1 citations · 1 across the 3 of their papers we have counts for
5 papers
Apodex 1.1: Scaling Agentic Intelligence for Complex Work
B. An, B. Li, B. Wang +72
General-purpose language models can reason and synthesize knowledge, but complex work also requires sustained interaction with files, information sources, and executable code, toge…
Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application
Jiachun Li, Zhuoran Jin, Tianyi Men +12
Environments serve as interactive systems for large language model (LLM) based agents across diverse scenarios and play a crucial role in driving the continual evolution of model c…
MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome
Fangda Ye, Yuxin Hu, Pengxiang Zhu +19
Recent progress in deep research systems has been impressive, but evaluation still lags behind real user needs. Existing benchmarks predominantly assess final reports using fixed r…
ExpertLongBench: Benchmarking Language Models on Expert-Level Long-Form Generation Tasks with Structured Checklists
Jie Ruan, Inderjeet Nair, Shuyang Cao +14
This paper introduces ExpertLongBench, an expert-level benchmark containing 11 tasks from 9 domains that reflect realistic expert workflows and applications. Beyond question answer…
Evaluating the Retrieval Robustness of Large Language Models
Shuyang Cao, Karthik Radhakrishnan, David Rosenberg +4
Retrieval-augmented generation (RAG) generally enhances large language models' (LLMs) ability to solve knowledge-intensive tasks. But RAG may also lead to performance degradation d…