3 citations · 3 across the 5 of their papers we have counts for
7 papers
Cop number of -free graphs without a claw or a fork
Uttam K. Gupta, Dinabandhu Pradhan, Rashmi Rekha Swain
In 2019, Liu proved that the cop number of any -free graph is at most . In 2026, Clow and Meger improved and generalized the result of Liu by showing that every c…
Verifiable Process Rewards for Agentic Reasoning
Huining Yuan, Zelai Xu, Huaijie Wang +6
Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning capabilities of large language models (LLMs), yet existing approaches predominantly rely on sparse…
MARSHAL: Incentivizing Multi-Agent Reasoning via Self-Play with Strategic LLMs
Huining Yuan, Zelai Xu, Zheyue Tan +10
Developing Large Language Models (LLMs) to cooperate and compete effectively within multi-agent systems (MASs) is a critical step towards more advanced intelligence. While reinforc…
VS-Bench: Evaluating VLMs for Strategic Abilities in Multi-Agent Environments
Zelai Xu, Zhexuan Xu, Xiangmin Yi +7
Recent advancements in Vision Language Models (VLMs) have expanded their capabilities to interactive agent tasks, yet existing benchmarks remain limited to single-agent or text-onl…
Scalable UAV Multi-Hop Networking via Multi-Agent Reinforcement Learning with Large Language Models
Yanggang Xu, Jirong Zha, Weijie Hong +5
In disaster scenarios, establishing robust emergency communication networks is critical, and unmanned aerial vehicles (UAVs) offer a promising solution to rapidly restore connectiv…
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play
Zelai Xu, Ruize Zhang, Chao Yu +9
Robot sports, characterized by well-defined objectives, explicit rules, and dynamic interactions, present ideal scenarios for demonstrating embodied intelligence. In this paper, we…
ARiADNE: A Reinforcement learning approach using Attention-based Deep Networks for Exploration
Yuhong Cao, Tianxiang Hou, Yizhuo Wang +2
In autonomous robot exploration tasks, a mobile robot needs to actively explore and map an unknown environment as fast as possible. Since the environment is being revealed during e…