Neural intel Pod

This document introduces Nash Mirror Prox (NashMP), a novel algorithm designed to improve Large Language Model (LLM) alignment with human preferences. Traditional methods, often relying on Reinforcem…

The document introduces MiniMax-M1, a novel open-weight large-scale reasoning model designed for efficient processing of extensive inputs and complex tasks. This model integrates a hybrid Mixture-of-…

This document introduces Direct Reasoning Optimization (DRO), a novel reinforcement learning framework designed to enhance the reasoning abilities of Large Language Models (LLMs) in open-ended, long-…

This document explores the integration of AI agents into the workplace, analyzing both worker desires and technological capabilities. It introduces the WORKBank database, a novel resource compiling f…

The provided sources introduce LLaMA Factory, a powerful and user-friendly platform designed to simplify the process of training and fine-tuning large language models (LLMs). The YouTube video offers…

The source details Anthropic's "Project Vend," an experiment where their AI model, Claude Sonnet 3.7 (nicknamed "Claudius"), was tasked with autonomously managing a small, automated shop for a month.…

The provided text describes Self-Adapting Language Models (SEAL), a novel framework enabling large language models (LLMs) to learn and improve autonomously. Unlike static models, SEAL empowers LLMs t…

The provided text, a commentary on Shojaee et al. (2025), challenges claims that Large Reasoning Models (LRMs)exhibit fundamental reasoning failures on planning puzzles. The author argues that observ…

This academic paper explores the strengths and limitations of Large Reasoning Models (LRMs) compared to standard Large Language Models (LLMs), specifically in problem-solving scenarios of varying com…

This academic paper introduces "minimum attention" as a novel regularization technique applied to meta-reinforcement learning (meta-RL), particularly within model-based RL frameworks. The authors int…

This research paper examines the ethical and societal implications of Reinforcement Learning from Human Feedback (RLHF) in generative Large Language Models (LLMs), such as ChatGPT and Claude. It argu…

The provided source explores enhancing assembly code performance using large language models (LLMs) through reinforcement learning (RL). It introduces a novel RL framework that trains LLMs with Proxi…

The provided text describes FileFix, a social engineering technique that leverages the File Explorer address bar to execute malicious PowerShell commands. This method tricks users into copying what a…

This paper introduces a novel framework for offline reinforcement learning (RL), specifically addressing challenges in scenarios with continuous action spaces and unmeasured confounding variables. Th…

This document outlines a novel deep reinforcement learning (DRL) framework for optimizing the placement of air purification booths in metropolitan areas, using Delhi, India as a case study. The resea…

This academic paper introduces Non-Stationary Natural Actor-Critic (NS-NAC), a novel model-free, policy-based reinforcement learning algorithm designed for time-varying environments where rewards and…

This document introduces a novel framework for offline reinforcement learning (RL), focusing on optimizing individual policies when data comes from diverse or heterogeneous populations. The authors p…

This article from Amazon Science, published in May 2025, focuses on machine learning and conversational AI, specifically addressing improvements in reinforcement learning with human feedback (RLHF) f…

This document introduces AXIOM, a novel artificial intelligence architecture designed to learn how to play games efficiently using object-centric models and active inference. Unlike traditional deep …

This academic paper explores a critical issue in reinforcement learning (RL) with large language models (LLMs): the rapid decline of policy entropy, which limits the models' ability to explore and im…