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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

https://arxiv.org/abs/2408.03314(1) searching against dense, process-based verifier reward models; and(2) updating the model’s distribution over a response adaptively, given the prompt at test time. We find that in both cases, the effectiveness of different approaches to scaling test-time compute critically varies depending on the difficulty of the prompt모델 크기를 키우는 것과 test-time에 추가 계산을 수행하는 것 중 ..

https://openreview.net/pdf?id=Ep0TtjVoapa 는 CoT, b는 PAL ,c는 ToRA의 tool(PAL)을 통합한 rationale(CoT)을 활용imitation learningGPT4 같은 모델을 써서 만든 ToRA corpus로 모델 M 학습진행 output space shaping 모델 M의 ToRA를 샘플링 후 이를 teacher model에 evaluate, validate 후 수정된 trajectory 를 corpus로 사용

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..

https://arxiv.org/pdf/2407.18901