#multi-agent debate
93 related articles
·4 min
AIML Capstone Project Selection: Is a Multi-Agent Debate System Worth Building?
Torn over your capstone topic? This article analyzes the academic value, feasibility, and innovation potential of a Multi-agent Debate system to help AIML students decide.
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·3 min
From Linear Pipelines to State Machines: A Practical Guide to Advanced Agent Architecture
Learn how to advance from linear pipeline to state machine Agent architecture through a YouTube script-to-storyboard case study, covering fault tolerance, LLM evaluation frameworks, and LangGraph vs AutoGen selection.
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·3 min
EMNLP 2026 Launches AI Reviewing Experiment: A Turning Point for Academic Peer Review
EMNLP 2026 introduces AI-generated reviews in ACL Rolling Review, exploring LLM-assisted academic peer review. Analysis of the experiment's background, mechanics, controversies, and implications.
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·5 min
The Complete Guide to AI Agent Development: A Four-Stage Progressive Roadmap
A systematic guide to AI Agent development across four stages: LLM fundamentals, ReAct paradigm, memory & tools, and multi-agent collaboration for developers.
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·5 min
Will Chinese Open-Source Models Burst the US AI Bubble? A Deep Dive into the Transmission Chain
Chinese open-source models like Kimi K3 and DeepSeek approach US closed-source performance at a fraction of the cost. This deep dive analyzes the transmission chain from price competition to valuation reassessment.
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·3 min
Context Engineering: Why Does More Context Make AI Dumber?
Anthropic introduces Context Engineering, revealing Context Rot: the more tokens in the window, the worse AI retrieval accuracy. Learn the three principles, just-in-time retrieval, and three moves against context overflow.
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·4 min
Burning Through 80 Billion Tokens: Why Vibe Coding Is Doomed to Fail
A creator spent 40 days and 80 billion tokens testing the real limits of Vibe Coding. This article dissects why AI programming crashes in production: complexity, context limits, and compression loss.
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·2 min
Can One Skill Transform All Skills? The Boundary Debate Between Dify Workflows and Agents
A recursive technical proposition: Can we build a "meta-Skill" that auto-transforms any Skill into a Dify workflow? This article dissects the boundary between deterministic orchestration and autonomous Agent decisions.
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·3 min
The Rise of General AI: Breakthroughs in Mathematical Reasoning and Competitive Programming
Frontier AI is going general: costs are dropping, general models are beating specialized ones in math and competitive programming, and multi-agent workflows are maturing fast.
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·8 min
LangChain Crash Course: A Complete Roadmap from Zero to Agent Development
A systematic roadmap from LangChain and LangGraph to multi-agent development, covering RAG, Tool Calling, MCP, and more, helping developers break into AI app development.
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Human-Centered AI: Real-World Implemen…
·6 min
Human-Centered AI: Real-World Implementation in Healthcare and Design
An MSR workshop reveals the truth about AI deployment: from a $20 corneal diagnostic device to expert-in-the-loop chatbots, researchers share real-world experiences of AI in healthcare and design within resource-scarce environments.
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·4 min
Teaching AI to Have "Taste": Exploring Multi-Agent Design with Open-Source Skill Taste
Why does AI-generated UI all look the same? Designer SEN open-sourced a Skill called Taste that uses a four-agent pipeline to reverse-engineer website "taste" into verifiable design principles called Taste DNA.
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·3 min
Coze Deep Dive: A Complete Guide to the Agent Building Platform vs. Dify
A deep dive into ByteDance's Coze platform: tool categories, positioning vs. Dify, skill store, multi-agent collaboration, and workflow building — your AI Agent selection guide.
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·3 min
Codex vs Fable: Which AI Agent Is Better at Finding Real Pain Points?
Codex vs Fable in an open-ended problem space: Codex delivers flawless execution but plays it safe; Fable shows sharp strategic vision but lands too narrow. Here's how to combine both.
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·3 min
AI Agent 4-Week Learning Roadmap: A Complete Guide from Zero to Independent Development
A structured 4-week AI Agent learning roadmap: Week 1 covers LLMs & Prompt engineering, Week 2 ReAct paradigms, Week 3 RAG memory systems, Week 4 multi-agent architectures.
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·2 min
TradingAgents-astock: A Practical Guide to the Multi-Agent Research Framework for A-Share Markets
TradingAgents-astock is a multi-agent A-share research framework with free domestic data sources, seven debating analyst agents, and support for DeepSeek, Qwen, and more.
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·2 min
Local Models & Open-Source Toolchains: How to Protect Data Sovereignty in the AI Era
Learn how local LLMs (Llama, Mistral, Qwen) and open-source toolchains protect your data sovereignty, reduce platform dependency, and give you full control over AI workflows.
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·2 min
AI Agent Beginner's Guide: A Structured Learning Path and Hands-On Project Roadmap
New to AI Agents? This guide breaks down the full learning path — covering Agent principles, Prompt Engineering, RAG, multi-Agent systems, and hands-on projects to get you building fast.
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Codex Encrypts Sub-Agent Prompts: The …
·2 min
Codex Encrypts Sub-Agent Prompts: The AI Black-Box Trend and What Developers Should Do
OpenAI Codex is encrypting sub-agent prompts, sparking debate about AI transparency. We analyze the motivations, community concerns, and strategies for developers navigating the black-box trend.
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·3 min
Meta Muse Spark 1.1 Deep Dive: The Platform Ambitions Behind Meta's First Closed-Source Model
Meta Muse Spark 1.1 deep dive: native multimodal architecture, platform tools, social data retrieval, e-commerce vision — Meta's first closed-source API model benchmarks against Anthropic Sonnet.
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