arXiv Computation and Language September 11, 2026 By Yu Wang, Yuchen Li, Rui Kong, Xinran Chen, Jiamin Chen, Hengyi Cai, Shuaiqiang Wang, Jiashu Zhao, Yulun Zhang, Zhonghao Lyu, Haoyi Xiong, Linghe Kong, Jimmy Xiangji Huang, Dawei Yin

SWRouter: Similarity-Contractive Window Routing for Multi-Turn Large Language Model Conversations

Read the original on arXiv Computation and Language →

SWRouter is a new routing method for multi‑turn large language model conversations that uses a similarity‑based context segmentation mechanism to construct prompts and a dual‑metric evaluation framework to separate construction accuracy from router performance. The approach addresses two key challenges in multi‑turn dialogue: preventing information loss or confusion during context construction and evaluating routing quality independently of prompt quality. Experiments on multi‑turn dialogue benchmarks show that SWRouter outperforms strong baselines, improving evaluation accuracy by 16.26% over the best individual large language model and by 8.22% over the Conv‑ID Context baseline.

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 Computation and Language.

arXiv AI

Jun 9

DYCP: Dynamic Context Pruning for Long-Form Dialogue with LLMs

arXiv:2601. 07994v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) increasingly operate over long-form dialogues with frequent topic shifts.

By Nayoung Choi, Jonathan Zhang, Jinho D. Choi

llms efficiency benchmarks

arXiv Machine Learning

Aug 11

Routesplain: Towards Faithful and Intervenable Routing for Software-related Tasks

arXiv:2511. 09373v2 Announce Type: replace-cross Abstract: LLMs now tackle a wide range of software-related tasks, yet we show that their performance varies markedly both across and within these tasks.

By Adam \v{S}torek, Vikas Upadhyay, Marianne Menglin Liu, Daniel W. Peterson, Anshul Mittal, Sujeeth Bharadwaj, Fahad Shah, Sujith Ravi, Dan Roth

llms benchmarks

arXiv AI

Jun 16

ChatPlanner: A Large Language Model Framework for Personalized Public Transit Routing

arXiv:2606. 15315v1 Announce Type: new Abstract: Personalized public transit routing in public transit systems remains challenging due to the difficulty of capturing and integrating diverse user preferences into routing algorithms.

By Tingting Yang, Chenhao Xue, Jun Chen

llms rag fine-tuning

arXiv Computation and Language

Sep 1

Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey

arXiv:2603.04445v3 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent mo...

By Yasmin Moslem, John D. Kelleher

llms reinforcement-learning

arXiv Machine Learning

Aug 28

A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns, and Practical Uses

The paper presents a survey of 129 public large language model (LLM) prompt datasets, totaling over 1.22 TB and 673 million instances, and introduces a unified taxonomy for them. By analyzing seven datasets in depth, the authors identify lexical, syntactic, and semantic patterns that differentiate prompts from general text, and evaluate these patterns for tasks such as prompt filtering, source domain routing, and response quality assessment. They demonstrate that a 63‑dimensional linguistic feature set extracted on a CPU can match over 91 % of the F1 score of GPU‑based sentence embeddings while halving latency, and that structural features can effectively route prompts across datasets, though they may negatively impact response quality when prompt length is controlled.

By Yuanming Zhang, Yan Lin, Arijit Khan, Huaiyu Wan

llms rag

arXiv Computation and Language

Aug 27

VDAR-Router: Adaptive LLMs Routing via Verbalized Query Difficulty Analysis Retrieval

The paper introduces VDAR-Router, a routing framework for large language models that uses verbalized query difficulty analysis to guide model selection. It first generates an explicit difficulty profile for each query, retrieves historical examples with similar profiles, and then estimates model suitability to choose a model based on a reward function balancing performance and cost. Experiments on three datasets show that VDAR-Router consistently outperforms existing baselines in cost‑performance trade‑offs, and case studies confirm that explicit difficulty analysis improves example relevance and routing reliability.

By Yu-Chien Tang, Jun-Chen Hung, Wen-Chih Peng, An-Zi Yen