Search Results for "reasoning" - Page 11
Sort By:
Showing 271 open source projects for "reasoning"
View related business solutions-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
-
1
Graph Nets library
Build Graph Nets in Tensorflow
...This library implements the foundational ideas from DeepMind’s paper “Relational Inductive Biases, Deep Learning, and Graph Networks”, offering tools to explore relational reasoning and message-passing neural networks. Graph Nets supports both TensorFlow 1 and TensorFlow 2, working with CPU and GPU environments, and includes educational Jupyter demos for shortest path finding, sorting, and physical prediction tasks. The codebase emphasizes modularity, allowing users to easily define their own edge, node, and global update functions. -
2
ELI5
A library for debugging/inspecting machine learning classifiers
ELI5 is a Python library designed to help developers interpret, debug, and explain the predictions of machine learning models. The project focuses on improving model transparency by providing tools that visualize feature importance and prediction reasoning. It supports several popular machine learning frameworks including scikit-learn, XGBoost, LightGBM, CatBoost, and Keras. The library allows users to inspect model weights, analyze decision trees, and compute permutation feature importance for black-box models. It also provides specialized tools such as TextExplainer, which can highlight important words in text classification tasks to explain why a model produced a particular prediction. ... -
3
Video Nonlocal Net
Non-local Neural Networks for Video Classification
...Non-local blocks compute attention-like responses across all positions in space-time, allowing a feature at one frame and location to aggregate information from distant frames and regions. This formulation improves action recognition and spatiotemporal reasoning, especially for classes requiring context beyond short temporal windows. The repo provides training recipes and models for standard datasets, as well as ablations that show how many non-local blocks to insert and at which stages. Efficient implementations keep memory and compute manageable so the blocks can be added without rewriting the entire backbone. ... -
4
Madara
Middleware for distributed applications
...MADARA was originally developed as an agent-based middleware specifically for real-time, distributed artificial intelligence, but is now more general purpose for distributed timing, control, knowledge and reasoning, and quality-of-service. MADARA is composed of several tools and middleware, and the main entry point into the system is the Knowledge and Reasoning Language (KaRL) Engine, which provides a real-time scripting language for nanosecond execution times hooked into a flexible transport layer for distributed reasoning. The KaRL engine also supports object-oriented C++, Java, and Python programming through Containers, classes that provide abstractions and references for variable location within the KaRL Knowledge Base. ... -
Demo Series - Small Business Backup By VeeamWatch this on-demand demo series and learn how to protect your Microsoft 365 data with clear, simple, actionable steps that are easy to implement for businesses of all sizes.
-
5
DeepLearn
Implementation of research papers on Deep Learning+ NLP+ CV in Python
Welcome to DeepLearn. This repository contains an implementation of the following research papers on NLP, CV, ML, and deep learning. The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it. CV, transfer learning, representation learning. -
6
ANGie
Alice Next Generation (internet entity)
...Wallace and the ALICE AI Foundation and the PyAIML code base written by Cort Stratton, the ANGie project incorporates additional AIML sets, adds its own AIML to the set, adds new AIML tags and additional code to provide more dynamic responses and more logical case-based-reasoning. Reading through most AIML sets it seems like the authors' intention was to have a response to every input that a bot has ever seen. The ANGie project strives to have intelligent and sensible responses, but to allow the bot to have no response when the meaning of the input is inconclusive, when additional context would be required to properly respond, or in general for questions for which the bot is unprepared - in order to create a bot that is capable of carrying on basic conversations with a human similar to the sort of small talk that two humans might have. ... -
7
Autonomous Robot Simulator
Physically-accurate robotics simulator written in Python
...It's main purpose is to help researchers with to develop mobile manipulators and, in general, any multi-body system. It is open-source, modular, easy to learn and use, and can be a valuable tool in the process of robot design, in the development of control and reasoning algorithms, as well as in teaching and educational activities. -
8
OpenCyc is the open source version of the Cyc(r) technology, the world's largest and most complete general knowledge base and reasoning engine. A new release with even more content, better performance, and improved reliability will be up shortly.
