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PresentAgent-2 is an agentic framework that generates presentation videos from user queries by conducting research, creating multimodal slides, and producing interactive content across single, discussion, and interaction modes.
This paper introduces AutoLLMResearch, an agentic framework that automates the configuration of expensive LLM experiments by learning from low-fidelity environments and extrapolating to high-cost settings. It aims to reduce computational waste and reliance on expert intuition in scalable LLM research.
This paper introduces FoodCHA, a multi-modal LLM agent framework designed for fine-grained food analysis, addressing challenges in hierarchical consistency and attribute discrimination for dietary monitoring.
Chat2Workflow introduces a benchmark and agentic framework for generating executable visual workflows from natural language, showing current LLMs struggle with industrial-grade automation despite intent capture.
This paper introduces Discover and Prove (DAP), an open-source agentic framework for automated theorem proving in Lean 4 that tackles 'Hard Mode' problems where the answer must be discovered independently before formal proof construction. The work releases new Hard Mode benchmark variants and achieves state-of-the-art results while revealing a significant gap between LLM answer accuracy (>80%) and formal prover success (<10%).
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.