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    Heyflyingpig/README.md

    Hi , I'm Heyflyingpig 👋

    专注于 LLMs 智能体开发 | 将大模型应用到生活中的方方面面：因果推断与智能体，多智能体应用探索

    About Me

    我是 DMIR 实验室 的一员，热衷于探索人工智能的前沿领域。

    目前专注于

    • Multi-agent Systems：探索多智能体协作架构，研究复杂任务环境下的智能体分工、通信机制与协同决策。
    • Agentic Applications：致力于构建端到端的智能体解决方案，将 LLM 的推理能力转化为解决实际问题的自主行动力。
    • LLMs Tools：开发增强大模型能力的工具链与中间件，涵盖 RAG（检索增强生成）、长短期记忆管理 (Memory) 及工具调用框架。
    • Causal Inference：结合大模型与因果科学，专注于自动因果发现、反事实推理及可解释性分析，提升 AI 的逻辑可靠性。

    Research Interests & Focus

    LLMsMulti-AgentRAGCausal Inference

    Working On

    ProjectDescriptionLink
    CausalAgent一个集成了 AGENT 的因果分析工具。它能够自动识别因果关系，生成专业的分析报告，并提供可交互的因果图谱。View Project

    Article

    1. CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference(IUI2026)
    • DOI: 10.1145/3742414.3794777
    • Contributors: Jiawei Zhu; Wei Chen; Ruichu Cai
    1. A Report on the llms evaluating the high school questions
    1. VIGOR+: Iterative Confounder Generation and Validation via LLM-CEVAE Feedback Loop

    Connect with Me

    Pranesh's GitHub stats

    Pinned Loading

    1. CausalAgentCausalAgentPublic

      这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图

      Python 32 9

    , 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
     blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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      Heyflyingpig/README.md
      Hi , I'm Heyflyingpig 👋
      专注于 LLMs 智能体开发 | 将大模型应用到生活中的方方面面：因果推断与智能体，多智能体应用探索
      About Me
      我是 DMIR 实验室 的一员，热衷于探索人工智能的前沿领域。
      目前专注于
      • Multi-agent Systems：探索多智能体协作架构，研究复杂任务环境下的智能体分工、通信机制与协同决策。
      • Agentic Applications：致力于构建端到端的智能体解决方案，将 LLM 的推理能力转化为解决实际问题的自主行动力。
      • LLMs Tools：开发增强大模型能力的工具链与中间件，涵盖 RAG（检索增强生成）、长短期记忆管理 (Memory) 及工具调用框架。
      • Causal Inference：结合大模型与因果科学，专注于自动因果发现、反事实推理及可解释性分析，提升 AI 的逻辑可靠性。
      Research Interests & Focus
      LLMsMulti-AgentRAGCausal Inference
      Working On
      ProjectDescriptionLink
      CausalAgent一个集成了 AGENT 的因果分析工具。它能够自动识别因果关系，生成专业的分析报告，并提供可交互的因果图谱。View Project
      Article
      1. CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference(IUI2026)
      • DOI: 10.1145/3742414.3794777
      • Contributors: Jiawei Zhu; Wei Chen; Ruichu Cai
      1. A Report on the llms evaluating the high school questions
      1. VIGOR+: Iterative Confounder Generation and Validation via LLM-CEVAE Feedback Loop
      Connect with Me
      Pranesh's GitHub stats
      
Pinned
Loading
      1. CausalAgentCausalAgentPublic
        
这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图

        Python
32

9

      
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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        Achievement: Pull Sharkx2Achievement: YOLOAchievement: Starstruck
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        Heyflyingpig/README.md
        Hi , I'm Heyflyingpig 👋
        专注于 LLMs 智能体开发 | 将大模型应用到生活中的方方面面：因果推断与智能体，多智能体应用探索
        About Me
        我是 DMIR 实验室 的一员，热衷于探索人工智能的前沿领域。
        目前专注于
        • Multi-agent Systems：探索多智能体协作架构，研究复杂任务环境下的智能体分工、通信机制与协同决策。
        • Agentic Applications：致力于构建端到端的智能体解决方案，将 LLM 的推理能力转化为解决实际问题的自主行动力。
        • LLMs Tools：开发增强大模型能力的工具链与中间件，涵盖 RAG（检索增强生成）、长短期记忆管理 (Memory) 及工具调用框架。
        • Causal Inference：结合大模型与因果科学，专注于自动因果发现、反事实推理及可解释性分析，提升 AI 的逻辑可靠性。
        Research Interests & Focus
        LLMsMulti-AgentRAGCausal Inference
        Working On
        ProjectDescriptionLink
        CausalAgent一个集成了 AGENT 的因果分析工具。它能够自动识别因果关系，生成专业的分析报告，并提供可交互的因果图谱。View Project
        Article
        1. CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference(IUI2026)
        • DOI: 10.1145/3742414.3794777
        • Contributors: Jiawei Zhu; Wei Chen; Ruichu Cai
        1. A Report on the llms evaluating the high school questions
        1. VIGOR+: Iterative Confounder Generation and Validation via LLM-CEVAE Feedback Loop
        Connect with Me
        Pranesh's GitHub stats
        
