{"id":6667,"date":"2026-10-27T08:10:39","date_gmt":"2026-10-27T08:10:39","guid":{"rendered":"https:\/\/i10xnews.kinsta.cloud\/google-ai-bcmacc-decentralized-llm-collaboration"},"modified":"2026-10-27T08:10:39","modified_gmt":"2026-10-27T08:10:39","slug":"google-ai-bcmacc-decentralized-llm-collaboration","status":"publish","type":"post","link":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration","title":{"rendered":"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">The push toward swarms of autonomous AI agents has hit a snag. Individual models often can&#8217;t coordinate effectively without a central manager calling the shots, and that setup is slow and costly. Researchers at Google AI are addressing this with a new &#8220;belief-conditioned&#8221; training approach. It lets decentralized LLMs figure out what their peers are up to based on minimal communication, opening the door to quicker and more scalable multi-agent setups.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u26a1 Quick Take<\/h2>\n\n\n\n<ul class=\"wp-block-list\"><li><strong>What happened:<\/strong> Google AI researchers introduced BCMAAC, a reinforcement learning method that teaches decentralized LLM agents to collaborate by maintaining &#8220;belief states&#8221; about their peers&#8217; actions.<\/li><li><strong>Why it matters now:<\/strong> Current multi-agent architectures rely on centralized orchestration, which throttles parallel inference and drives up compute costs; true decentralization is the key to scaling agentic workflows.<\/li><li><strong>The technical shift:<\/strong> Instead of passing every message through a central graph or critic, agents learn to infer missing context from sparse communication traces, adapting techniques from classical Multi-Agent Reinforcement Learning (MARL).<\/li><li><strong>The bottom line:<\/strong> If AI is moving from chat interfaces to asynchronous, autonomous task forces, models need the ability to collaborate in the dark without constant supervision.<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">What happened<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A team of researchers recently unveiled the Belief-Conditioned Multi-Agent Actor-Critic (BCMAAC) method, a new approach designed to solve the coordination problem in decentralized LLM systems. Accepted at the NeurIPS Workshop PTA, the research tackles a core challenge: how multiple AI agents can work together on complex, multi-turn tasks like web searching, tool usage, and Minecraft gameplay when they only have access to partial information.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Instead of relying on a centralized controller that dictates every move, BCMAAC equips individual agents with the ability to maintain &#8220;belief states.&#8221; By analyzing limited communication traces\u2014essentially reading between the lines of what other agents are saying and doing\u2014each LLM infers the broader hidden context of the task.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The method uses an actor-critic reinforcement learning framework to train these agents. During the training phase, the system rewards LLMs that successfully deduce the collaborative context, effectively teaching them to act independently while still dynamically adjusting to serve a shared objective.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why it matters now<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The AI ecosystem is currently obsessed with &#8220;agentic&#8221; workflows. Startups and tech giants alike are trying to move beyond single-prompt LLMs to swarms of specialized agents that can write code, debug, and execute software autonomously. However, existing multi-agent setups run into severe scaling limits. Whether they use centralized critics or heavy graph-based message routing, centralized execution protocols choke parallel inference, driving up latency and API costs.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">By pushing the coordination logic down to the individual decentralized agent, BCMAAC introduces a paradigm shift. If an LLM can infer what its counterpart is doing without needing a master orchestrator to pass the exact state back and forth, communication overhead drops sharply. This is exactly what is needed for asynchronous, distributed AI workforces that operate at scale without causing massive compute gridlocks.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Furthermore, this signals a deeper merging of classical Multi-Agent Reinforcement Learning (MARL) with modern generative AI. Until recently, LLM collaboration was mostly managed via prompt engineering and rigid, human-designed pipelines. Now, researchers are treating LLMs as true RL agents navigating environments with &#8220;partial observability&#8221; and sparse rewards. This leap fundamentally changes how intelligence will be orchestrated in the cloud, moving from rigid routing graphs to organic, decentralized teamwork.