due to their lack of adaptability and global optimization capabilities. Our proposed system utilizes an RL agent to dynamically adjust code optimization strategies based on observed program behavior and runtime characteristics. The agent learns through trial and error, receiving rewards for improved performance metrics (e.g., execution time, memory usage) and penalties for detrimental changes. The system's adaptive nature allows it to effectively tailor optimization strategies to diverse environments and codebases, potentially surpassing the limitations of static analysis methods. We detail the system architecture, the RL algorithm employed (specifically, a Q-learning variant), and the key components involved in the optimization process. The core claim is the construction of an adaptive system capable of automatically optimizing program code performance based on the running environment and code characteristics. The mechanism utilizes reinforcement learning to adjust optimization strategies based on runtime and performance indicators, achieving self-adaptive optimization. This research explores a new paradigm in code optimization, moving beyond static analysis to a dynamic, learning-based approach.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the application of Multi-Agent Reinforcement Learning (MARL) to the problem of distributed resource management. Traditional approaches to this problem often rely on centralized control, which can be inefficient and vulnerable to single points of failure. This research proposes a decentralized solution utilizing MARL, where multiple agents, each responsible for managing a subset of resources, learn to coordinate and optimize overall system performance through interaction and reward signals. We formulate the resource management task as a multi-agent Markov Decision Process (MAMP), leveraging algorithms such as Independent Q-Learning (IQL) and Centralized Training with Decentralized Execution (CTDE) to train the agents. The core claim is that MARL offers a viable framework for distributed resource management. The mechanism relies on the agents learning optimal strategies through trial and error, adapting to changing conditions, and ultimately achieving greater efficiency and robustness compared to traditional methods. The novelty of this work lies in its exploration of MARL's potential in this domain, a field still in its nascent stages. The results demonstrate the feasibility and effectiveness of this approach.
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.
