Best AI tools for< Rule-based Reasoning >

Bulk Rename Utility is a free online file renaming tool that combines AI-powered and rule-based operations to efficiently rename multiple files or folders. Users can choose between AI Mode, where they describe renaming needs to the AI, and Rule Mode, which offers customizable renaming methods. The tool supports various file operations, diverse renaming rules, and ensures user privacy by performing local operations and secure browsing. Bulk Rename Utility stands out for its user-friendly interface, advanced features, browser compatibility, and platform support, making it a versatile solution for batch file renaming tasks.

UiPath is a leading provider of robotic process automation (RPA) and artificial intelligence (AI) software. Its platform enables businesses to automate repetitive, rule-based tasks, freeing up employees to focus on more strategic initiatives. UiPath's AI capabilities allow businesses to further enhance their automation efforts by enabling robots to learn from data, make decisions, and interact with humans in a more natural way.

Syntho is a self-service AI-generated synthetic data platform that offers a comprehensive solution for generating synthetic data for various purposes. It provides tools for de-identification, test data management, rule-based synthetic data generation, data masking, and more. With a focus on privacy and accuracy, Syntho enables users to create synthetic data that mirrors real production data while ensuring compliance with regulations and data privacy standards. The platform offers a range of features and use cases tailored to different industries, including healthcare, finance, and public organizations.

Graphlogic.ai is an AI-powered platform that offers Conversational AI solutions through text and voice bots. It provides partner-enabled services for various industries, including HR, customer support, marketing, and internal task management. The platform features AI-powered chatbots with goal-oriented NLU and rule-based bots, seamless integrations with CRM systems, and 24/7 omnichannel availability. Graphlogic.ai aims to transform and speed up customer service and FAQ conversations by providing instant replies in a human-like manner. It also offers dedicated HR manager bots, hiring assistants for mass recruitment, responsible managers for internal tasks, and outbound marketing coordinators.

Social Share is an all-in-one social tool that allows users to create bio link pages, shorten links, generate QR codes, create vCard links, and generate file links. It is a comprehensive platform that provides users with everything they need to manage their social media presence and online marketing efforts.

Watchdog is an AI-powered chat moderation tool designed to fully automate chat moderation for Telegram communities. It helps community owners tackle rulebreakers, trolls, and spambots effortlessly, ensuring consistent rule enforcement and user retention. With features like automatic monitoring, customizable rule enforcement, and quick setup, Watchdog offers significant cost savings and eliminates the need for manual moderation. The tool is developed by Ben, a solo developer, who created it to address the challenges he faced in managing his own community. Watchdog aims to save time, money, and enhance user experience by swiftly identifying and handling rule violations.

Alston & Bird Privacy, Cyber & Data Strategy Blog is an AI tool that provides insights and updates on key data privacy, cybersecurity issues, and regulations. The blog covers a wide range of topics such as COPPA Rule amendments, AI advisory in healthcare, DHS Playbook for AI deployment, cybersecurity sanctions, and data breach notification laws. It aims to keep readers informed about the latest developments in privacy, cyber, and data strategy.

Ascent RLM is a regulatory lifecycle management platform that helps financial services companies identify, analyze, and manage regulatory obligations. It is composed of two integrated modules: AscentHorizon, a global horizon scanning tool, and AscentFocus, a regulatory mapping tool. Ascent RLM automates the regulatory mapping process, extracts individual obligations from regulatory text, and provides a centralized digital register of a firm's regulatory obligations. It also includes features such as side-by-side rule comparison, scenario planning, and audit trail.

Rapid Claims AI is an autonomous medical coding and documentation solution powered by AI technology. It aims to streamline medical coding operations, reduce administrative costs, improve reimbursements, and ensure compliance for healthcare providers. The platform offers features like automated coding, personalized solutions, actionable insights, and customizable AI rule sets. Rapid Claims AI is designed to seamlessly integrate into existing workflows, catering to various healthcare setups and specialties. The application prioritizes security and privacy, with data encryption and secure cloud storage. It serves as a valuable tool for enhancing revenue cycle management processes in the healthcare industry.

