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The main features of ber666/rap are: Reasoning And Planning.
Open-source alternatives to ber666/rap include: amazon-science/mm-cot — This project is a multimodal large language model reasoning framework designed to train and evaluate models in… craftjarvis/jarvis-1 — [[Website]](http://craftjarvis-jarvis1.github.io/) [[Paper]](https://arxiv.org/abs/2311.05997)… craftjarvis/mc-planner. cranial-xix/llm-pddl — This repo contains the source code for making plans based on problems decribed by natural language. dinobby/reconcile. aiwaves-cn/agents — This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It…
This project is a multimodal large language model reasoning framework designed to train and evaluate models in performing chain-of-thought reasoning across text and image data. It provides a reasoning engine and training system that enable vision-language models to generate step-by-step logical rationales and final answers for complex queries. The framework utilizes a two-stage training pipeline that decouples the generation of logical justifications from final answer inference. It transforms visual data into descriptive text through image captioning and uses vision-transformer feature extrac
[Website](http://craftjarvis-jarvis1.github.io/) [Paper](https://arxiv.org/abs/2311.05997) [Twitter](https://twitter.com/jeasinema/status/1723900032653643796)
This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It functions as a computational graph orchestrator that organizes agent interactions and tool sequences as a trainable graph of nodes. The framework focuses on data-centric agent optimization, allowing agent pipelines and prompts to be upgraded through data-driven training rather than manual engineering. It utilizes a symbolic learning process that applies language-based loss and textual reflections to refine the operational logic and symbolic components of an agent. The system