Pinned
Loading
        1. CausalAgentCausalAgentPublic
          
这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图

          Python
32

9

        
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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          Achievement: Pull Sharkx2Achievement: YOLOAchievement: Starstruck
          Achievements
          Achievement: Pull Sharkx2Achievement: YOLOAchievement: Starstruck
          
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          Heyflyingpig/README.md
          Hi , I'm Heyflyingpig 👋
          专注于 LLMs 智能体开发 | 将大模型应用到生活中的方方面面：因果推断与智能体，多智能体应用探索
          About Me
          我是 DMIR 实验室 的一员，热衷于探索人工智能的前沿领域。
          目前专注于
          • Multi-agent Systems：探索多智能体协作架构，研究复杂任务环境下的智能体分工、通信机制与协同决策。
          • Agentic Applications：致力于构建端到端的智能体解决方案，将 LLM 的推理能力转化为解决实际问题的自主行动力。
          • LLMs Tools：开发增强大模型能力的工具链与中间件，涵盖 RAG（检索增强生成）、长短期记忆管理 (Memory) 及工具调用框架。
          • Causal Inference：结合大模型与因果科学，专注于自动因果发现、反事实推理及可解释性分析，提升 AI 的逻辑可靠性。
          Research Interests & Focus
          LLMsMulti-AgentRAGCausal Inference
          Working On
          ProjectDescriptionLink
          CausalAgent一个集成了 AGENT 的因果分析工具。它能够自动识别因果关系，生成专业的分析报告，并提供可交互的因果图谱。View Project
          Article
          1. CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference(IUI2026)
          • DOI: 10.1145/3742414.3794777
          • Contributors: Jiawei Zhu; Wei Chen; Ruichu Cai
          1. A Report on the llms evaluating the high school questions
          1. VIGOR+: Iterative Confounder Generation and Validation via LLM-CEVAE Feedback Loop
          Connect with Me
          Pranesh's GitHub stats
          
Pinned
Loading
          1. CausalAgentCausalAgentPublic
            
这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图

            Python
32

9

          
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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            Heyflyingpig/README.md
            Hi , I'm Heyflyingpig 👋
            专注于 LLMs 智能体开发 | 将大模型应用到生活中的方方面面：因果推断与智能体，多智能体应用探索
            About Me
            我是 DMIR 实验室 的一员，热衷于探索人工智能的前沿领域。
            目前专注于
            • Multi-agent Systems：探索多智能体协作架构，研究复杂任务环境下的智能体分工、通信机制与协同决策。
            • Agentic Applications：致力于构建端到端的智能体解决方案，将 LLM 的推理能力转化为解决实际问题的自主行动力。
            • LLMs Tools：开发增强大模型能力的工具链与中间件，涵盖 RAG（检索增强生成）、长短期记忆管理 (Memory) 及工具调用框架。
            • Causal Inference：结合大模型与因果科学，专注于自动因果发现、反事实推理及可解释性分析，提升 AI 的逻辑可靠性。
            Research Interests & Focus
            LLMsMulti-AgentRAGCausal Inference
            Working On
            ProjectDescriptionLink
            CausalAgent一个集成了 AGENT 的因果分析工具。它能够自动识别因果关系，生成专业的分析报告，并提供可交互的因果图谱。View Project
            Article
            1. CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference(IUI2026)
            • DOI: 10.1145/3742414.3794777
            • Contributors: Jiawei Zhu; Wei Chen; Ruichu Cai
            1. A Report on the llms evaluating the high school questions
            1. VIGOR+: Iterative Confounder Generation and Validation via LLM-CEVAE Feedback Loop
            Connect with Me
            Pranesh's GitHub stats
            
Pinned
Loading
            1. CausalAgentCausalAgentPublic
              
这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图

              Python
32

9

            
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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              Achievement: Pull Sharkx2Achievement: YOLOAchievement: Starstruck
              
