NeuroGenesysThe research we build on.
1,514 papers by other researchers that our work cites, from continual learning and verification to language models, agents and hardware. Each entry links to the paper on arXiv and to where we use it. 301 are cited on published pages of this site; the rest in the research notes behind our studies. Our own papers are in the archive.
- 2026-11arXiv 2611.12953Agent Architecture Cost-Success Trade-offs: A Controlled Evaluation across Enterprise-Style SOPs
Subhrangshu Nandi, Hitesh Arora, Yunfei Bai et al. · AI & agents
Enterprises increasingly use large language model agents to execute standard operating procedures (SOPs): long, branching, tool-intensive workflows. Yet there is little controlled evidence for choosing among competing agentic architectures.
- 2026-11arXiv 2611.06760Route First, See Later: Enabling Vision Agents to Reason Scientifically by Separating Routing Policy from Action Spaces
Shubhabrata Mukherjee, Jeffrey Donatelli, Daniela Ushizima · Vision
Scientific image-analysis workflows require agents to select among diverse capabilities like description, detection, counting, and segmentation.
- 2026-10arXiv 2611.00544Skill++: Towards Lifelong Agents via Routing-Aware Skill Refinement
Hanrong Zhang, Minghao Liang, Jiawei Wu et al. · AI & agents
Large language model (LLM) agents extend their capabilities with skills, each with a name, a description, and a body. Under progressive disclosure, an LLM router selects skills by name and description alone and loads only the selected bodies.
- 2026-10arXiv 2610.18522Lost in Implicit Routing: How LLM Agents Fail to Load Relevant Skills at Scale
Jinlin Liu, Wei Chen, Xiaojin Zhang · AI & agents
Reusable skills let LLM agents load task-specific instructions on demand, but they make execution depend on implicit routing over skill metadata. We test whether this design scales by holding SkillsBench tasks and their golden skills fixed while adding irrelevant skills.
- 2026-05arXiv 2605.17571Stable Routing for Mixture-of-Experts in Class-Incremental Learning
Zirui Guo, Quan Cheng, Da-Wei Zhou et al. · Vision
Class-incremental learning (CIL) requires models to learn new classes sequentially while preserving prior knowledge.
- 2026-05arXiv 2605.14438BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE
Juntong Wu, Jialiang Cheng, Qishen Yin et al. · AI & agents
Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K routing strategy, leading to redundant computation and suboptimal inference latency.
- 2026-02arXiv 2603.06616RACER: Risk-Aware Calibrated Efficient Routing for Large Language Models
Sai Hao, Hao Zeng, Hongxin Wei et al. · Machine learning
Efficiently routing queries to the optimal large language model (LLM) is crucial for optimizing the cost-performance trade-off in multi-model systems. However, most existing routers rely on single-model selection, making them susceptible to misrouting.
- 2026-02arXiv 2602.03473Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts
Meng Lou, Yunxiang Fu, Yizhou Yu · Machine learning
Continual learning, especially class-incremental learning (CIL), on the basis of a pre-trained model (PTM) has garnered substantial research interest in recent years.
- 2026-01arXiv 2601.07206LLMRouterBench: A Massive Benchmark and Unified Framework for LLM Routing
Hao Li, Yiqun Zhang, Zhaoyan Guo et al. · AI & agents
Large language model (LLM) routing assigns each query to the most suitable model from an ensemble. We introduce LLMRouterBench, a large-scale benchmark and unified framework for LLM routing.
- 2025-11arXiv 2511.16883PersonalizedRouter: Personalized LLM Routing via Graph-based User Preference Modeling
Zhongjie Dai, Tao Feng, Jiaxuan You · Machine learning
The growing number of Large Language Models (LLMs) with diverse capabilities and response styles provides users with a wider range of choices, which presents challenges in selecting appropriate LLMs, as user preferences vary in terms of performance, cost, and response style.
- 2025-09arXiv 2510.00202RouterArena: An Open Platform for Comprehensive Comparison of LLM Routers
Yifan Lu, Rixin Liu, Jiayi Yuan et al. · Machine learning
Today's LLM ecosystem comprises a wide spectrum of models that differ in size, capability, and cost. No single model is optimal for all scenarios; hence, LLM routers have become essential for selecting the most appropriate model under varying circumstances.
- 2025-08arXiv 2508.09834Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou et al. · Language models & NLP
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models.
- 2025-08arXiv 2508.07738Separation and Collaboration: Two-Level Routing Grouped Mixture-of-Experts for Multi-Domain Continual Learning
Jialu Zhou, Dianxi Shi, Shaowu Yang et al. · Machine learning
Multi-Domain Continual Learning (MDCL) acquires knowledge from sequential tasks with shifting class sets and distribution. Despite the Parameter-Efficient Fine-Tuning (PEFT) methods can adapt for this dual heterogeneity, they still suffer from catastrophic forgetting and forward forgetting.
- 2025-08arXiv 2508.01780LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?
Guozhao Mo, Wenliang Zhong, Jiawei Chen et al. · AI & agents
Model Context Protocol (MCP) has become a key infrastructure for connecting LLMs with external tools, scaling to 10,000+ MCP servers with diverse tools.
