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WISE proposes a long-horizon agent framework for Minecraft that enhances low-level controllers with a Causal Event Graph for episodic memory, enabling robust recall under viewpoint changes and opportunistic task reordering via causal reasoning. It also features a multi-scale progressive exploration strategy and demonstrates improved success and efficiency on long-horizon sparse tasks.
Introduces HIPIF, a method for training LLM agents to handle long-horizon tasks by hierarchical planning and information folding to reduce long-context interference, achieving strong results on three benchmarks.
A practical sharing on multi-agent AI collaboration, proposing a hierarchical strategy using Opus 4.8 for planning and Deepseek/Gemma for execution, achieving a 10x cost reduction and 2x speed improvement, with open-source implementation.
MM-WebAgent is a hierarchical agentic framework that generates coherent and visually consistent webpages by coordinating AIGC-based element generation through joint optimization of layout and multimodal content. The paper introduces a benchmark and multi-level evaluation protocol, demonstrating improvements over code-generation and agent-based baselines.
HiVLA introduces a hierarchical vision-language-action framework that decouples semantic planning from motor control using a diffusion transformer action expert for improved robotic manipulation. The system combines a VLM planner for task decomposition and visual grounding with a specialized DiT action expert using cascaded cross-attention, outperforming end-to-end baselines particularly in long-horizon tasks and fine-grained manipulation.
Agent S2 is a new compositional framework for computer use agents that achieves state-of-the-art performance on multiple benchmarks by utilizing Mixture-of-Grounding and Proactive Hierarchical Planning.