activity
20242026
most citedAdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making

1 citations · 1 across the 12 of their papers we have counts for

collaborators

13 papers

cs.CL2026

Grounded Normative Rule Generation with Structured Search

Fanqi Kong, Huaxiao Yin, Ruijie Zhang +5

Normative rules like institutional charters and workplace policies must be both human-readable and operationally verifiable against actual environment records. However, current lan…

cs.MA2026

Adaptive Punishment for Cooperation in Mixed-Motive Games

Min Tang, Fanqi Kong, Linyuan Lü +1

Mixed-motive scenarios are ubiquitous in real-world multi-agent interactions, where self-interested agents often defect for immediate rewards, overlooking the potential of altruist…

cs.AI2026

Scalable Environments Drive Generalizable Agents

Jiayi Zhang, Fanqi Kong, Guibin Zhang +7

Generalizable agents should adapt to diverse tasks and unseen environments beyond their training distribution. This position paper argues that such generalization requires environm…

cs.LG2026

When Does Value-Aware KV Eviction Help? A Fixed-Contract Diagnostic for Non-Monotone Cache Compression

Ruijie Zhang, Haozhe Liang, Da Chang +4

Long-context LLM inference is bottlenecked by the memory and bandwidth cost of reading large KV caches during decoding. KV compression reduces this cost by keeping only part of the…

cs.LG2026

Co-Evolution of Policy and Internal Reward for Language Agents

Xinyu Wang, Hanwei Wu, Jingwei Song +8

Large language model (LLM) agents learn by interacting with environments, but long-horizon training remains fundamentally bottlenecked by sparse and delayed rewards. Existing metho…

cs.AI2026

InfoPO: Information-Driven Policy Optimization for User-Centric Agents

Fanqi Kong, Jiayi Zhang, Mingyi Deng +3

Real-world user requests to LLM agents are often underspecified. Agents must interact to acquire missing information and make correct downstream decisions. However, current multi-t…

cs.AI2025

ToMPO: Training LLM Strategic Decision Making from a Multi-Agent Perspective

Yiwen Zhang, Ziang Chen, Fanqi Kong +2

Large Language Models (LLMs) have been used to make decisions in complex scenarios, where they need models to think deeply, reason logically, and decide wisely. Many existing studi…

cs.SI2025

Enhancing LLM-Based Social Bot via an Adversarial Learning Framework

Fanqi Kong, Xiaoyuan Zhang, Xinyu Chen +3

Developing Large Language Model (LLM) agents that exhibit human-like behavior, encompassing not only individual heterogeneity rooted in unique user profiles but also adaptive respo…

cs.RO2025

Aegis: Automated Error Generation and Attribution for Multi-Agent Systems

Fanqi Kong, Ruijie Zhang, Huaxiao Yin +7

Large language model based multi-agent systems (MAS) have unlocked significant advancements in tackling complex problems, but their increasing capability introduces a structural fr…

cs.CV2025

SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning

Fanqi Kong, Weiqin Zu, Xinyu Chen +3

Understanding social interaction, which encompasses perceiving numerous and subtle multimodal cues, inferring unobservable mental states and relations, and dynamically predicting o…

cs.MA2024★ 1 cited

AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-Making

Yizhe Huang, Xingbo Wang, Hao Liu +7

Traditional interactive environments limit agents' intelligence growth with fixed tasks. Recently, single-agent environments address this by generating new tasks based on agent act…

cs.AI2024

Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games

Fanqi Kong, Yizhe Huang, Song-Chun Zhu +2

Real-world multi-agent scenarios often involve mixed motives, demanding altruistic agents capable of self-protection against potential exploitation. However, existing approaches of…

cs.AI2024

Efficient Adaptation in Mixed-Motive Environments via Hierarchical Opponent Modeling and Planning

Yizhe Huang, Anji Liu, Fanqi Kong +3

Despite the recent successes of multi-agent reinforcement learning (MARL) algorithms, efficiently adapting to co-players in mixed-motive environments remains a significant challeng…