2 citations · 2 across the 4 of their papers we have counts for
4 papers
Agent-RewardBench: Towards a Unified Benchmark for Reward Modeling across Perception, Planning, and Safety in Real-World Multimodal Agents
Tianyi Men, Zhuoran Jin, Pengfei Cao +3
As Multimodal Large Language Models (MLLMs) advance, multimodal agents show promise in real-world tasks like web navigation and embodied intelligence. However, due to limitations i…
Large Language Models for Planning: A Comprehensive and Systematic Survey
Pengfei Cao, Tianyi Men, Wencan Liu +7
Planning represents a fundamental capability of intelligent agents, requiring comprehensive environmental understanding, rigorous logical reasoning, and effective sequential decisi…
RAG-RewardBench: Benchmarking Reward Models in Retrieval Augmented Generation for Preference Alignment
Zhuoran Jin, Hongbang Yuan, Tianyi Men +4
Despite the significant progress made by existing retrieval augmented language models (RALMs) in providing trustworthy responses and grounding in reliable sources, they often overl…
Unlocking the Future: Exploring Look-Ahead Planning Mechanistic Interpretability in Large Language Models
Tianyi Men, Pengfei Cao, Zhuoran Jin +3
Planning, as the core module of agents, is crucial in various fields such as embodied agents, web navigation, and tool using. With the development of large language models (LLMs),…