S²-MAS research release

.gitattributes

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LICENSE

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+S²-MAS Controlled Model Research License, version 1.2
+19 October 2026
+
+Copyright 2026 S²-MAS authors, to the extent of rights held in the project contributions.
+
+1. Scope. This prospective distribution offer applies to the project-owned model contributions offered in this repository and its derivative model distribution. It changes no model parameters, training or evaluation. By accepting and exercising the permissions in this offer, the recipient agrees to the following conditions.
+
+2. Noncommercial research. The recipient may copy, store, load and run the supplied model locally for noncommercial scientific research and evaluation, and publish attributed, non-identifying aggregate findings. Commercial use requires prior written permission. Commercial use includes paid products or services, commercial deployment, consulting, business operations or decisions, and developing models or products for commercial distribution. Commercial entities require separate written permission.
+
+3. No redistribution. Without prior written permission, do not sell, rent, sublicense, share, mirror, rehost, publicly upload or distribute the covered material, substantial parameter extracts or model derivatives. Do not expose it to third parties through an API, endpoint, hosted demo or model service. Each recipient obtains permission from the authors.
+
+4. No unauthorized reverse engineering. Without prior written permission, do not reverse engineer, decompile, disassemble, reconstruct, extract, imitate or distill the covered project model; perform model inversion or parameter-extraction attacks; recover its parameters or training records through probes or outputs; or circumvent access controls. Do not reconstruct confidential source records or identify individuals. Reading and loading the supplied weights as needed for permitted evaluation is allowed. Non-waivable statutory exceptions remain effective.
+
+5. Other permissions. Training, fine-tuning, weight modification, model integration, distillation or derivative distribution requires separate written permission. Preserve this license and applicable upstream notices. Cite the model name and version when reporting research. No training records or source-data access is granted; obtain restricted inputs through their authorized providers. Wikidata structured facts retain CC0 independently.
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+6. Breach. Breach terminates permissions granted solely by this offer. Stop use and remove copies held only under it, except for mandatory retention. Independent rights continue. Other rights are reserved. The material is provided as is, without warranty, to the extent permitted by law. This is a restricted research-use offer, not an OSI open-source license.
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Model_Card_EN.md

@@ -0,0 +1,42 @@
+---
+language:
+- en
+license: other
+license_name: s2-mas-controlled-research-v1.2
+license_link: https://huggingface.co/ya9919/fin-laya/blob/main/LICENSE
+base_model: convaiinnovations/laya
+base_model_relation: finetune
+tags:
+- s2-mas
+- financial-one-shot
+- decision-model
+---
+
+# Fin-Laya · compatibility entry
+
+A financial one-shot decision model for S²-MAS. Inputs consist of a household state, a question and its complete candidate alternatives. Targets are observed responses from real-survey data. The model has approximately 421M parameters.
+
+Fin-Laya uses **2,925,263 post-training examples per language**, the largest disclosed domain post-training set among the financial decision models compared in the paper. The full-data MLX release achieves **74.5544% accuracy and 38.3746% question-macro F1**, exceeding Jev accuracy by **9.2960 percentage points** on the shared test.
+
+## Model files and inference
+
+This repository contains model weights, the tokenizer and the inference configuration. The native runtime is Laya / PyTorch with BF16 weights. `predict_example.py` uses an independently synthetic example.
+
+This is a compatibility entry; the named [native six-round release](https://huggingface.co/ya9919/fin-laya-6-rounds) and [MLX six-round release](https://huggingface.co/ya9919/fin-laya-mlx-6-rounds) provide their corresponding runtime formats.
+
+## Paper evaluation
+
+Evaluation: Native BF16.
+
+| Test | Records | Accuracy | Question-macro F1 |
+|---|---:|---:|---:|
+| Shared | 240,458 | 74.5586% | 38.3986% |
+| Full | 418,597 | 67.2728% | 27.8355% |
+
+The shared test contains 119 question versions. Question-macro F1 averages candidate-class F1 within each question version and then weights versions equally. 
+
+## License and manuscript
+
+The project model contribution is offered under the [Controlled Model Research License v1.2](LICENSE). The full license and retained upstream Apache-2.0 rights are in LICENSE. Training and evaluation records are not distributed with the model.
+
+[S²-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf) · [Project](https://github.com/KataDavidXD/s2-mas)