-
9
Ask Minerva is a chat-bot that is intended to be used as a "Cardboard analyst". (IE: A person who a developer explains a problem to as a way of examining their own reasoning)
-
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
-
10
this project provides a Glue to Access GOFER Functionality through C and Python. Gofer ("Good For Equational Reasoning") is an implementation of the programming language Haskell intended for educational purposes and supporting a language based
-
11
A knowledge base engine for Python built on F-Logic. It uses the FLORA-2 reasoning engine and provides permanent storage through ZODB. Logic programming concepts like logical variables, facts, rules and queries are included.
-
12
The Discrete Event Calculus Reasoner is an open source program for performing automated commonsense reasoning using the discrete event calculus, a comprehensive and highly usable formalism for reasoning about action, change, space, and mental states.
-
13
HORUS is a system for knowledge acquisition, hypothesis generation, inference and learning. It is an interactive, internet environment accessible to a diverse community of users (public-access or membership basis) - see also UMKAILASH project for more.
-
14
Nemotron 3 Nano
LL model providing reasoning and conversational capabilities
...This architecture allows the system to maintain strong reasoning capabilities while improving throughput and reducing the computational cost associated with large context processing. The model is designed as a general-purpose language system capable of handling tasks such as chat interaction, coding assistance, document analysis, and instruction following. -
15
Nemotron 3 Super
Open language model developed by NVIDIA as part of Nemotron-3 family
...The model is optimized for building AI agents that must perform complex tasks such as planning, tool usage, coding assistance, and multi-step reasoning. -
16
Mistral Small 4
Model that fuses instruct, reasoning and agentic skills
...Mistral Small 4 models are built to handle tasks such as conversational AI, software development assistance, and reasoning-heavy problem solving, making them versatile tools for both developers and enterprise applications. -
17
Leanstral
Open-source code agent designed for Lean 4
...The model is built to understand and generate Lean 4 code, which is used to express complex mathematical constructs as well as formal software specifications. By focusing on theorem proving and formal reasoning, Leanstral represents a specialized direction within large language models, targeting domains that require strict correctness and logical rigor rather than general conversational tasks. It leverages modern large-scale architectures, likely incorporating mixture-of-experts techniques, to balance efficiency and capability while handling structured symbolic reasoning tasks. ... -
18
DeepSeek-V3.2-Speciale
High-compute ultra-reasoning model surpassing model surpassing GPT-5
DeepSeek-V3.2-Speciale is the high-compute, ultra-reasoning variant of DeepSeek-V3.2, designed specifically to push the boundaries of mathematical, logical, and algorithmic intelligence. It builds on the DeepSeek Sparse Attention (DSA) framework, delivering dramatically improved long-context efficiency while preserving full model quality. Unlike the standard version, Speciale is tuned exclusively for deep reasoning and therefore does not support tool-calling, focusing its full capacity on pure cognitive performance. ... -
19
DeepSeek-V3.2
High-efficiency reasoning and agentic intelligence model
DeepSeek-V3.2 is a cutting-edge large language model developed by DeepSeek-AI, focused on achieving high reasoning accuracy and computational efficiency for agentic tasks. It introduces DeepSeek Sparse Attention (DSA), a new attention mechanism that dramatically reduces computational overhead while maintaining strong long-context performance. Built with a scalable reinforcement learning framework, it reaches near-GPT-5 levels of reasoning and outperforms comparable models like DeepSeek-V3.1 and Gemini-3.0-Pro in advanced benchmarks. ... -
20
gpt-oss-20b
OpenAI’s compact 20B open model for fast, agentic, and local use
GPT-OSS-20B is OpenAI’s smaller, open-weight language model optimized for low-latency, agentic tasks, and local deployment. With 21B total parameters and 3.6B active parameters (MoE), it fits within 16GB of memory thanks to native MXFP4 quantization. Designed for high-performance reasoning, it supports Harmony response format, function calling, web browsing, and code execution. Like its larger sibling (gpt-oss-120b), it offers adjustable reasoning depth and full chain-of-thought visibility for better interpretability. It’s released under a permissive Apache 2.0 license, allowing unrestricted commercial and research use. ... -
21
OWLAND is an automated testing framework for description logic reasoners. It supports both ABox and TBox reasoning methods and is capable of running fully automated tests with any ontology and number of currently available reasoners.