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Who is most affected<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The immediate impact of decentralized collaboration frameworks hits the builders of AI infrastructure, developer tooling, and enterprise agent deployments. As orchestration shifts from centralized routers to the models themselves, companies building agent frameworks will need to adapt to environments where agents are expected to hold and update their own internal &#8220;beliefs&#8221; about their peers.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table class=\"has-fixed-layout\"><thead><tr><th>Stakeholder<\/th><th>Impact<\/th><th>Why<\/th><\/tr><\/thead><tbody><tr><td><strong>AI Framework Developers<\/strong><\/td><td>High<\/td><td>Must transition from building strict centralized orchestration pipelines to supporting decentralized, asynchronous agent execution.<\/td><\/tr><tr><td><strong>Compute &amp; Cloud Providers<\/strong><\/td><td>Medium<\/td><td>Decentralized execution enables massive parallel inference, altering how workloads are batched and potentially smoothing out compute bottlenecks.<\/td><\/tr><tr><td><strong>Applied AI Startups<\/strong><\/td><td>High<\/td><td>Unlocks the ability to deploy cheaper, faster multi-agent teams for complex enterprise tasks by removing the token overhead of constant central monitoring.<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">\u270d\ufe0f About the analysis<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This is an independent i10x analysis designed for AI developers, CTOs, and infrastructure builders, examining the shift toward decentralized agent architectures based on recent academic research.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li><a href=\"https:\/\/arxiv.org\/html\/2610.35398\" target=\"_blank\" rel=\"noreferrer noopener\">Belief-Conditioned Multi-Agent Actor Critic for Decentralized LLM Collaboration<\/a><\/li><li><a href=\"https:\/\/arxiv.org\/html\/2601.21972v5\" target=\"_blank\" rel=\"noreferrer noopener\">Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic<\/a><\/li><li><a href=\"https:\/\/arxiv.org\/html\/2411.00053v3\" target=\"_blank\" rel=\"noreferrer noopener\">ACC-Collab: An Actor-Critic Approach to Multi-Agent LLM Collaboration<\/a><\/li><li><a href=\"https:\/\/arxiv.org\/html\/2501.06322v1\" target=\"_blank\" rel=\"noreferrer noopener\">Multi-Agent Collaboration Mechanisms: A Survey of LLMs<\/a><\/li><li><a href=\"https:\/\/arxiv.org\/abs\/2604.17148\" target=\"_blank\" rel=\"noreferrer noopener\">Graph-of-Agents: A Graph-based Framework for Multi-Agent LLM Collaboration<\/a><\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">\ud83d\udd2d i10x Perspective<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">We are witnessing the end of the strict &#8220;manager-employee&#8221; architecture in AI agent systems. By teaching LLMs to intuitively infer context rather than waiting to be explicitly told what to do by a routing script, methods like BCMAAC bring us closer to true AI swarms\u2014decentralized, asynchronous, and highly resilient. The competitive moat for the next generation of AI won&#8217;t just be who has the smartest single model, but whose models can seamlessly collaborate in the dark.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Google AI researchers introduced BCMAAC, a reinforcement learning approach that enables decentralized LLM agents to maintain belief states about peers&#8217; actions. This allows effective collaboration with minimal communication, reducing reliance on costly\u2026<\/p>\n","protected":false},"author":2,"featured_media":6666,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"paper_title":"","paper_url":"","pdf_url":"","doi":"","arxiv_id":"","authors":"","institutions":"","paper_date":"","venue":"","peer_reviewed":false,"code_url":"","data_url":"","funding_coi":"","figure_license":"","paper_license":"","replication":"","study_models":"","study_sample":"","footnotes":"","_yoast_wpseo_primary_category":"14"},"categories":[14],"tags":[],"companies":[37],"products":[],"class_list":["post-6667","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-agents","company-google"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate - i10X News<\/title>\n<meta name=\"description\" content=\"Google AI researchers developed BCMAAC, a belief-conditioned method that trains decentralized LLM agents to infer peer actions from sparse communication\u2026\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate - i10X News\" \/>\n<meta property=\"og:description\" content=\"Google AI researchers developed BCMAAC, a belief-conditioned method that trains decentralized LLM agents to infer peer actions from sparse communication\u2026\" \/>\n<meta property=\"og:url\" content=\"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration\" \/>\n<meta property=\"og:site_name\" content=\"i10X News\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-27T08:10:39+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1344\" \/>\n\t<meta property=\"og:image:height\" content=\"768\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Christopher Ort\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@i10x_ai\" \/>\n<meta