9 citations · 15 across the 31 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…
Text2Grad: Reinforcement Learning from Natural Language Feedback
Hanyang Wang, Lu Wang, Chaoyun Zhang +5
Traditional RLHF optimizes language models with coarse, scalar rewards that mask the fine-grained reasons behind success or failure, leading to slow and opaque learning. Recent wor…
Minerva: A Programmable Memory Test Benchmark for Language Models
Menglin Xia, Victor Ruehle, Saravan Rajmohan +1
How effectively can LLM-based AI assistants utilize their memory (context) to perform various tasks? Traditional data benchmarks, which are often manually crafted, suffer from seve…
DI-BENCH: Benchmarking Large Language Models on Dependency Inference with Testable Repositories at Scale
Linghao Zhang, Junhao Wang, Shilin He +13
Large Language Models have advanced automated software development, however, it remains a challenge to correctly infer dependencies, namely, identifying the internal components and…