Model_Card_ZH.md

@@ -0,0 +1,42 @@
+---
+language:
+- en
+license: other
+license_name: s2-mas-controlled-research-v1.2
+license_link: https://huggingface.co/ya9919/fin-laya/blob/main/LICENSE
+base_model: convaiinnovations/laya
+base_model_relation: finetune
+tags:
+- s2-mas
+- financial-one-shot
+- decision-model
+---
+
+# Fin-Laya · compatibility entry
+
+用于 S²-MAS 的金融一次性决策模型。输入为家庭状态、问题及完整候选选项，学习目标为真实调查中的已观察回答。模型约有421M参数。
+
+完整数据后训练集每语言含2,925,263个样本，是论文所比较金融决策模型中披露规模最大的领域后训练集。完整数据 MLX 版本在共享测试集上准确率为74.5544%，question-macro F1为38.3746%，准确率比Jev高9.2960个百分点。
+
+## 模型文件与推理
+
+本仓库包含模型权重、tokenizer和必要推理配置。使用原生 Laya / PyTorch 的 BF16 格式。 `predict_example.py` 使用独立合成的示例输入。
+
+此为兼容入口；具名的[原生六轮模型](https://huggingface.co/ya9919/fin-laya-6-rounds)和[MLX六轮模型](https://huggingface.co/ya9919/fin-laya-mlx-6-rounds)分别提供对应运行格式。
+
+## 论文结果
+
+口径：Native BF16。
+
+| 测试集 | 记录数 | Accuracy | Question-macro F1 |
+|---|---:|---:|---:|
+| 共享测试 | 240,458 | 74.5586% | 38.3986% |
+| 完整测试 | 418,597 | 67.2728% | 27.8355% |
+
+共享测试包含119个问题版本。Question-macro F1先在每个问题内平均候选类别F1，再对问题版本等权平均。
+
+## 许可与论文
+
+项目模型贡献适用[受限科研许可v1.2](LICENSE)。完整许可及保留的上游Apache-2.0权利见LICENSE。训练与评估记录不随模型发布。
+
+[S²-MAS论文](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf) · [项目](https://github.com/KataDavidXD/s2-mas)

NOTICE

@@ -0,0 +1,2 @@
+Derived from Convai Innovations Laya English, Apache-2.0.
+Original survey records retain their source-provider terms.

README.md

@@ -0,0 +1,42 @@
+---
+language:
+- en
+license: other
+license_name: s2-mas-controlled-research-v1.2
+license_link: https://huggingface.co/ya9919/fin-laya/blob/main/LICENSE
+base_model: convaiinnovations/laya
+base_model_relation: finetune
+tags:
+- s2-mas
+- financial-one-shot
+- decision-model
+---
+
+# Fin-Laya · compatibility entry
+
+A financial one-shot decision model for S²-MAS. Inputs consist of a household state, a question and its complete candidate alternatives. Targets are observed responses from real-survey data. The model has approximately 421M parameters.
+
+Fin-Laya uses **2,925,263 post-training examples per language**, the largest disclosed domain post-training set among the financial decision models compared in the paper. The full-data MLX release achieves **74.5544% accuracy and 38.3746% question-macro F1**, exceeding Jev accuracy by **9.2960 percentage points** on the shared test.
+
+## Model files and inference
+
+This repository contains model weights, the tokenizer and the inference configuration. The native runtime is Laya / PyTorch with BF16 weights. `predict_example.py` uses an independently synthetic example.
+
+This is a compatibility entry; the named [native six-round release](https://huggingface.co/ya9919/fin-laya-6-rounds) and [MLX six-round release](https://huggingface.co/ya9919/fin-laya-mlx-6-rounds) provide their corresponding runtime formats.
+
+## Paper evaluation
+
+Evaluation: Native BF16.
+
+| Test | Records | Accuracy | Question-macro F1 |
+|---|---:|---:|---:|
+| Shared | 240,458 | 74.5586% | 38.3986% |
+| Full | 418,597 | 67.2728% | 27.8355% |
+
+The shared test contains 119 question versions. Question-macro F1 averages candidate-class F1 within each question version and then weights versions equally. 
+
+## License and manuscript
+
+The project model contribution is offered under the [Controlled Model Research License v1.2](LICENSE). The full license and retained upstream Apache-2.0 rights are in LICENSE. Training and evaluation records are not distributed with the model.
+
+[S²-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf) · [Project](https://github.com/KataDavidXD/s2-mas)

