Beyond Single-Agent: Survey of Collaboration, Attribution, and Self‑Evolution in LLM Multi‑Agents

This survey introduces the LIFE framework for LLM‑based multi‑agent systems, outlining four stages—from individual agent capabilities through collaborative structures, failure attribution, to systemic self‑evolution—while analyzing how role design, communication, and scheduling affect performance, error propagation, and adaptive improvement.

AI SurveyCollaborationFailure Attribution

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Beyond Single-Agent: Survey of Collaboration, Attribution, and Self‑Evolution in LLM Multi‑Agents

PaperAgent

Apr 21, 2026 · Artificial Intelligence

How to Understand Agents: From Resource‑Constrained Decisions to Contextual Cognition

This survey clarifies the essence of AI agents as resource‑limited sequential decision‑making and contextual‑cognition systems, introduces a formal definition, outlines a five‑stage evolution of large models, presents a four‑loop architecture, and illustrates the concepts with the OpenClaw agent case study.

AI SurveyAgent ArchitectureContextual Cognition

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How to Understand Agents: From Resource‑Constrained Decisions to Contextual Cognition

Data Party THU

Oct 9, 2025 · Artificial Intelligence

How Reinforcement Learning Is Transforming the Full Lifecycle of Large Language Models

This survey systematically reviews recent advances in applying reinforcement learning across the entire lifecycle of large language models, detailing methods, datasets, benchmarks, open‑source tools, and future challenges such as scalability, reward design, and evaluation standards.

AI SurveyLLM lifecycleRLVR

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How Reinforcement Learning Is Transforming the Full Lifecycle of Large Language Models

Architect

Mar 30, 2025 · Artificial Intelligence

What Is Retrieval-Augmented Generation? A Deep Dive into RAG Techniques

This article provides a comprehensive survey of Retrieval‑Augmented Generation (RAG), covering its basic principles, key components, seven technical variants, challenges, evaluation methods, and future research directions across multimodal, graph‑based, and agentic extensions.

AI SurveyKnowledge retrievalRAG

0 likes · 9 min read

What Is Retrieval-Augmented Generation? A Deep Dive into RAG Techniques