{"total":16,"rows":[{"id":"2606.24597","title":"Qwen-AgentWorld: Language World Models for General Agents","created":"2026-06-23","cited_by_count":28,"area":"wm"},{"id":"2602.14721","title":"WebWorld: A Large-Scale World Model for Web Agent Training","created":"2026-02-16","cited_by_count":26,"area":"wm"},{"id":"2601.08955","title":"Imagine-then-Plan: Agent Learning from Adaptive Lookahead with World Models","created":"2026-01-13","cited_by_count":8,"area":"wm"},{"id":"2606.25421","title":"Beyond Next-Observation Prediction: Agent-Authored World Modeling for Sequential Decision Making","created":"2026-06-24","cited_by_count":8,"area":"wm"},{"id":"2601.13247","title":"Aligning Agentic World Models via Knowledgeable Experience Learning","created":"2026-01-19","cited_by_count":5,"area":"wm"},{"id":"2605.24375","title":"Distilling Game Code World Model Generation into Lightweight Large Language Models","created":"2026-05-23","cited_by_count":5,"area":"wm"},{"id":"2606.09032","title":"Bridging the Agent-World Gap: Text World Models for LLM-based Agents","created":"2026-06-08","cited_by_count":5,"area":"wm"},{"id":"2512.20615","title":"Active Intelligence in Video Avatars via Closed-loop World Modeling","created":"2025-12-23","cited_by_count":3,"area":"wm"},{"id":"2606.27483","title":"Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning","created":"2026-06-25","cited_by_count":3,"area":"wm"},{"id":"2607.24720","title":"The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation","created":"2026-07-27","cited_by_count":3,"area":"wm"},{"id":"2608.06197","title":"EnvACE: Internalizing Environment Dynamics via World Rehearsal for Agentic Reinforcement Learning","created":"2026-08-06","cited_by_count":2,"area":"wm"},{"id":"2605.03782","title":"What You Think is What You See: Driving Exploration in VLM Agents via Visual-Linguistic Curiosity","created":"2026-05-05","cited_by_count":1,"area":"wm"},{"id":"2608.30067","title":"How do World Models and Policies Compose in LLM Agents? A Joint Spectral and Behavioral Account","created":"2026-08-30","cited_by_count":0,"area":"wm"},{"id":"2609.01608","title":"WMLLM: Self-Evolving Optimization Agents via Predict-Then-Act World Modeling","created":"2026-06-02","cited_by_count":0,"area":"wm"},{"id":"2609.33335","title":"Does Learning to Predict the World Help Agents Act? Auditing World-Model Post-Training","created":"2026-09-27","cited_by_count":0,"area":"wm"},{"id":"2610.12417","title":"WOVEN: Weaving Visual World Modeling into Multimodal LLMs","created":"2026-10-08","cited_by_count":0,"area":"wm"}],"next_cursor":null}