Recent diffusion and autoregressive video generators have substantially improved short-form synthesis, yet composing multiple independently generated clips into a coherent audiovisual story remains challenging. Character identities may drift across scenes, narrative events can become inconsistent, and dialogue or sound may not align with the evolving visual context. We present **DramaAgent**, a hierarchical, agentic, and model-agnostic framework for multi-scene long-horizon audiovisual story generation. Rather than modifying the underlying video backbone, DramaAgent introduces a stateful control layer that combines hierarchical story planning, persistent character conditioning, scene-wise generation, and reflection-guided targeted repair. The framework maintains reusable story and character states across scenes, diagnoses failures along identity, semantic, temporal, and audio-visual dimensions, and maps each diagnosed failure to a corresponding localized repair operation. Experiments on 50 narrative scripts across five video-generation backbones show consistent improvements in semantic alignment, character consistency, narrative fidelity, and scene-level audio-visual compatibility. Controlled same-backbone comparisons further show that these gains cannot be explained solely by stronger generators or additional sampling. Our results suggest that persistent state, failure diagnosis, and targeted repair provide a practical control paradigm for coherent multi-scene audiovisual generation.

Large language models (LLMs) have recently shown promise as planners across web, spatial, and embodied domains. However, existing agents often handle information seeking, spatial grounding, and action planning as separate capabilities, limiting their ability to solve long-horizon compositional tasks. To address these challenges, we present **WebWorld**, a web-simulated world model for long-horizon reasoning over web evidence, scene observations, and action plans. WebWorld maintains structured intermediate states across heterogeneous modalities and verifies them through schema-guided hierarchical planning. The framework introduces a Mixture-of-Transformers architecture that coordinates website, scene, and motion experts via multi-modal self-attention. To support training and evaluation, we develop WebWorld-CoT-55K, a large-scale dataset that integrates heterogeneous modalities into schema-guided reasoning traces, and **WebWorldBench**, a curated benchmark for evaluating long-horizon compositional tasks. Training combines cold-start supervised fine-tuning with offline reinforcement learning based on structural, semantic, and planning rewards; inference uses test-time scaling to aggregate multiple reasoning trajectories. Experiments on WebWorldBench show that WebWorld improves the overall score by up to 8.4% over strong LLM-agent baselines, with gains in outdoor reasoning, geolocation, and travel planning, while still leaving substantial room for progress toward human-level performance.

Large Language Models (LLMs) have significantly advanced legal intelligence, but the scarcity of scenario data impedes the progress toward interactive legal scenarios. This paper introduces a Multi-agent Legal Simulation Driver (MASER) to scalably generate synthetic data by simulating interactive legal scenarios. Leveraging real-legal case sources, MASER ensures the consistency of legal attributes between participants and introduces a supervisory mechanism to align participants’ characters and behaviors as well as addressing distractions. A Multi-stage Interactive Legal Evaluation (MILE) benchmark is further constructed to evaluate LLMs’ performance in dynamic legal scenarios. Extensive experiments confirm the effectiveness of our framework.