Agent Newsroom: Efficient Chronological Report Generation via Dynamic Multi-Agent Collaboration
Year: 2026
Type: conference-paper
Abstract: Many real-world applications require generating a chronological report from an evolving document stream; Timeline Summarization (TLS) provides a standard testbed for this setting.While large language models (LLMs) improve event synthesis, most LLM-based TLS systems remain monolithic: they repeatedly process overlapping evidence and often mirror the corpus' bursty reporting patterns, producing redundant timelines with temporal/topical imbalance and high cost.We propose MAS-TLS, a multi-agent framework that casts TLS as a newsroom-like collaboration.A master editor steers balanced coverage by allocating systemvisible evidence with a coverage-diversity objective; specialist reporter agents independently draft time-anchored, evidence-grounded events while cross-reviewing to limit redundancy; an adjudication round reconciles competing drafts and consolidates duplicates into a global timeline; and a non-stationary Bayesian controller adaptively staffs agents under token/time budgets.Experiments on three benchmarks show that MAS-TLS improves semantic coverage and temporal grounding while substantially reducing token usage and latency.
Authors: Zhenhua Wang, Chunlei Wang, Yue Geng, Bang Wang
Institution: Huazhong University of Science and Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G5824610820
OpenAlex ID: W7166892031