- 2025-06arXiv 2506.20650Mastering Multiple-Expert Routing: Realizable -Consistency and Strong Guarantees for Learning to Defer
Anqi Mao, Mehryar Mohri, Yutao Zhong · Machine learning
The problem of learning to defer with multiple experts consists of optimally assigning input instances to experts, balancing the trade-off between their accuracy and computational cost.
- 2025-06arXiv 2506.01048IRT-Router: Effective and Interpretable Multi-LLM Routing via Item Response Theory
Wei Song, Zhenya Huang, Cheng Cheng et al. · AI & agents
Large language models (LLMs) have demonstrated exceptional performance across a wide range of natural language tasks. However, selecting the optimal LLM to respond to a user query often necessitates a delicate balance between performance and cost.
- 2025-05arXiv 2506.11038MoTE: Mixture of Task-specific Experts for Pre-Trained ModelBased Class-incremental Learning
Linjie Li, Zhenyu Wu, Yang Ji · Machine learning
Class-incremental learning (CIL) requires deep learning models to continuously acquire new knowledge from streaming data while preserving previously learned information.
- 2025-05arXiv 2505.12601Rethinking Predictive Modeling for LLM Routing: When Simple kNN Beats Complex Learned Routers
Yang Li · Machine learning
As large language models (LLMs) grow in scale and specialization, routing--selecting the best model for a given input--has become essential for efficient and effective deployment.
- 2025-05arXiv 2505.03275RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation
Tiantian Gan, Qiyao Sun · AI & agents
Large language models (LLMs) struggle to effectively utilize a growing number of external tools, such as those defined by the Model Context Protocol (MCP)\cite{IntroducingMCP}, due to prompt bloat and selection complexity.
- 2025-03arXiv 2503.21227LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models
Hengyuan Zhao, Ziqin Wang, Qixin Sun et al. · Language models & NLP
Mixture of Experts (MoE) architectures have recently advanced the scalability and adaptability of large language models (LLMs) for continual multimodal learning.
- 2025-03arXiv 2503.10657RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMs
Zhongzhan Huang, Guoming Ling, Yupei Lin et al. · Language models & NLP
Routing large language models (LLMs) is a new paradigm that uses a router to recommend the best LLM from a pool of candidates for a given input., a capable router can significantly enhance the performance of this paradigm as the number of candidates increases.
- 2025-03arXiv 2503.01763Retrieval Models Aren't Tool-Savvy: Benchmarking Tool Retrieval for Large Language Models
Zhengliang Shi, Yuhan Wang, Lingyong Yan et al. · Language models & NLP
Tool learning aims to augment large language models (LLMs) with diverse tools, enabling them to act as agents for solving practical tasks.
- 2024-12arXiv 2412.19437DeepSeek-V3 Technical Report
DeepSeek-AI, Aixin Liu, Bei Feng et al. · Language models & NLP
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total parameters with 37B activated for each token.
- 2024-12arXiv 2412.14711ReMoE: Fully Differentiable Mixture-of-Experts with ReLU Routing
Ziteng Wang, Jun Zhu, Jianfei Chen · Machine learning
Sparsely activated Mixture-of-Experts (MoE) models are widely adopted to scale up model capacity without increasing the computation budget. However, vanilla TopK routers are trained in a discontinuous, non-differentiable way, limiting their performance and scalability.
- 2024-11arXiv 2411.17766Dual Prototypes for Adaptive Pre-Trained Model in Class-Incremental Learning
Zhiming Xu, Suorong Yang, Baile Xu et al. · Machine learning
Class-incremental learning (CIL) aims to learn new classes while retaining previous knowledge. Although pre-trained model (PTM) based approaches show strong performance, directly fine-tuning PTMs on incremental task streams often causes renewed catastrophic forgetting.
- 2024-11arXiv 2411.00430Class Incremental Learning with Task-Specific Batch Normalization and Out-of-Distribution Detection
Zhiping Zhou, Xuchen Xie, Yiqiao Qiu et al. · Machine learning
This study focuses on incremental learning for image classification, exploring how to reduce catastrophic forgetting of all learned knowledge when access to old data is restricted.
- 2024-10arXiv 2410.13284Learning to Route LLMs with Confidence Tokens
Yu-Neng Chuang, Prathusha Kameswara Sarma, Parikshit Gopalan et al. · Language models & NLP
Large language models (LLMs) have demonstrated impressive performance on several tasks and are increasingly deployed in real-world applications. However, especially in high-stakes settings, it becomes vital to know when the output of an LLM may be unreliable.
- 2024-10arXiv 2410.07348MoE++: Accelerating Mixture-of-Experts Methods with Zero-Computation Experts
Peng Jin, Bo Zhu, Li Yuan et al. · Machine learning
In this work, we aim to simultaneously enhance the effectiveness and efficiency of Mixture-of-Experts (MoE) methods. To achieve this, we propose MoE++, a general and heterogeneous MoE framework that integrates both Feed-Forward Network~(FFN) and zero-computation experts.
