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2,613 papers
This paper explores a novel approach to discovering and validating fundamental physical laws using Multi-Agent Reinforcement Learning (MARL). The core idea is to leverage the interactions and competition among multiple agents within a simulated physical environment to autonomously identify and verify potential laws gov...
This paper introduces Algorithmic Topology, a novel approach to computational geometry centered on the design of self-consistent, self-repairing topology algorithms. Traditional methods often struggle with complex constructions, requiring significant manual intervention. Our work proposes a paradigm shift towards adapt...
This paper introduces a novel reinforcement learning algorithm, termed Adaptive Topological Network Structures (ATNS), designed to enhance network structure optimization within complex environments. Traditional reinforcement learning often relies on predefined reward functions, limiting adaptability. The algorithm leve...
Reinforcement learning (RL) has demonstrated remarkable success in various domains, yet its application is often hindered by the computational complexity associated with large state spaces. Traditional dynamic programming algorithms, such as Value Iteration and Policy Iteration, suffer severely from the curse of dimens...
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Quantum topology is a rapidly developing field with the potential to revolutionize quantum computing and information processing. It leverages the unique properties of spacetime to create topologically protected quantum states, offering enhanced resilience against noise and decoherence. The accurate modeling of quantum...
This paper investigates the potential of utilizing biofeedback signals, specifically electroencephalogram (EEG) and electrocardiogram (ECG) data, as inputs to design adaptive and personalized machine learning algorithms. The core concept involves translating biofeedback signals into adjustable parameters within a machi...
BACKGROUND Traditional rehabilitation medicine, primarily dependent on qualitative clinical assessment and static therapeutic protocols, faces significant challenges in scalability, objectivity, and dynamic adaptability. The integration of Artificial Intelligence (AI) is catalyzing a paradigm shift from "experience-dri...
We document Organismo Vivo, a multi-agent trading framework combining reinforcement learning agents with encoder-based regime detection, validated through walk-forward testing, preregistration, and adversarial auditing.
This paper explores a novel approach to program self-optimization, termed "Entropy-Driven Program Self-Optimization (EDPSO)." The core concept revolves around a program's ability to monitor and adapt its own execution based on the inherent information entropy within its processes. The system employs an "entropy-aware"...
This paper introduces Dynamic Topological Dependency Learning (TDTL), a novel approach to knowledge representation and reasoning that leverages reinforcement learning to dynamically construct and adapt an internal knowledge graph based on observed data and inference results. Unlike traditional methods that rely on pre-...
Automated theorem proving (ATP) aims to develop systems capable of mechanically proving mathematical theorems. Despite significant advancements, ATP systems often struggle with complex reasoning tasks, largely due to the inherent difficulty in representing and executing logical deduction rules. This work proposes a nov...
This paper presents a novel dynamic probability field model designed to simulate complex system behavior and predict future states by dynamically adjusting probability field parameters based on real-time data and environmental changes. Leveraging the integration of machine learning and reinforcement learning, this mode...
A Blog post by Ai2 on Hugging Face
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.
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.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
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