works on

From the 1 of 19 linked papers with an AI index.

most citedAlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment

1 citations · 1 across the 6 of their papers we have counts for

collaborators

19 papers

cs.AI2026

FinDeepIndicator: Benchmarking Deep Research Agents in End-to-End Financial Indicator Construction

Chaoqun Yang, Fengbin Zhu, Xinyu Lin +5

Financial indicators are essential tools for transforming raw financial data into interpretable measures for various downstream tasks, such as valuation, risk assessment, and econo…

cs.IR2026

Beyond Action Imitation: Learning a Decision-Aware User Simulator for Online Advertising

Zipeng Chen, Jiaer Zheng, Xiangyang Xu +15

The paper introduces DASH, a decision-aware user simulator that generates reasoning traces and predicts actions for online advertising by integrating heterogeneous cross-domain his…

cs.IR2026

Autonomous Information Seeking: A Roadmap for Agentic Recommender Systems

Xinyu Lin, Yashar Deldjoo, Sunhao Dai +7

The rapid integration of large language model-based agents into recommender systems has driven a shift from static, ranking-based pipelines toward autonomous and interactive system…

cs.AI2026

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond

Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +47

As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that ma…

cs.CL20261 cited

AlpsBench: An LLM Personalization Benchmark for Real-Dialogue Memorization and Preference Alignment

Jianfei Xiao, Xiang Yu, Chengbing Wang +8

As Large Language Models (LLMs) evolve into lifelong AI assistants, LLM personalization has become a critical frontier. However, progress is currently bottlenecked by the absence o…

cs.CL2026

TacoMAS: Test-Time Co-Evolution of Topology and Capability in LLM-based Multi-Agent Systems

Chen Xu, Yicheng Hu, Ruizi Wang +4

Multi-agent systems (MAS) have emerged as a promising paradigm for solving complex tasks. Recent work has explored self-evolving MAS that automatically optimize agent capabilities…