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cs.CL2025
Cost-Aware Retrieval-Augmentation Reasoning Models with Adaptive Retrieval Depth
Helia Hashemi, Victor Rühle, Saravan Rajmohan
Reasoning models have gained significant attention due to their strong performance, particularly when enhanced with retrieval augmentation. However, these models often incur high c…
cs.CL2025
OdysseyBench: Evaluating LLM Agents on Long-Horizon Complex Office Application Workflows
Weixuan Wang, Dongge Han, Daniel Madrigal Diaz +3
Autonomous agents powered by large language models (LLMs) are increasingly deployed in real-world applications requiring complex, long-horizon workflows. However, existing benchmar…
cs.CL2025
Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models
Camille Couturier, Spyros Mastorakis, Haiying Shen +2
Large Language Models (LLMs) are increasingly deployed across edge and cloud platforms for real-time question-answering and retrieval-augmented generation. However, processing leng…