name=\"twitter:site\" content=\"@i10x_ai\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Christopher Ort\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"NewsArticle\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration\"},\"author\":{\"name\":\"Christopher Ort\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#\\\/schema\\\/person\\\/d410bb7ee72febefca2c9c369939219a\"},\"headline\":\"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate\",\"datePublished\":\"2026-10-27T08:10:39+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration\"},\"wordCount\":832,\"publisher\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp\",\"articleSection\":[\"Agents\"],\"inLanguage\":\"en-US\",\"dateModified\":\"2026-10-27T08:10:39+00:00\"},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration\",\"name\":\"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate - i10X News\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp\",\"datePublished\":\"2026-10-27T08:10:39+00:00\",\"description\":\"Google AI researchers developed BCMAAC, a belief-conditioned method that trains decentralized LLM agents to infer peer actions from sparse communication\u2026\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration#primaryimage\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp\",\"contentUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp\",\"width\":1344,\"height\":768,\"caption\":\"Illustration: i10X\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/google-ai-bcmacc-decentralized-llm-collaboration#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/i10x.ai\\\/news\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#website\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\",\"name\":\"i10X News\",\"description\":\"Stay updated with the latest news and announcements from i10X.\",\"publisher\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/i10x.ai\\\/news?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\",\"name\":\"i10X\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/i10xnews.kinsta.cloud\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/i10x-logo-180.png\",\"contentUrl\":\"https:\\\/\\\/i10xnews.kinsta.cloud\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/i10x-logo-180.png\",\"width\":180,\"height\":180,\"caption\":\"i10X\"},\"image\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/x.com\\\/i10x_ai\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/i10x-ai\",\"https:\\\/\\\/instagram.com\\\/i10x.ai\",\"https:\\\/\\\/www.youtube.com\\\/@i10X_ai_official\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#\\\/schema\\\/person\\\/d410bb7ee72febefca2c9c369939219a\",\"name\":\"Christopher Ort\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"contentUrl\":\"https:\\\/\\\/i10x.ai\\\/news\\\/wp-content\\\/uploads\\\/2026\\\/10\\\/christopher-ort-192.webp\",\"caption\":\"Christopher Ort\"},\"description\":\"Christopher Ort handles SEO, AI agent workflows and PR & partnerships at i10x.ai, the all-in-one AI workspace built by i10X Global Pte. Ltd. in Singapore. He is a graduate of Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU). He is the author of \u201cThe Style Penalty\u201d, a study published on SSRN that analyses 1,576 evaluation decisions by four frontier AI models to show how AI-written CVs fare in automated hiring screens; the research has been covered by heise online and t3n.\",\"url\":\"https:\\\/\\\/i10x.ai\\\/news\\\/author\\\/christopher-ort\",\"sameAs\":[\"https:\\\/\\\/i10x.ai\\\/blog\\\/author\\\/christopher-ort\"],\"worksFor\":{\"@id\":\"https:\\\/\\\/i10x.ai\\\/news\\\/#organization\"},\"alumniOf\":{\"@type\":\"EducationalOrganization\",\"name\":\"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU)\",\"url\":\"https:\\\/\\\/www.fau.eu\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate - i10X News","description":"Google AI researchers developed BCMAAC, a belief-conditioned method that trains decentralized LLM agents to infer peer actions from sparse communication\u2026","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration","og_locale":"en_US","og_type":"article","og_title":"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate - i10X News","og_description":"Google AI researchers developed BCMAAC, a belief-conditioned method that trains decentralized LLM agents to infer peer actions from sparse communication\u2026","og_url":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration","og_site_name":"i10X News","article_published_time":"2026-10-27T08:10:39+00:00","og_image":[{"width":1344,"height":768,"url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp","type":"image\/webp"}],"author":"Christopher Ort","twitter_card":"summary_large_image","twitter_creator":"@i10x_ai","twitter_site":"@i10x_ai","twitter_misc":{"Written by":"Christopher Ort","Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"NewsArticle","@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration#article","isPartOf":{"@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration"},"author":{"name":"Christopher