encoder/config.json

@@ -0,0 +1,85 @@
+{
+  "architectures": [
+    "ModernBertForMaskedLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 50281,
+  "classifier_activation": "gelu",
+  "classifier_bias": false,
+  "classifier_dropout": 0.0,
+  "classifier_pooling": "mean",
+  "cls_token_id": 50281,
+  "decoder_bias": true,
+  "deterministic_flash_attn": false,
+  "dtype": "float32",
+  "embedding_dropout": 0.0,
+  "eos_token_id": 50282,
+  "global_attn_every_n_layers": 3,
+  "global_rope_theta": 160000.0,
+  "gradient_checkpointing": false,
+  "hidden_activation": "gelu",
+  "hidden_size": 1024,
+  "initializer_cutoff_factor": 2.0,
+  "initializer_range": 0.02,
+  "intermediate_size": 2624,
+  "layer_norm_eps": 1e-05,
+  "layer_types": [
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "local_attention": 128,
+  "local_rope_theta": 10000.0,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "mlp_dropout": 0.0,
+  "model_type": "modernbert",
+  "norm_bias": false,
+  "norm_eps": 1e-05,
+  "num_attention_heads": 16,
+  "num_hidden_layers": 28,
+  "pad_token_id": 50283,
+  "position_embedding_type": "absolute",
+  "repad_logits_with_grad": false,
+  "rope_parameters": {
+    "full_attention": {
+      "rope_theta": 160000.0,
+      "rope_type": "default"
+    },
+    "sliding_attention": {
+      "rope_theta": 10000.0,
+      "rope_type": "default"
+    }
+  },
+  "sep_token_id": 50282,
+  "sparse_pred_ignore_index": -100,
+  "sparse_prediction": false,
+  "transformers_version": "4.57.6",
+  "vocab_size": 50368
+}

evaluation_report.json

@@ -0,0 +1,36 @@
+{
+  "report_scope": "Aggregate evaluation values reported in the paper",
+  "paper": {
+    "title": "S²-MAS: A Society-Scale Multi-Agent Simulation Framework with 500 Million Household Persona Agents",
+    "version": "S²-MAS manuscript",
+    "date": "2026-10-20",
+    "project_url": "https://github.com/KataDavidXD/s2-mas",
+    "manuscript_url": "https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf"
+  },
+  "repository_role": "Compatibility entry referencing the paper's full-data MLX result; not an additional model",
+  "model_reference": {
+    "model": "Fin-Laya 6-round",
+    "runtime": "MLX CUDA",
+    "precision": "FP16"
+  },
+  "training": {
+    "continuation_examples_per_language": 2925263,
+    "paper_reference": "Supplementary Methods M6"
+  },
+  "metric_definition": "Accuracy pools records; question-macro F1 averages candidate-class F1 within each question version, then weights versions equally",
+  "shared_test": {
+    "records": 240458,
+    "question_versions": 119,
+    "language": "English",
+    "accuracy_pct": 74.5544,
+    "question_macro_f1_pct": 38.3746,
+    "paper_reference": "Supplementary Methods M5"
+  },
+  "expanded_test": {
+    "records": 418597,
+    "language": "English",
+    "accuracy_pct": 67.2726,
+    "question_macro_f1_pct": 27.8955,
+    "paper_reference": "Supplementary Methods M6"
+  }
+}

