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.
SWRouter: Similarity-Contractive Window Routing for Multi-Turn Large Language Model Conversations
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.
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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.
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.
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...
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.
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.
arXiv Machine Learning
Jun 11
Context-Driven Incremental Compression for Multi-Turn Dialogue Generation
arXiv:2606. 12411v1 Announce Type: cross Abstract: Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with conversation length.
arXiv Machine Learning
Sep 3
GMTRouter: Personalized LLM Router over Multi-turn User Interactions
GMTRouter is a personalized large language model router that represents multi‑turn user‑LLM interactions as a heterogeneous graph with five node types—user, LLM, query, response, and turn—to preserve relational structure. Using a lightweight inductive graph learning framework and a user‑conditioned graph sampling mechanism, it captures user preferences from few‑shot data, enabling effective personalization without extensive fine‑tuning. Experiments show GMTRouter outperforms strong baselines, improving accuracy by up to 0.108 and AUC by 0.124, and adapts to new users with minimal data.
arXiv AI
Jul 2
SWE-Router: Routing in Multi-turn Agentic Software Engineering Tasks
arXiv:2607. 00053v1 Announce Type: cross Abstract: Large language models (LLMs) embedded in multi-turn agentic harnesses are reshaping software engineering (SWE), but routing every task to a frontier model is wasteful when many issues admit cheap fixes.
Hugging Face Trending Papers
Jun 10
Context-Driven Incremental Compression for Multi-Turn Dialogue Generation
Modern conversational agents condition on an ever-growing dialogue history at each turn, incurring redundant attention and encoding costs that grow with conversation length. Naive truncation or summarization degrades fidelity, while existing context compressors lack cross-turn memory sharing or revision, causing information loss and compounding errors in long dialogues.
arXiv AI
Sep 15
CompCQR: Compositional Query Generation for Training-Free Conversational Search
arXiv:2609.14646v1 Announce Type: cross Abstract: Multi-turn interactions with LLMs are becoming increasingly common in information-seeking scenarios. However, user queries are often ambiguous and co...
arXiv AI
Jul 14
Agentic Routing: The Harness-Native Data Flywheel
arXiv:2607. 11399v1 Announce Type: cross Abstract: Large language model agents are increasingly executed not by a single model call, but by an execution harness that manages observation, context, control, action, state, and verification.