1 citations · 1 across the 7 of their papers we have counts for
18 papers
Continuous Benchmark Generation for Evaluating Enterprise-scale LLM Agents
Divyanshu Saxena, Rishikesh Maurya, Xiaoxuan Ou +7
The rapid adoption of AI agents across domains has made systematic evaluation crucial for ensuring their usefulness and successful production deployment. Evaluation of AI agents ty…
Simulating Environments with Reasoning Models for Agent Training
Yuetai Li, Huseyin A Inan, Xiang Yue +6
LLM agents excel in compact environments requiring deep reasoning but remain brittle when operating in broader, more complex contexts that demand robustness across diverse tools an…
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…
LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation
Dongge Han, Camille Couturier, Daniel Madrigal Diaz +3
We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories int…
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…
Enhancing Reasoning Capabilities of Small Language Models with Blueprints and Prompt Template Search
Dongge Han, Menglin Xia, Daniel Madrigal Diaz +7
Small language models (SLMs) offer promising and efficient alternatives to large language models (LLMs). However, SLMs' limited capacity restricts their reasoning capabilities and…