11 papers
Repair the Amplifier, Not the Symptom: Stable World-Model Correction for Agent Rollouts
Xinyuan Song, Zekun Cai
Long-horizon language agents increasingly maintain executable world models in the form of planning graphs, where tool calls, validators, memory updates, recovery branches, and fina…
Ask the World Before Acting: Environment Probing for Calibrated Agent World Models
Xinyuan Song, Zekun Cai
Language agents acting over long horizons must maintain beliefs about tool states, object locations, graph edges, and subgoal dependencies. When these beliefs drift, failures can b…
Agent vs. Parametric World Models: Hybrid Planning for Reliable Language Agents
Xinyuan Song, Zekun Cai
Language agents plan by generating not only actions but also implicit predictions of how the world will change. These imagined state updates make agents flexible, but they also cre…
Understanding Rollout Error in Graph World Models
Xinyuan Song, Zekun Cai
World models are increasingly used for planning, yet most analyses of rollout error assume vector-valued states and scalar error amplification. Many planning environments, however,…
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…
AlgoBench: Benchmarking Algorithmic Adaptation in Code Generation
Xinyuan Song, Zekun Cai, Liang Zhao
High pass rates on established programming benchmarks such as HumanEval and LiveCodeBench do not always show whether a model can reason about algorithms. Many fixed benchmarks even…