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              Heyflyingpig/README.md
              Hi , I'm Heyflyingpig 👋
              专注于 LLMs 智能体开发 | 将大模型应用到生活中的方方面面：因果推断与智能体，多智能体应用探索
              About Me
              我是 DMIR 实验室 的一员，热衷于探索人工智能的前沿领域。
              目前专注于
              • Multi-agent Systems：探索多智能体协作架构，研究复杂任务环境下的智能体分工、通信机制与协同决策。
              • Agentic Applications：致力于构建端到端的智能体解决方案，将 LLM 的推理能力转化为解决实际问题的自主行动力。
              • LLMs Tools：开发增强大模型能力的工具链与中间件，涵盖 RAG（检索增强生成）、长短期记忆管理 (Memory) 及工具调用框架。
              • Causal Inference：结合大模型与因果科学，专注于自动因果发现、反事实推理及可解释性分析，提升 AI 的逻辑可靠性。
              Research Interests & Focus
              LLMsMulti-AgentRAGCausal Inference
              Working On
              ProjectDescriptionLink
              CausalAgent一个集成了 AGENT 的因果分析工具。它能够自动识别因果关系，生成专业的分析报告，并提供可交互的因果图谱。View Project
              Article
              1. CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference(IUI2026)
              • DOI: 10.1145/3742414.3794777
              • Contributors: Jiawei Zhu; Wei Chen; Ruichu Cai
              1. A Report on the llms evaluating the high school questions
              1. VIGOR+: Iterative Confounder Generation and Validation via LLM-CEVAE Feedback Loop
              Connect with Me
              Pranesh's GitHub stats
              
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              1. CausalAgentCausalAgentPublic
                
这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图

                Python
32

9

              
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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                Heyflyingpig/README.md
                Hi , I'm Heyflyingpig 👋
                专注于 LLMs 智能体开发 | 将大模型应用到生活中的方方面面：因果推断与智能体，多智能体应用探索
                About Me
                我是 DMIR 实验室 的一员，热衷于探索人工智能的前沿领域。
                目前专注于
                • Multi-agent Systems：探索多智能体协作架构，研究复杂任务环境下的智能体分工、通信机制与协同决策。
                • Agentic Applications：致力于构建端到端的智能体解决方案，将 LLM 的推理能力转化为解决实际问题的自主行动力。
                • LLMs Tools：开发增强大模型能力的工具链与中间件，涵盖 RAG（检索增强生成）、长短期记忆管理 (Memory) 及工具调用框架。
                • Causal Inference：结合大模型与因果科学，专注于自动因果发现、反事实推理及可解释性分析，提升 AI 的逻辑可靠性。
                Research Interests & Focus
                LLMsMulti-AgentRAGCausal Inference
                Working On
                ProjectDescriptionLink
                CausalAgent一个集成了 AGENT 的因果分析工具。它能够自动识别因果关系，生成专业的分析报告，并提供可交互的因果图谱。View Project
                Article
                1. CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference(IUI2026)
                • DOI: 10.1145/3742414.3794777
                • Contributors: Jiawei Zhu; Wei Chen; Ruichu Cai
                1. A Report on the llms evaluating the high school questions
                1. VIGOR+: Iterative Confounder Generation and Validation via LLM-CEVAE Feedback Loop
                Connect with Me
                Pranesh's GitHub stats
                
Pinned
Loading
                1. CausalAgentCausalAgentPublic
                  
这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图

                  Python
32

9

                
, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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                  Achievement: Pull Sharkx2Achievement: YOLOAchievement: Starstruck
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                  Heyflyingpig/README.md
                  Hi , I'm Heyflyingpig 👋
                  专注于 LLMs 智能体开发 | 将大模型应用到生活中的方方面面：因果推断与智能体，多智能体应用探索
                  About Me
                  我是 DMIR 实验室 的一员，热衷于探索人工智能的前沿领域。
                  目前专注于
                  • Multi-agent Systems：探索多智能体协作架构，研究复杂任务环境下的智能体分工、通信机制与协同决策。
                  • Agentic Applications：致力于构建端到端的智能体解决方案，将 LLM 的推理能力转化为解决实际问题的自主行动力。
                  • LLMs Tools：开发增强大模型能力的工具链与中间件，涵盖 RAG（检索增强生成）、长短期记忆管理 (Memory) 及工具调用框架。
                  • Causal Inference：结合大模型与因果科学，专注于自动因果发现、反事实推理及可解释性分析，提升 AI 的逻辑可靠性。
                  Research Interests & Focus
                  LLMsMulti-AgentRAGCausal Inference
                  Working On
                  ProjectDescriptionLink
                  CausalAgent一个集成了 AGENT 的因果分析工具。它能够自动识别因果关系，生成专业的分析报告，并提供可交互的因果图谱。View Project
                  Article
                  1. CausalAgent: A Conversational Multi-Agent System for End-to-End Causal Inference(IUI2026)
                  • DOI: 10.1145/3742414.3794777
                  • Contributors: Jiawei Zhu; Wei Chen; Ruichu Cai
                  1. A Report on the llms evaluating the high school questions
                  1. VIGOR+: Iterative Confounder Generation and Validation via LLM-CEVAE Feedback Loop
                  Connect with Me
                  Pranesh's GitHub stats
                  
Pinned
Loading
                  1. CausalAgentCausalAgentPublic
                    
这是一个由LangGraph协议主导的因果分析Muti-Agent，结合MCP,RAG等多种工具进行辅助进行因果分析，提供给用户一份完善的因果分析的分析报告和因果图

                    Python
32

9