Idea Hunt is an AI tool designed to help users uncover sales leads on Reddit by finding potential customers who are interested in reacting to ideas, solutions, and products. It utilizes advanced AI technologies, including GPT-4, to provide users with a platform to discover trends, find customers, and receive feedback. Idea Hunt offers features such as discovering potential customer leads, customized matching prompts, self-service matching rule optimization, comment assistance, and generating new revenue. The platform assists users in engaging with Reddit users effectively and converting them into paying customers, making it a valuable marketing tool for startups and large companies alike.

This repository contains the source code and datasets for Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs accepted to ACL 2024. It proposes a framework called Graph Chain-of-thought (Graph-CoT) to enable Language Models to traverse graphs step-by-step for reasoning, interaction, and execution. The motivation is to alleviate hallucination issues in Language Models by augmenting them with structured knowledge sources represented as graphs.

Nucleoid is a declarative (logic) runtime environment that manages both data and logic under the same runtime. It uses a declarative programming paradigm, which allows developers to focus on the business logic of the application, while the runtime manages the technical details. This allows for faster development and reduces the amount of code that needs to be written. Additionally, the sharding feature can help to distribute the load across multiple instances, which can further improve the performance of the system.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

SWE-RL is the official codebase for the paper 'SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution'. It is the first approach to scale reinforcement learning based LLM reasoning for real-world software engineering, leveraging open-source software evolution data and rule-based rewards. The code provides prompt templates and the implementation of the reward function based on sequence similarity. Agentless Mini, a part of SWE-RL, builds on top of Agentless with improvements like fast async inference, code refactoring for scalability, and support for using multiple reproduction tests for reranking. The tool can be used for localization, repair, and reproduction test generation in software engineering tasks.

The Awesome-System2-Reasoning-LLM repository is dedicated to a survey paper titled 'From System 1 to System 2: A Survey of Reasoning Large Language Models'. It explores the development of reasoning Large Language Models (LLMs), their foundational technologies, benchmarks, and future directions. The repository provides resources and updates related to the research, tracking the latest developments in the field of reasoning LLMs.

Search-R1 is a tool that trains large language models (LLMs) to reason and call a search engine using reinforcement learning. It is a reproduction of DeepSeek-R1 methods for training reasoning and searching interleaved LLMs, built upon veRL. Through rule-based outcome reward, the base LLM develops reasoning and search engine calling abilities independently. Users can train LLMs on their own datasets and search engines, with preliminary results showing improved performance in search engine calling and reasoning tasks.

AgentSociety is an advanced framework designed for building agents in urban simulation environments. It integrates LLMs' planning, memory, and reasoning capabilities to generate realistic behaviors. The framework supports dataset-based, text-based, and rule-based environments with interactive visualization. It includes tools for interviews, surveys, interventions, and metric recording tailored for social experimentation.

OREAL is a reinforcement learning framework designed for mathematical reasoning tasks, aiming to achieve optimal performance through outcome reward-based learning. The framework utilizes behavior cloning, reshaping rewards, and token-level reward models to address challenges in sparse rewards and partial correctness. OREAL has achieved significant results, with a 7B model reaching 94.0 pass@1 accuracy on MATH-500 and surpassing previous 32B models. The tool provides training tutorials and Hugging Face model repositories for easy access and implementation.

This repository contains the implementation of Self-Play of Adversarial Language Game (SPAG) as described in the paper 'Self-playing Adversarial Language Game Enhances LLM Reasoning'. The SPAG involves training Language Models (LLMs) in an adversarial language game called Adversarial Taboo. The repository provides tools for imitation learning, self-play episode collection, and reinforcement learning on game episodes to enhance LLM reasoning abilities. The process involves training models using GPUs, launching imitation learning, conducting self-play episodes, assigning rewards based on outcomes, and learning the SPAG model through reinforcement learning. Continuous improvements on reasoning benchmarks can be observed by repeating the episode-collection and SPAG-learning processes.

