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  "text": "Even the best reasoning models hit an accuracy collapse beyond a certain problem complexity.\n\nGiving an LRM the exact solution algorithm doesn't fix it either.\n\nThis new work, BIGMAS, improves LLM agents by taking inspiration from the human brain.\n\nBIGMAS outperforms both ReAct and Tree of Thoughts across all three tasks.\n\nIt organizes specialized LLM agents as nodes in a dynamically constructed directed graph, coordinated through a centralized shared workspace inspired by global workspace theory.\n\nA GraphDesigner builds task-specific agent topologies per problem, and a global Orchestrator routes decisions using the complete shared state, eliminating the local-view bottleneck of reactive approaches.\n\nAcross Game24, Six Fives, and Tower of London on six frontier LLMs, including GPT-5 and Claude 4.5, BIGMAS consistently improves accuracy.\n\nThe gains are largest where models struggle most: DeepSeek-V3.2 jumps from 12% to 30% on Six Fives.\n\nPaper: https://t.co/sMqUfvHAGp\n\nLearn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c",
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