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https://arxiv.org/html/2407.20798v1DIFFUSION AUGMENTED AGENTS: A FRAMEWORK FOR EFFICIENT EXPLORATION AND TRANSFER LEARNINGRL의 실제 환경에서의 data scarcity 문제기존의 학습된 knowledge에서 transfer, sample 효율성을 향상시키기LLM이 main controller, 즉 뇌처럼 역할LLM이 VLM, 디퓨전모델(DM)에 input을 즉 querying , 에이젼트의 high-level 행동을 이끔(guide) LLM을 통해 instruction으로 text description을 얻고 (decompose)VLM을 통해 observation과 텍스트 묘사를 임베딩으로 얻고 이를 코사인..

https://cybertronagent.github.io/Optimus-1.github.io/

https://arxiv.org/pdf/2307.07924v5https://github.com/OpenBMB/ChatDevchat-powered software- development framework를 의미 Technically, to facilitate cooperative communication,협력적 커뮤니케이션을 촉진시키기 위해 ChatDev introduces chat chain to further break down each phase into smaller and manageable subtasks,채팅 chain을 사용해 각 단계를 subtask로 나누는 which guides multi-turn communications between different roles to propose ..

https://arxiv.org/pdf/2406.04151가장 중요한것1) diverse environments for agent exploration and learning 에이전트의 탐색과 학습을 위한 다양한 환경 2) a trajectory set to equip agents with basic capabilities and prior knowledge에이전트에게 기본적인 능력과 사전 지식을 갖추게 하는 trajectory 집합3) an effective and scalable evolution method효과적이고 확장 가능한 진화 방법

가장 중요한점https://arxiv.org/pdf/2402.14034https://github.com/modelscope/agentscope GitHub - modelscope/agentscope: Start building LLM-empowered multi-agent applications in an easier way.Start building LLM-empowered multi-agent applications in an easier way. - modelscope/agentscopegithub.com following aspects feature the challenge1) Agents involved in a multi-agent application can specialize at diff..

https://arxiv.org/pdf/2402.16823노드는 오퍼레이션(LLM , tool 사용)edge는 에이젼트간의 커뮤니케이션+ The nodes implement functions to process multimodal data or query LLMs, and the edges describe the information flow between operations 2. GPTSwarm2.1. Language Agents as Graphs2.2. Graph Definitioninput x and context information z from its predecessor nodes by applying a computational routine f 2.3. Edge Optimization  2..

https://arxiv.org/pdf/2303.17760https://github.com/camel-ai/camel가장 중요한 것 : role-playing In particular, we conduct comprehensive studies on instruction-following cooperation in multi-agent settings.

기존의 한계Current methodologies for assigning role descriptions to autonomous agents predominantly involve manual assignment, necessitating prior knowledge and understanding of the task. Consequently, the scalability remains ambiguous, especially in the face of diverse and intricate problem contexts.Specifically, AGENTVERSE splits the problem-solving process into four pivotal stages as shown in Figu..

https://arxiv.org/pdf/2406.07155LLM의 power law와 같이 agent의 수를 늘린다고 해서 emergent capability가 생기는가?Inspired by the neural scaling law, a natural question arises: does increasing agents in multi-agent collaboration exhibit emergent capabilities? 3. Multi-Agent Collaboration Network This arrangement ensures that each node-occupied agent ai precedes its corresponding edge-occupied agent aij , and aij p..

1. Introduction예를 들어, 그림 1에서 Reflexion 에이전트는 시도, 오류 및 자기 성찰(trial, error, self-reflect)을 통해 의사결정, 프로그래밍 및 추론 작업을 해결하기 위해 자신의 행동을 최적화하는 방법을 학습한다. 유용한 성찰 피드백을 생성하는 것은 모델이 어디서 실수를 했는지에 대한 이해(즉, the credit assignment problem)뿐만 아니라 개선을 위한 actionable insight을 포함한 요약을 생성할 수 있는 능력을 필요로 하기 때문에 challenging 우리는 이를 수행하기 위해 세 가지 방법을 탐구한다 –1) simple binary environment feedback2) pre-defined heuristics for co..