Artificial intelligence (AI) has become a significant force in the field of quantitative trading because it extends traditional rule-based systems to areas such as adaptive prediction, dynamic configuration and automated execution. At the same time, the widespread adoption of machine learning, reinforcement learning, a...
Zi-Chong Long· Applied and Computational En...· 0 citations
The research findings show that RL has gradually expanded from simply improving the accuracy of the final answer to optimizing queries, multi-round search, process decision-making and trustworthy screening, providing new ideas for enhancing the active retrieval ability of RAG and improving the credibility of informatio...
Zun-Long Hong· Applied and Computational En...· 0 citations
This paper investigates the development path of large language model preference alignment techniques, focusing on the training mechanism of Reinforcement Learning from Human Feedback and its three-stage process, including supervised fine-tuning, reward model training, and PPO-based policy optimization.
Hong-Yu Jiang· Applied and Computational En...· 0 citations
The transition toward semantic-aware communication offers a paradigm shift for next-generation mobile networks, promising to decouple information significance from raw data transmission. Despite advances in semantic extraction, the integration of semantic intelligence into the Medium Access Control (MAC) layer remains...
Hamidreza Mazandarani, Masoud Shokrnezhad, T. Taleb· IEEE Transactions on Network...· 0 citations
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Adaptive Self-Design Algorithm is a novel algorithmic framework designed to automate the design and optimization of mathematical formulas, particularly within complex non-standard and nonlinear equation systems. This approach leverages a probabilistic search algorithm, incorporating reinforcement learning and genetic a...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to symbolic artificial intelligence (AI) that addresses the limitations of traditional, manually-engineered rule-based systems. The core challenge in symbolic AI has historically been the extensive and often laborious process of crafting and maintaining a comprehensive rule set. Exi...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces Neuro-Topology Adaptive Dynamics (NTAD), a novel approach to neural network design that leverages reinforcement learning to dynamically optimize the topology of a neural network. The core claim is that by adapting the network's connectivity structure in real-time, using algorithms like Proximal Po...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces the Adaptive Spatiotemporal Neural Models (ASPNM), a novel approach to time series prediction that addresses the limitations of traditional, static neural network models. The core idea is to design a neural network architecture capable of dynamically adjusting its internal parameters and behaviora...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of quantum self-organized networks (QSON) reinforcement learning, leveraging quantum entanglement and coherence to enhance learning strategies and improve the performance of reinforcement learning agents. Traditional reinforcement learning relies on classical computers, while this re...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
TOPO-RLHF: Full Summary Overview TOPO-RLHF is the first integration of topological permanence (TOPO-2026) with Reinforcement Learning from Human Feedback (RLHF), enabling existing AI models to acquire new capabilities without forgetting prior knowledge. Core Problem Solved Catastrophic forgetting (identified 1989) has...
FRANK MORALES· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to deep learning model compression called Adaptive Precision Quantization (APQ). The core idea is to dynamically adjust the quantization precision of individual layers or parameters during training, guided by a reinforcement learning agent. Unlike traditional fixed-point quantizatio...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Quantum error correction (QEC) is a crucial step towards fault-tolerant quantum computation. However, traditional QEC schemes often rely on fixed, pre-determined parameters, which may not be optimal for all quantum computations. This paper proposes a novel approach to QEC that leverages adaptive learning algorithms to...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