example_request.json

@@ -0,0 +1,19 @@
+{
+  "state": {
+    "Household": "Independent synthetic example",
+    "Monthly income": 8000,
+    "Available cash": 15000,
+    "Monthly essential spending": 4000
+  },
+  "questions": {
+    "decision01": {
+      "type": "choice",
+      "instructions": "Choose the spending plan most suitable for this synthetic household.",
+      "criteria": {
+        "1": "Spend 1000 now and retain the remaining cash",
+        "2": "Spend 2000 now and retain the remaining cash",
+        "3": "Postpone the purchase"
+      }
+    }
+  }
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5b8cda43c63beaccf014e12845f6205d74e78695abfe60839ef41491b2fdebe9
+size 842609220

paired_release_report.json

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+{
+  "report_scope": "Paper-reported six-round native/MLX results and separate bilingual deployment benchmark",
+  "paper": {
+    "title": "S²-MAS: A Society-Scale Multi-Agent Simulation Framework with 500 Million Household Persona Agents",
+    "version": "S²-MAS manuscript",
+    "date": "2026-10-20",
+    "project_url": "https://github.com/KataDavidXD/s2-mas",
+    "manuscript_url": "https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf"
+  },
+  "repository_role": "Compatibility entry; no additional independent model",
+  "training": {
+    "continuation_examples_per_language": 2925263,
+    "paper_reference": "Supplementary Methods M6"
+  },
+  "evaluation_protocol": {
+    "shared_records": 240458,
+    "shared_question_versions": 119,
+    "expanded_records": 418597,
+    "language": "English",
+    "metric": "candidate-class question-macro F1",
+    "paper_reference": "Supplementary Methods M4-M6"
+  },
+  "native_mlx_evaluation": [
+    {
+      "model": "Fin-Laya 6-round",
+      "runtime": "PyTorch",
+      "precision": "BF16",
+      "shared_accuracy_pct": 74.5586,
+      "shared_question_macro_f1_pct": 38.3986,
+      "expanded_accuracy_pct": 67.2728,
+      "expanded_question_macro_f1_pct": 27.8355
+    },
+    {
+      "model": "Fin-Laya 6-round",
+      "runtime": "MLX CUDA",
+      "precision": "FP16",
+      "shared_accuracy_pct": 74.5544,
+      "shared_question_macro_f1_pct": 38.3746,
+      "expanded_accuracy_pct": 67.2726,
+      "expanded_question_macro_f1_pct": 27.8955
+    }
+  ],
+  "common_question_system_comparison": {
+    "records": 240458,
+    "question_versions": 119,
+    "fin_laya_mlx_accuracy_pct": 74.5544,
+    "fin_laya_mlx_question_macro_f1_pct": 38.3746,
+    "jev_1_13_api_accuracy_pct": 65.2584,
+    "jev_1_13_api_question_macro_f1_pct": 37.4793,
+    "fin_laya_minus_jev_accuracy_percentage_points": 9.296,
+    "paper_reference": "Supplementary Methods M5"
+  },
+  "separate_bilingual_deployment_benchmark": {
+    "checkpoint": "Bilingual Fin-Laya, multilingual-base adaptation",
+    "measurement_date": "2026-10-11",
+    "comparison_unit": "shared-input-token throughput on identical serialized inputs",
+    "hardware": "Local arms: one NVIDIA A100 PCIe 40GB; Jev: provider-managed",
+    "local_precision": "FP16",
+    "fixed_chinese_inputs": 3,
+    "timed_calls_per_arm": 60,
+    "batch_size": 1,
+    "concurrency": 1,
+    "timing_interval": "Input preparation, tokenization, synchronized prediction and response formatting; Jev includes HTTPS/API round trip; loading excluded",
+    "shared_tokenizer": "mmBERT",
+    "mlx_to_jev_shared_input_throughput_ratio": 22.72,
+    "mlx_to_same_checkpoint_pytorch_shared_input_throughput_ratio": 2.42,
+    "results": [
+      {
+        "model_or_checkpoint": "Bilingual Fin-Laya",
+        "runtime": "PyTorch A100",
+        "precision": "FP16",
+        "mean_latency_ms": 22.06,
+        "choices_per_s": 45.33,
+        "shared_input_tokens_per_s": 15247.34
+      },
+      {
+        "model_or_checkpoint": "Bilingual Fin-Laya",
+        "runtime": "MLX CUDA A100",
+        "precision": "FP16",
+        "mean_latency_ms": 9.1,
+        "choices_per_s": 109.88,
+        "shared_input_tokens_per_s": 36955.47
+      },
+      {
+        "model_or_checkpoint": "Jev 1.13",
+        "runtime": "Provider API",
+        "precision": "Provider-managed",
+        "mean_latency_ms": 206.77,
+        "choices_per_s": 4.84,
+        "shared_input_tokens_per_s": 1626.63
+      }
+    ],
+    "paper_reference": "Supplementary Methods M9"
+  }
+}

