arXiv AI July 15, 2026 By Yaopei Zeng, Congchao Wang, Blake JianHang Chen, Lu Lin

ReLope: KL-Regularized LoRA Probes for Multimodal LLM Routing

arXiv:2603. 24787v2 Announce Type: replace Abstract: Routing has emerged as a promising strategy for balancing performance and cost in large language model (LLM) systems that combine lightweight models with powerful but expensive large models.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Aug 13

LLM Router: Rethinking Routing with Prefill Activations

arXiv:2603. 20895v3 Announce Type: replace-cross Abstract: Existing routers rely on semantic query features or handcrafted features, which often fail to capture model-specific failures or intrinsic task difficulty.

By Tanay Varshney, Annie Surla, Michelle Xu, Gomathy Venkata Krishnan, Maximilian Jeblick, David Austin, Neal Vaidya, Davide Onofrio

llms

arXiv AI

Jun 9

An Effective Router for Vision-Language Model Selection

arXiv:2606. 08970v1 Announce Type: new Abstract: Vision-language models (VLMs) with varying performance and resource requirements are widely deployed, making it difficult for users to select the most appropriate one among numerous VLM candidates.

By Can Wang, Shengwei Wang, Bolin Zhang, Zhiying Tu, Dianhui Chu

llms multimodal

arXiv AI

Sep 15

AdaVSkip: Adaptive Visual Token Skipping Across Layers For Efficient MLLMs Inference

arXiv:2609.15131v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) require substantial computation to process numerous visual tokens across all transformer layers. Most method...

By Yuyao Sun, Tao Deng, Shuang Li, Deqing Wang

llms reinforcement-learning multimodal

arXiv AI

4d ago

Pretrain Once, Route Anywhere: Towards a Foundation Model for LLM Routing

arXiv:2609.37362v1 Announce Type: new Abstract: Large language model (LLM) routing aims to assign each query to the most suitable model from a heterogeneous candidate pool, improving the quality--eff...

By Guannan Lai, Han-Jia Ye

llms

arXiv AI

Jun 26

Tuning Language Models by Mixture-of-Depths Ensemble

arXiv:2410. 13077v2 Announce Type: replace-cross Abstract: Transformer-based Large Language Models (LLMs) traditionally rely on final-layer loss for finetuning and final-layer representations for predictions, potentially overlooking the predictive power embedded in late layers.

By Haoyan Luo, Lucia Specia

llms fine-tuning safety

arXiv AI

Jun 11

Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models

arXiv:2606. 12412v1 Announce Type: cross Abstract: Vision-language models (VLMs) project images into hundreds to thousands of visual tokens, making decoder inference expensive in both attention computation and KV-cache memory.

By Cheng-Yu Yang, Shao-Yuan Lo, Yu-Lun Liu

llms efficiency multimodal

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LLM Router: Rethinking Routing with Prefill Activations

An Effective Router for Vision-Language Model Selection

AdaVSkip: Adaptive Visual Token Skipping Across Layers For Efficient MLLMs Inference

Pretrain Once, Route Anywhere: Towards a Foundation Model for LLM Routing

Tuning Language Models by Mixture-of-Depths Ensemble

Reroute, Don't Remove: Recoverable Visual Token Routing for Vision-Language Models