Meta, 자체 개선 AI인 HyperAgent 출시
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#ai
#ai 딜
#anthropic
#api
#gemini
#hyperagent
#meta
#openai
#자체개선
원문 출처: hackernews · Genesis Park에서 요약 및 분석
요약
메타는 자체 개선과 성능 향상을 위해 기초 모델을 활용하는 AI 에이전트 구조인 '하이퍼에이전트(HyperAgents)'를 공개했습니다. 이 프로젝트는 메타 에이전트와 작업 에이전트를 구현하고 다양한 도메인에서 알고리즘을 실행할 수 있는 코드를 제공하며, 사용자는 도커를 이용해 쉽게 구축할 수 있습니다. 단, 연구팀은 실행되는 코드가 모델에 의해 생성되는 만큼 잠재적인 보안 위험과 파괴적 행동 가능성에 대해 주의를 당부했습니다.
본문
# API keys, put these into .env file OPENAI_API_KEY=... ANTHROPIC_API_KEY=... GEMINI_API_KEY=... # Install things sudo dnf install -y python3.12-devel sudo dnf install -y graphviz graphviz-devel cmake ninja-build bzip2-devel zlib-devel ncurses-devel libffi-devel # Create virtual environment python3.12 -m venv venv_nat source venv_nat/bin/activate pip install -r requirements.txt pip install -r requirements_dev.txt # To build the docker container docker build --network=host -t hyperagents . # Setup initial agents bash ./setup_initial.sh # See the script for args, and baseline selections python generate_loop.py --domains By default, outputs will be saved in outputs/ directory. agent/ code for using foundation modelsanalysis/ scripts used for plotting and analysisdomains/ code for each domainutils/ common code used in the reporun_meta_agent.py script to help run the meta agent and get the diffsmeta_agent.py main implementation of the meta agenttask_agent.py main implementation of the task agentgenerate_loop.py entry point for running the algorithm The experiment logs are stored as a multi-part ZIP archive. To extract them, ensure all .z01, .z02, etc., files are in the same directory as the .zip file, then run: zip -s 0 outputs_os_parts.zip --out unsplit_logs.zip unzip unsplit_outputs.zip Warning This repository involves executing untrusted, model-generated code. We strongly advise users to be aware of the associated safety risks. While it is highly unlikely that such code will perform overtly malicious actions under our current settings and with the models we use, it may still behave destructively due to limitations in model capability or alignment. By using this repository, you acknowledge and accept these risks. If you find this project useful, please consider citing: @misc{zhang2026hyperagents, title={Hyperagents}, author={Jenny Zhang and Bingchen Zhao and Wannan Yang and Jakob Foerster and Jeff Clune and Minqi Jiang and Sam Devlin and Tatiana Shavrina}, year={2026}, eprint={2603.19461}, archivePrefix={arXiv}, primaryClass={cs.AI}, url={https://arxiv.org/abs/2603.19461}, }
Genesis Park 편집팀이 AI를 활용하여 작성한 분석입니다. 원문은 출처 링크를 통해 확인할 수 있습니다.
공유