predict_example.py

@@ -0,0 +1,17 @@
+"""Run one native typed-choice request from the downloaded model directory."""
+import argparse,json
+from pathlib import Path
+ap=argparse.ArgumentParser();ap.add_argument('--backend',choices=['torch','mlx'],default='torch');ap.add_argument('--device')
+ap.add_argument('--request',type=Path);ap.add_argument('--output',type=Path);a=ap.parse_args()
+root=Path(__file__).resolve().parent
+request=json.loads((a.request or root/'example_request.json').read_text(encoding='utf-8'))
+if a.backend=='mlx':
+    from laya_mlx.agent import Agent
+    agent=Agent(str(root),dtype='float16',device=a.device or 'gpu',compile=False,cache_prompts=False)
+else:
+    import torch
+    from laya.agent import Agent
+    torch.set_num_threads(4);agent=Agent(str(root),device=a.device or 'cuda',compile=False)
+result=agent.predict(request['state'],request['questions']);serialized=json.dumps(result,ensure_ascii=False,indent=2)
+if a.output:a.output.write_text(serialized,encoding='utf-8')
+print(serialized)

reports/PAPER_RESULTS.csv

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+measurement,model_or_checkpoint,runtime,precision,language,records,question_versions,accuracy_pct,question_macro_f1_pct,training_examples_per_language,mean_latency_ms,choices_per_s,shared_input_tokens_per_s,paper_reference
+shared_test,Fin-Laya 6-round,PyTorch,BF16,English,240458,119,74.5586,38.3986,2925263,,,,Methods M4-M6
+expanded_test,Fin-Laya 6-round,PyTorch,BF16,English,418597,,67.2728,27.8355,2925263,,,,Methods M4-M6
+shared_test,Fin-Laya 6-round,MLX CUDA,FP16,English,240458,119,74.5544,38.3746,2925263,,,,Methods M4-M6
+expanded_test,Fin-Laya 6-round,MLX CUDA,FP16,English,418597,,67.2726,27.8955,2925263,,,,Methods M4-M6
+shared_test,Jev 1.13,Provider API,Provider-managed,English,240458,119,65.2584,37.4793,,,,,Methods M5
+separate_bilingual_deployment,Bilingual Fin-Laya,PyTorch A100,FP16,Chinese,,,,,,22.06,45.33,15247.34,Methods M9
+separate_bilingual_deployment,Bilingual Fin-Laya,MLX CUDA A100,FP16,Chinese,,,,,,9.1,109.88,36955.47,Methods M9
+separate_bilingual_deployment,Jev 1.13,Provider API,Provider-managed,Chinese,,,,,,206.77,4.84,1626.63,Methods M9

