54 lines
3.8 KiB
Markdown
54 lines
3.8 KiB
Markdown
# 参考文献索引
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本目录收录了"多专业小模型 + 路由模型"可行性分析报告中引用的全部 15 篇参考文献。
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---
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## 📄 arXiv 论文（13 篇 PDF）
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| # | 文件名 | 标题 | 会议/期刊 | arXiv ID |
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|---|--------|------|----------|----------|
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| 01 | `01_Densing_Law_of_LLMs_2412.04315.pdf` | Densing Law of LLMs | **Nature Machine Intelligence** (封面文章) | [2412.04315](https://arxiv.org/abs/2412.04315) |
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| 02 | `02_R2R_Token_Routing_2505.21600.pdf` | R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing | **NeurIPS 2025** | [2505.21600](https://arxiv.org/abs/2505.21600) |
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| 03 | `03_BEST_Route_2506.22716.pdf` | BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute | **ICML 2025** | [2506.22716](https://arxiv.org/abs/2506.22716) |
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| 04 | `04_SATER_2510.05164.pdf` | SATER: A Self-Aware and Token-Efficient Approach to Routing and Cascading | **EMNLP 2025** | [2510.05164](https://arxiv.org/abs/2510.05164) |
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| 05 | `05_Token_Level_Routing_2504.07878.pdf` | Token Level Routing Inference System for Edge Devices | **ACL 2025** | [2504.07878](https://arxiv.org/abs/2504.07878) |
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| 06 | `06_Comp_LLM_2511.22955.pdf` | Experts are all you need: A Composable Framework for Large Language Model Inference (Comp-LLM) | arXiv 2025 | [2511.22955](https://arxiv.org/abs/2511.22955) |
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| 07 | `07_Mixture_of_Parrots_2410.19034.pdf` | Mixture of Parrots: Experts Improve Memorization More than Reasoning | **ICLR 2025** | [2410.19034](https://arxiv.org/abs/2410.19034) |
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| 08 | `08_DomainCodeBench_2412.18573.pdf` | Top General Performance ≠ Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark | arXiv 2025 | [2412.18573](https://arxiv.org/abs/2412.18573) |
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| 09 | `09_Model_SAT_CIT_2502.17282.pdf` | Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing (Model-SAT) | **AAAI 2025** | [2502.17282](https://arxiv.org/abs/2502.17282) |
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| 10 | `10_RouterRetriever_2409.02685.pdf` | RouterRetriever: Routing over a Mixture of Expert Embedding Models | **AAAI 2025** | [2409.02685](https://arxiv.org/abs/2409.02685) |
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| 11 | `11_Inverse_Depth_Scaling_2602.05970.pdf` | Inverse Depth Scaling From Most Layers Being Similar | **ICML 2026** | [2602.05970](https://arxiv.org/abs/2602.05970) |
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| 12 | `12_MergeBench_2505.10833.pdf` | MergeBench: A Benchmark for Merging Domain-Specialized LLMs | **NeurIPS 2025** (Datasets & Benchmarks) | [2505.10833](https://arxiv.org/abs/2505.10833) |
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| 13 | `13_Doing_More_With_Less_2502.00409.pdf` | Doing More with Less: Implementing Routing Strategies in LLM-Based Systems (Extended Survey) | arXiv 2025 | [2502.00409](https://arxiv.org/abs/2502.00409) |
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## 📝 博客文章（2 篇）
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| 文件名 | 标题 | 来源 |
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|--------|------|------|
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| `RouterArena_blog.md` | Who Routes LLM Routers? — RouterArena: Building the Evaluation Foundation for LLM Routing | Hugging Face Blog (2025.11) |
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| `Small_FineTuned_Models_blog.md` | Small Fine-tuned Models are All You Need | Oumi Blog (2025.10) |
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## 📊 按会议/期刊分布
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| 会议/期刊 | 论文数 | 论文编号 |
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|----------|--------|---------|
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| **NeurIPS 2025** | 2 | 02 (R2R), 12 (MergeBench) |
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| **ICML 2025** | 1 | 03 (BEST-Route) |
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| **EMNLP 2025** | 1 | 04 (SATER) |
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| **ACL 2025** | 1 | 05 (Token Level Routing) |
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| **AAAI 2025** | 2 | 09 (Model-SAT), 10 (RouterRetriever) |
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| **ICLR 2025** | 1 | 07 (Mixture of Parrots) |
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| **Nature Machine Intelligence** | 1 | 01 (Densing Law) |
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| **ICML 2026** | 1 | 11 (Inverse Depth Scaling) |
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| **arXiv / 预印本** | 3 | 06 (Comp-LLM), 08 (DomainCodeBench), 13 (Survey) |
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## 🔍 按主题分类
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- **Routing 路由系统：** 02, 03, 04, 05, 09, 13, RouterArena
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- **小模型能力：** 07, 08, Small_FineTuned
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- **Scaling Law / 架构理论：** 01, 11
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- **专家模型合并：** 12
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- **可组合推理系统：** 06
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- **检索路由：** 10
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