| 01 |
01_Densing_Law_of_LLMs_2412.04315.pdf |
Densing Law of LLMs |
Nature Machine Intelligence (封面文章) |
2412.04315 |
| 02 |
02_R2R_Token_Routing_2505.21600.pdf |
R2R: Efficiently Navigating Divergent Reasoning Paths with Small-Large Model Token Routing |
NeurIPS 2025 |
2505.21600 |
| 03 |
03_BEST_Route_2506.22716.pdf |
BEST-Route: Adaptive LLM Routing with Test-Time Optimal Compute |
ICML 2025 |
2506.22716 |
| 04 |
04_SATER_2510.05164.pdf |
SATER: A Self-Aware and Token-Efficient Approach to Routing and Cascading |
EMNLP 2025 |
2510.05164 |
| 05 |
05_Token_Level_Routing_2504.07878.pdf |
Token Level Routing Inference System for Edge Devices |
ACL 2025 |
2504.07878 |
| 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 |
| 07 |
07_Mixture_of_Parrots_2410.19034.pdf |
Mixture of Parrots: Experts Improve Memorization More than Reasoning |
ICLR 2025 |
2410.19034 |
| 08 |
08_DomainCodeBench_2412.18573.pdf |
Top General Performance ≠ Top Domain Performance? DomainCodeBench: A Multi-domain Code Generation Benchmark |
arXiv 2025 |
2412.18573 |
| 09 |
09_Model_SAT_CIT_2502.17282.pdf |
Capability Instruction Tuning: A New Paradigm for Dynamic LLM Routing (Model-SAT) |
AAAI 2025 |
2502.17282 |
| 10 |
10_RouterRetriever_2409.02685.pdf |
RouterRetriever: Routing over a Mixture of Expert Embedding Models |
AAAI 2025 |
2409.02685 |
| 11 |
11_Inverse_Depth_Scaling_2602.05970.pdf |
Inverse Depth Scaling From Most Layers Being Similar |
ICML 2026 |
2602.05970 |
| 12 |
12_MergeBench_2505.10833.pdf |
MergeBench: A Benchmark for Merging Domain-Specialized LLMs |
NeurIPS 2025 (Datasets & Benchmarks) |
2505.10833 |
| 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 |
