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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
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
The main features of craftjarvis/mc-planner are: Manipulation and Control, Reasoning And Planning, Task Automation Agents.
Projects with overlapping indexed features include: aiwaves-cn/agents — This is an LLM agent framework and symbolic learning system designed for building self-evolving autonomous agents. It… allenai/embodied-clip. amazon-science/mm-cot — This project is a multimodal large language model reasoning framework designed to train and evaluate models in… ber666/rap. canner/wrenai — WrenAI is a platform designed to enable natural language interaction with relational and analytical databases. By… ahundt/robotsenactmalignantstereotypes.