Ort","@id":"https:\/\/i10x.ai\/news\/#\/schema\/person\/d410bb7ee72febefca2c9c369939219a"},"headline":"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate","datePublished":"2026-10-27T08:10:39+00:00","mainEntityOfPage":{"@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration"},"wordCount":832,"publisher":{"@id":"https:\/\/i10x.ai\/news\/#organization"},"image":{"@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration#primaryimage"},"thumbnailUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp","articleSection":["Agents"],"inLanguage":"en-US","dateModified":"2026-10-27T08:10:39+00:00"},{"@type":"WebPage","@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration","url":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration","name":"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate - i10X News","isPartOf":{"@id":"https:\/\/i10x.ai\/news\/#website"},"primaryImageOfPage":{"@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration#primaryimage"},"image":{"@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration#primaryimage"},"thumbnailUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp","datePublished":"2026-10-27T08:10:39+00:00","description":"Google AI researchers developed BCMAAC, a belief-conditioned method that trains decentralized LLM agents to infer peer actions from sparse communication\u2026","breadcrumb":{"@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration"]}]},{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration#primaryimage","url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp","contentUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/google-ai-bcmacc-decentralized-llm-collaboration-title.webp","width":1344,"height":768,"caption":"Illustration: i10X"},{"@type":"BreadcrumbList","@id":"https:\/\/i10x.ai\/news\/google-ai-bcmacc-decentralized-llm-collaboration#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/i10x.ai\/news"},{"@type":"ListItem","position":2,"name":"Google AI BCMAAC Trains Decentralized LLM Agents to Collaborate"}]},{"@type":"WebSite","@id":"https:\/\/i10x.ai\/news\/#website","url":"https:\/\/i10x.ai\/news","name":"i10X News","description":"Stay updated with the latest news and announcements from i10X.","publisher":{"@id":"https:\/\/i10x.ai\/news\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/i10x.ai\/news?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"},{"@type":"Organization","@id":"https:\/\/i10x.ai\/news\/#organization","name":"i10X","url":"https:\/\/i10x.ai\/news\/","logo":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/i10x.ai\/news\/#\/schema\/logo\/image\/","url":"https:\/\/i10xnews.kinsta.cloud\/wp-content\/uploads\/2026\/10\/i10x-logo-180.png","contentUrl":"https:\/\/i10xnews.kinsta.cloud\/wp-content\/uploads\/2026\/10\/i10x-logo-180.png","width":180,"height":180,"caption":"i10X"},"image":{"@id":"https:\/\/i10x.ai\/news\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/x.com\/i10x_ai","https:\/\/www.linkedin.com\/company\/i10x-ai","https:\/\/instagram.com\/i10x.ai","https:\/\/www.youtube.com\/@i10X_ai_official"]},{"@type":"Person","@id":"https:\/\/i10x.ai\/news\/#\/schema\/person\/d410bb7ee72febefca2c9c369939219a","name":"Christopher Ort","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","url":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","contentUrl":"https:\/\/i10x.ai\/news\/wp-content\/uploads\/2026\/10\/christopher-ort-192.webp","caption":"Christopher Ort"},"description":"Christopher Ort handles SEO, AI agent workflows and PR & partnerships at i10x.ai, the all-in-one AI workspace built by i10X Global Pte. Ltd. in Singapore. He is a graduate of Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU). He is the author of \u201cThe Style Penalty\u201d, a study published on SSRN that analyses 1,576 evaluation decisions by four frontier AI models to show how AI-written CVs fare in automated hiring screens; the research has been covered by heise online and t3n.","url":"https:\/\/i10x.ai\/news\/author\/christopher-ort","sameAs":["https:\/\/i10x.ai\/blog\/author\/christopher-ort"],"worksFor":{"@id":"https:\/\/i10x.ai\/news\/#organization"},"alumniOf":{"@type":"EducationalOrganization","name":"Friedrich-Alexander-Universit\u00e4t Erlangen-N\u00fcrnberg (FAU)","url":"https:\/\/www.fau.eu"}}]}},"_links":{"self":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts\/6667","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/comments?post=6667"}],"version-history":[{"count":0,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/posts\/6667\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/media\/6666"}],"wp:attachment":[{"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/media?parent=6667"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/categories?post=6667"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/tags?post=6667"},{"taxonomy":"company","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/companies?post=6667"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/i10x.ai\/news\/wp-json\/wp\/v2\/products?post=6667"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}