Oat is a simple and efficient framework for running online LLM alignment algorithms. It implements a distributed Actor-Learner-Oracle architecture, with components optimized using state-of-the-art tools. Oat simplifies the experimental pipeline of LLM alignment by serving an Oracle online for preference data labeling and model evaluation. It provides a variety of oracles for simulating feedback and supports verifiable rewards. Oat's modular structure allows for easy inheritance and modification of classes, enabling rapid prototyping and experimentation with new algorithms. The framework implements cutting-edge online algorithms like PPO for math reasoning and various online exploration algorithms.

LLM Reasoners is a library that enables LLMs to conduct complex reasoning, with advanced reasoning algorithms. It approaches multi-step reasoning as planning and searches for the optimal reasoning chain, which achieves the best balance of exploration vs exploitation with the idea of "World Model" and "Reward". Given any reasoning problem, simply define the reward function and an optional world model (explained below), and let LLM reasoners take care of the rest, including Reasoning Algorithms, Visualization, LLM calling, and more!

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework enables LLMs to reason and interact with the environment, optimizing entire trajectories for long-horizon reasoning while maintaining computational efficiency.

RAGEN is a reinforcement learning framework designed to train reasoning-capable large language model (LLM) agents in interactive, stochastic environments. It addresses challenges such as multi-turn interactions and stochastic environments through a Markov Decision Process (MDP) formulation, Reason-Interaction Chain Optimization (RICO) algorithm, and progressive reward normalization strategies. The framework consists of MDP formulation, RICO algorithm with rollout and update stages, and reward normalization strategies to stabilize training. RAGEN aims to optimize reasoning and action strategies for LLM agents operating in complex environments.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

Avalon-LLM is a repository containing the official code for AvalonBench and the Avalon agent Strategist. AvalonBench evaluates Large Language Models (LLMs) playing The Resistance: Avalon, a board game requiring deductive reasoning, coordination, collaboration, and deception skills. Strategist utilizes LLMs to learn strategic skills through self-improvement, including high-level strategic evaluation and low-level execution guidance. The repository provides instructions for running AvalonBench, setting up Strategist, and conducting experiments with different agents in the game environment.

The W3C Cognitive AI Community Group focuses on advancing Cognitive AI through collaboration on defining use cases, open source implementations, and application areas. The group aims to demonstrate the potential of Cognitive AI in various domains such as customer services, healthcare, cybersecurity, online learning, autonomous vehicles, manufacturing, and web search. They work on formal specifications for chunk data and rules, plausible knowledge notation, and neural networks for human-like AI. The group positions Cognitive AI as a combination of symbolic and statistical approaches inspired by human thought processes. They address research challenges including mimicry, emotional intelligence, natural language processing, and common sense reasoning. The long-term goal is to develop cognitive agents that are knowledgeable, creative, collaborative, empathic, and multilingual, capable of continual learning and self-awareness.

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I generate creative business ideas based on a text about a problem, a news item, a topic, a reflection. The 3 Words Rule

Expert in malware analysis and Yara rules, using web sources for specifics.

Generate your eslint rule

Expert in simplified financial rule engines.

Formal and concise golf rule expert with current rulebook access.

NFL rules expert with clear and concise responses.

Your personal board game guide

Machiavellian advisor to help you rule in business, politics and finance.

MTG rule assistant

Combines rule analysis with media insights.

FRC expert with precise rule citations and tech advice.

College Football Data and Rule Analyst in your pocket

A golf rules expert referencing R&A rule book.

Provides campaign plots, character design and rule interpretations for tabletop RPG game masters.

Your Expert on the Official 2023 Golf Rules: Simply describe or upload an image of your play scenario, and receive precise, reliable guidance on the applicable rules. Perfect for players and enthusiasts seeking accurate and instant rule clarifications

Expert in Wayfarers of the South Tigris rules and strategies

Ah, Perry the Platypus, behold my new -inator!

Discover the 80/20 in any situation.