reports/PAPER_RESULTS.md

@@ -0,0 +1,33 @@
+# Paper-reported results
+
+Compatibility entry. The MLX scores below refer to the paper's full-data six-round MLX model; this entry is not an additional evaluated model.
+
+Source: *S²-MAS: A Society-Scale Multi-Agent Simulation Framework with 500 Million Household Persona Agents*. [S²-MAS manuscript](https://github.com/KataDavidXD/s2-mas/blob/main/paper/S2_MAS_Paper.pdf).
+
+## Training and prediction
+
+The grouped continuation training partition contains **2,925,263 examples per language** (Methods M6). English is the default evaluation language. Accuracy pools all records; question-macro F1 first averages candidate-class F1 within a question version, then weights versions equally.
+
+| Model and runtime | Precision | Test records | Question versions | Accuracy (%) | Question-macro F1 (%) | Paper source |
+|---|---|---:|---:|---:|---:|---|
+| Fin-Laya 6-round, PyTorch | BF16 | 240,458 | 119 | 74.5586 | 38.3986 | Methods M4 |
+| Fin-Laya 6-round, MLX CUDA | FP16 | 240,458 | 119 | 74.5544 | 38.3746 | Methods M5 |
+| Fin-Laya 6-round, PyTorch, expanded test | BF16 | 418,597 | — | 67.2728 | 27.8355 | Methods M6 |
+| Fin-Laya 6-round, MLX CUDA, expanded test | FP16 | 418,597 | — | 67.2726 | 27.8955 | Methods M6 |
+| Jev 1.13 API | Provider-managed | 240,458 | 119 | 65.2584 | 37.4793 | Methods M5 |
+
+The paper's shared-test accuracy difference between Fin-Laya 6-round MLX and Jev is **9.2960 percentage points**.
+
+## Separate bilingual deployment benchmark
+
+Methods M9 reports the bilingual Fin-Laya checkpoint on three fixed Chinese inputs, with 60 timed calls per arm, batch size and concurrency one. Local FP16 inference uses one A100 PCIe 40GB. The measured interval includes input preparation, tokenization, synchronized prediction and response formatting; Jev includes its API round trip. Loading is excluded. Identical serialized inputs use common mmBERT token counts.
+
+| Measured model and runtime | Mean latency (ms) | Choices/s | Shared input tokens/s |
+|---|---:|---:|---:|
+| Bilingual Fin-Laya, PyTorch A100 | 22.06 | 45.33 | 15,247.34 |
+| Bilingual Fin-Laya, MLX CUDA A100 | 9.10 | 109.88 | 36,955.47 |
+| Jev 1.13 API | 206.77 | 4.84 | 1,626.63 |
+
+The bilingual MLX arm's **shared-input-token throughput** is **22.72× Jev API's** and **2.42× the same checkpoint's PyTorch throughput**. Prediction accuracy above uses the six-round evaluation; this timing table uses the paper's separate bilingual checkpoint.
+
+The accompanying [CSV table](PAPER_RESULTS.csv) contains these paper-reported aggregates. Model-use terms remain in the repository LICENSE.

requirements.txt

@@ -0,0 +1,3 @@
+git+https://github.com/NandhaKishorM/laya.git@68804629e8ccd9d616d48a40e87de9fabbeae069
+huggingface_hub>=0.34
+numpy>=1.26

rl_agent_config.json

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+{
+  "encoder": "answerdotai/ModernBERT-large",
+  "head_layers": 2,
+  "max_len": 512,
+  "head_max_len": 256,
+  "max_prefixes": 6,
+  "act_costs": {
+    "escalate": 0.5
+  },
+  "cost_wrong_act": 3.0,
+  "amp_dtype": "bf16",
+  "model_name": "fin-laya",
+  "temperature": [
+    1.0776201486587524,
+    1.0,
+    1.0
+  ],
+  "temperature_by_options": {
+    "choice:3-5": 1.0669360160827637,
+    "choice:2": 1.1027063131332397,
+    "choice:6-10": 1.0735206604003906,
+    "choice:11+": 1.1108461618423462
+  },
+  "fine_tuned": true
+}

tokenizer/special_tokens_map.json

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+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

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