C trl N ews: LLM -based Multi-Agent Controllable News Writing via Knowledge Gravitational Field (2025)

Abstract: News writing empowered by large language models (LLMs) has emerged as a prevalent trend due to their efficiency and scalability. This paradigm necessitates dynamic information acquisition, knowledge structuring, and precise viewpoint articulation. However, current approaches often rely on superficially retrieved information and oversimplified knowledge enumeration, resulting in shallow, repetitive, and unordered outputs. Additionally, the lack of controllability over narrative viewpoints fails to align with user-defined preferences. To address these limitations, we propose an LLM-based multi-agent controllable news writing framework termed CtrlNews. The framework simulates expert questioning through automated role assignment and question generation followed by a three-layer hierarchical gravitational graph iteratively refined via expansion-reflection cycles. Besides, we elaborate a fine-grained viewpoint control mechanism to precisely regulate bias, emotion, and exaggeration attributes. When composing long-form news articles, the controlled viewpoints are extended via emotion-preserving composition and self-reflection refinement to ensure the consistency of viewpoint control and prevent the dilution of the control effect. Experiments on quality and control effect evaluation, news dissemination effect assessment, and human evaluation demonstrate significant improvements across multiple metrics compared to existing methods.

## introduction news writing is a laborious and time-consuming process that requires meticulous research, factchecking, and multiple rounds of revisions to ensure accuracy, readability, and alignment with editorial standards @xcite . llm online search models ... data multi-agents models tree knowledge gravitational field data multi-agents models ... result lists 1.mul tiple pers pecti ve know ledg 2.str uctur al e 3.con trolla ble view point ... figure 1 : previous machine writing approaches have primarily focused on expanding new information or perspectives through retrieval-augmented generation (rag) and role-playing. both tree and list structures are inadequate for representing complex relational knowledge. in contrast, ctrlnews learns multi-perspective knowledge through perspective-guided knowledge acquisition and organizes this knowledge into a hierarchical gravitational graph. human journalists must navigate complex information landscapes, synthesize diverse perspectives, and adapt content for different audiences @xcite . while news writing traditionally relies on first-hand reporting such as interviews and onthe-ground investigations, automated frameworks are not intended to replace these essential practices. instead, they can complement journalistic workflows by accelerating draft generation, diversifying perspectives, and grounding content with retrieval or fact databases. ai-press @xcite has already demonstrated this auxiliary role. recent advancements in llm-based multi-agent systems (mas) further indicate a strong synergy with news production pipelines @xcite , showing that these architectures can be effectively aligned with journalistic workflows. when appropriately architected, these systems can exhibit specialization, wherein individual agents autonomously develop expertise in specific subtasks such as information retrieval, content composition, and factual verification @xcite @xcite . as illustrated in fig. 1 , llms possess rich parametric knowledge that enables direct generation of article outlines and even complete drafts @xcite . however, they often suffer from a lack of professionalism and hallucination issues @xcite , particularly in journalistic contexts requiring high accuracy. retrieval-augmented generation (rag) methods attempt to mitigate these issues by incorporating external knowledge @xcite yet struggle to retrieve comprehensive information through simple search queries @xcite . recently, ai-press @xcite leverages multi-agent collaboration and rag to rapidly generate news drafts, prioritizing journalistic efficiency. however, its reliance on existing sources limits novelty and impedes deep analysis of complex events @xcite . meanwhile, storm @xcite , co-storm @xcite , and wikiautogen @xcite collectively enhance automated writing through multi-perspective questioning, collaborative discourse simulation, and content integration to improve article coherence, depth, and engagement. however, these approaches often lack adaptive mechanisms to dynamically balance thematic focus and exploratory breadth during iterative expansion @xcite . although omnithink @xcite aims to expand knowledge boundaries through iterative cognitive refinement and dynamic information tree growth, its unchecked expansion causes thematic divergence that reveals the fundamental challenge of balancing exploratory richness with conceptual focus @xcite . despite demonstrating promising capabilities in generating news-style articles, these methods remain hindered by several critical shortcomings:1) shallow multi-agent collaboration: existing multi-agent frameworks prioritize breadth over depth but lack the domainspecific expertise needed for news writing standards @xcite . to address these limitations, we present an automated news writing framework termed ctrlnews to write structured and controllable news articles. we first employ specialized llm experts who conduct multi-round, domain-specific questioning combined with comprehensive search agent retrieval to ensure both depth and breadth of coverage. moving beyond simplistic knowledge structures, we introduce a hierarchical gravitational graph that dynamically organizes information through expansion-reflection cycles to enable nuanced viewpoint derivation through core, intermediate, and peripheral knowledge layers. to prevent viewpoint dilution, we develop a precision control mechanism that regulates narrative attributes while maintaining consistency through iterative verification, followed by emotion-preserving composition and self-reflection refinement to produce high-quality news articles. the main contributions of this paper are as follows: nasa's outrageous decision to pump billions into salvaging the obsolete hubble telescope shamelessly steals funding from groundbreaking projects that could transform 21st century astronomy! nasa's planned hubble servicing mission aims to extend the telescope's operational life, maintaining its unique scientific contributions while working within the agency's established budget allocations for legacy programs. ## methodology we present ctrlnews for automating news writing by first retrieving and synthesizing comprehensive information concerning a given input topic t through multi-round questioning of distinct llm experts along with the llm searching agent (section 3.1). then, the collected information is organized using a hierarchical gravitational graph that simulates human information gathering and enables iterative cognitive structuring (section 3.2). next, our controlled viewpoint mechanism provides precise manipulation of specific nodes within the graph, which employs a control consistency verification to both ensure alignment with predefined controlled viewpoint attributes and mitigate llm hallucination (section 3.3). finally, the controlled viewpoints are extended to a full-length news article via emotion-preserving composition and selfreflection refinement (section 3.4). fig. 2 gives an overview of ctrlnews with detailed pseudo-code available in appendix a. the prompts of all agents are in appendix b. ## conclusion we present ctrlnews, a novel llm-based multiagent framework for controllable news generation. by simulating expert questioning through automated role interactions and employing a hierarchical gravitational graph with iterative refinement, our method achieves deeper knowledge structuring and more coherent article composition. the proposed fine-grained viewpoint control mechanism enables precise regulation of triplet attributes, while emotion-preserving composition and selfreflection refinement maintain consistency in longform content. experimental results validate the framework's superiority across multiple metrics, demonstrating its ability to produce higher-quality, more viewpoint-consistent news articles compared to existing methods.