activity
20192026
most citedDL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic Prediction

154 citations · 173 across the 15 of their papers we have counts for

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6 papers · 1 filter

cs.AI2026

Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents

Xinyuan Song, Zekun Cai

Long-horizon language agents make interdependent decisions across multiple rounds, forming growing planning graphs in which later decisions depend on earlier states, observations,…

cs.AI2026

Ask the World Before Acting: Environment Probing for Calibrating Agent World Models

Xinyuan Song, Zekun Cai

Long-horizon language agents enable autonomous decision-making in complex environments by planning and executing sequences of interdependent actions. To act reliably, these agents…

cs.AI2026

Agentic vs. Parametric World Models: Hybrid Planning for Reliable Language Agents

Xinyuan Song, Zekun Cai

Language agents, such as ReAct, Reflexion, and RAP, implicitly predict how the world will change as they plan and act, using imagined state transitions to guide their decisions. Th…

cs.AI2026

Understanding Rollout Error in Graph World Models

Xinyuan Song, Zekun Cai

World models are increasingly used for planning by predicting future states under candidate actions. Many planning environments, however, are naturally graph-structured, with agent…

cs.AI2026

World-Model Collapse as a Phase Transition

Xinyuan Song, Zekun Cai

Water looks unchanged as it warms, then at a critical point it boils. We ask whether long-horizon language agents show an analogous transition in their implicit world models. In so…

cs.AI2026

AlgoSkill: Learning to Design Algorithms by Scheduling Human-Like Skills

Xinyuan Song, Zekun Cai, Liang Zhao

Designing an algorithm from a natural-language problem statement requires identifying the problem structure, reading constraints, choosing a suitable paradigm, checking correctness…