most citedSkillOps: Managing LLM Agent Skill Libraries as Self-Maintaining Software Ecosystems

4 citations · 4 across the 17 of their papers we have counts for

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cs.AI2026

World Model Science: Self-Organized Criticality, Weak Chaos, and Metastable Belief Dynamics in Long-Horizon LLM Agents

Xinyuan Song, Zekun Cai

Long-horizon LLM agents must maintain task state across extended sequences of observations, actions, tool calls, and intermediate beliefs. We study these trajectories through three…

cs.AI2026

MEMONDEMAND: A Memory Management System for Large-Scale Enterprise Data

Xinyuan Song, Bowen Zhu, Hasibul Haque +1

Enterprise repositories are large, heteroge- neous, and continuously updated, making re- trieval difficult when efficient access, source- faithful evidence, and cross-query adaptat…

cs.AI2026

MegaMem: A Retrieval Solution for Ultra-Large Context Windows

Xinyuan Song, Bowen Zhu, Hasibul Haque +1

Modern language models and agents increasingly require persistent memory for complete codebases, long interaction histories, and heterogeneous enterprise records. The key challenge…

cs.AI2026

Measuring Harness-Induced Belief Divergence in Multi-Step LLM Agents

Haiwen Yi, Xinyuan Song

Software agents are becoming increasingly important in modern coding workflows, where they solve complex tasks through multi-step interaction with code repositories. A central comp…

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…