activity
20242026
most citedModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges

1 citations · 1 across the 4 of their papers we have counts for

collaborators

7 papers

cs.AI2026

RINI: Seeing the Prior Is Not Enough

Hongyi Du, Tianyi Zhang, Heng Wang +7

A research proposal can describe an established mechanism correctly while claiming to introduce it. We study whether providing the earlier paper corrects such contribution claims.…

cs.AI2026

Relic: From Multi-Agent Collaboration to Persistent Organizational Capability

Hongyi Du, Tianyi Zhang, Weijia Zhang +9

Multiple agents may often conflict in an organization: for example, one coding agent changes an interface in a repository, but another continues to develop on the old version where…

cs.LG2026

-Bench: Evaluating Persona-Sensitive Influencing in Persuasive Dialogues

Peixuan Han, Hongyi Du, Jiayu Liu +3

Personalization is a crucial capability of modern language agents. However, current research primarily positions personalized agents as passive responders to user preferences, limi…

cs.AI2025

ProtocolBench: Which LLM MultiAgent Protocol to Choose?

Hongyi Du, Jiaqi Su, Jisen Li +6

As large-scale multi-agent systems evolve, the communication protocol layer has become a critical yet under-evaluated factor shaping performance and reliability. Despite the existe…

cs.AI2025★ 1 cited

ModelingAgent: Bridging LLMs and Mathematical Modeling for Real-World Challenges

Cheng Qian, Hongyi Du, Hongru Wang +6

Recent progress in large language models (LLMs) has enabled substantial advances in solving mathematical problems. However, existing benchmarks often fail to reflect the complexity…

cs.MA2025

MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents

Kunlun Zhu, Hongyi Du, Zhaochen Hong +8

Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks either focus on single-agent tasks or are confined to narrow domains,…

cs.CL2024

EscapeBench: Towards Advancing Creative Intelligence of Language Model Agents

Cheng Qian, Peixuan Han, Qinyu Luo +9

Language model agents excel in long-session planning and reasoning, but existing benchmarks primarily focus on goal-oriented tasks with explicit objectives, neglecting creative ada…