- 2024-10arXiv 2410.03834GraphRouter: A Graph-based Router for LLM Selections
Tao Feng, Yanzhen Shen, Jiaxuan You · AI & agents
The rapidly growing number and variety of Large Language Models (LLMs) present significant challenges in efficiently selecting the appropriate LLM for a given query, especially considering the trade-offs between performance and computational cost.
- 2024-10arXiv 2410.02173Efficiently Deploying LLMs with Controlled Risk
Michael J. Zellinger, Matt Thomson · Machine learning
Deploying large language models in production requires simultaneous attention to efficiency and risk control. Prior work has shown the possibility to cut costs while maintaining similar accuracy, but has neglected to focus on risk control.
- 2024-10arXiv 2410.02223EmbedLLM: Learning Compact Representations of Large Language Models
Richard Zhuang, Tianhao Wu, Zhaojin Wen et al. · Language models & NLP
With hundreds of thousands of language models available on Huggingface today, efficiently evaluating and utilizing these models across various downstream, tasks has become increasingly critical.
- 2024-09arXiv 2409.19886RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models
Shuhao Chen, Weisen Jiang, Baijiong Lin et al. · Machine learning
Recent works show that assembling multiple off-the-shelf large language models (LLMs) can harness their complementary abilities. To achieve this, routing is a promising method, which learns a router to select the most suitable LLM for each query.
- 2024-09arXiv 2409.02060OLMoE: Open Mixture-of-Experts Language Models
Niklas Muennighoff, Luca Soldaini, Dirk Groeneveld et al. · Language models & NLP
We introduce OLMoE, a fully open, state-of-the-art language model leveraging sparse Mixture-of-Experts (MoE). OLMoE-1B-7B has 7 billion (B) parameters but uses only 1B per input token.
- 2024-08arXiv 2408.15664Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts
Lean Wang, Huazuo Gao, Chenggang Zhao et al. · Machine learning
For Mixture-of-Experts (MoE) models, an unbalanced expert load will lead to routing collapse or increased computational overhead.
- 2024-07arXiv 2407.01032Overcoming Common Flaws in the Evaluation of Selective Classification Systems
Jeremias Traub, Till J. Bungert, Carsten T. Lüth et al. · Machine learning
Selective Classification, wherein models can reject low-confidence predictions, promises reliable translation of machine-learning based classification systems to real-world scenarios such as clinical diagnostics.
- 2024-06arXiv 2406.18665RouteLLM: Learning to Route LLMs with Preference Data
Isaac Ong, Amjad Almahairi, Vincent Wu et al. · Machine learning
Large language models (LLMs) exhibit impressive capabilities across a wide range of tasks, yet the choice of which model to use often involves a trade-off between performance and cost.
Cited inRoute on shared statistics, never expert confidence: the router architecture for frozen expertsStudy: Hierarchical and T shaped MoEInternal notesStudy: Model self knowledge and confident errorsStudy: Optimal router architectureStudy: Orchestrated specialists and bridge synthesisStudy: Stopping confident wrong expert votes - 2024-06arXiv 2406.13233AdaMoE: Token-Adaptive Routing with Null Experts for Mixture-of-Experts Language Models
Zihao Zeng, Yibo Miao, Hongcheng Gao et al. · AI & agents
Mixture of experts (MoE) has become the standard for constructing production-level large language models (LLMs) due to its promise to boost model capacity without causing significant overheads.
- 2024-04arXiv 2404.14618Hybrid LLM: Cost-Efficient and Quality-Aware Query Routing
Dujian Ding, Ankur Mallick, Chi Wang et al. · Machine learning
, edge) devices, tend to lag behind in terms of response quality. Therefore in this work we propose a hybrid inference approach which combines their respective strengths to save cost and maintain quality.
- 2024-03arXiv 2403.08568Consistent Prompting for Rehearsal-Free Continual Learning
Zhanxin Gao, Jun Cen, Xiaobin Chang · Vision
Continual learning empowers models to adapt autonomously to the ever-changing environment or data streams without forgetting old knowledge. Prompt-based approaches are built on frozen pre-trained models to learn the task-specific prompts and classifiers efficiently.
- 2024-03arXiv 2403.07652Harder Tasks Need More Experts: Dynamic Routing in MoE Models
Quzhe Huang, Zhenwei An, Nan Zhuang et al. · Machine learning
In this paper, we introduce a novel dynamic expert selection framework for Mixture of Experts (MoE) models, aiming to enhance computational efficiency and model performance by adjusting the number of activated experts based on input difficulty.
Built from every arXiv paper cited in our papers, posts, topic hubs, reports and research notes, with titles, authors, dates and abstracts from the arXiv API (updated 2026-11-12). Summaries are the opening sentences of each paper's own abstract, not our assessment; a citation means we used or checked the work, not that we endorse it. Fields follow each paper's primary arXiv category. Papers cited only outside arXiv (journals, proceedings) are not listed yet. Entries are generated from our citations, so a mistyped ID in a note would list the wrong paper; we remove those as we find them. Report a wrong entry.
