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  S²-MAS is a society-scale multi-agent simulation framework for studying how heterogeneous household decisions become population outcomes under changing conditions. It connects a constructed resource of **500 million household personas**, reusable scenarios, trainable decision models and stateful world updates. FinL supplies a reusable decision policy: it scores the complete alternatives for a household state and question, allowing the same policy interface to serve different tasks.
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- The manuscript's paired-condition application evaluates **12 million distinct synthetic household agents and 24 million choices**. A 24-month extension follows 10,000 households through changing income, expenses and bank offers. The applications show how household resources shape consumption responses: persistent income loss draws down reserves and delays purchases, while sustained income gains support both cash reserves and purchases. Repayment feedback affects later offers. These findings connect individual choices to consumption, cash and payment paths over time.
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  ## Repository role and training
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  This is the **MLX FP16 runtime of the English-base FinL 6-round checkpoint**, paired with the [native release](https://huggingface.co/ya9919/FinL). The approximately 421M-parameter Laya encoder and option-scoring head continue the 3-round financial checkpoint through SFT and RLCD over **2,925,263 training examples per language**, with expanded supervision from real-survey data. This is the largest disclosed domain post-training set among the financial decision models compared in the manuscript.
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- The manuscript's 12M-agent purchase-funding application and 24-month extension use this frozen checkpoint in MLX CUDA FP16.
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  ## Paper results for this checkpoint
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  S²-MAS is a society-scale multi-agent simulation framework for studying how heterogeneous household decisions become population outcomes under changing conditions. It connects a constructed resource of **500 million household personas**, reusable scenarios, trainable decision models and stateful world updates. FinL supplies a reusable decision policy: it scores the complete alternatives for a household state and question, allowing the same policy interface to serve different tasks.
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+ The manuscript's paired-condition application evaluates **12 million distinct synthetic household agents and 24 million choices**. These findings connect individual choices to consumption, cash and payment paths over time.
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  ## Repository role and training
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  This is the **MLX FP16 runtime of the English-base FinL 6-round checkpoint**, paired with the [native release](https://huggingface.co/ya9919/FinL). The approximately 421M-parameter Laya encoder and option-scoring head continue the 3-round financial checkpoint through SFT and RLCD over **2,925,263 training examples per language**, with expanded supervision from real-survey data. This is the largest disclosed domain post-training set among the financial decision models compared in the manuscript.
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+ The manuscript's 12M-agent purchase-funding application uses this frozen checkpoint in MLX CUDA FP16.
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  ## Paper results for this checkpoint
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