# ECHO: Event-Centric Hypergraph Operations via Multi-Agent Collaboration for Multimedia Event Extraction

arXiv: https://arxiv.org/abs/2603.06683 · HTML: https://mlsanity.com/paper/2603.06683/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 4 Mar 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 18 of 18)

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- [ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://mlsanity.com/paper/2309.13007/) (2023)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [CLIP-Event: Connecting Text and Images with Event Structures](https://mlsanity.com/paper/2201.05078/) (2022)
- [Cross-media Structured Common Space for Multimedia Event Extraction](https://mlsanity.com/paper/2005.02472/) (2020)
- [UMIE: Unified Multimodal Information Extraction with Instruction Tuning](https://mlsanity.com/paper/2401.03082/) (2024)
- [Training Multimedia Event Extraction With Generated Images and Captions](https://mlsanity.com/paper/2306.08966/) (2023)
- [MMUTF: Multimodal Multimedia Event Argument Extraction with Unified Template Filling](https://mlsanity.com/paper/2406.12420/) (2024)

## Abstract (arXiv)

Multimedia event extraction (M2E2) aims to predict triggers, ground arguments across text and images, and then assemble them into schema-consistent event records. Recent LLM-based approaches have shown strong potential for M2E2, but their intermediate event hypotheses often remain implicit, and event-argument linking is still tightly coupled with role binding. This leaves little opportunity to inspect or revise intermediate event hypotheses and makes predictions brittle to early errors. To bridge this gap, we present ECHO, a multi-agent framework that reframes M2E2 as iterative refinement over an explicit Multimedia Event Hypergraph (MEHG). Instead of relying on implicit linear generation, ECHO performs auditable atomic updates over a shared hypergraph, making intermediate event structures explicit and revisable. Furthermore, we introduce a Link-then-Bind strategy that decouples event-argument linking from role binding, reducing premature semantic commitment during structured prediction. Extensive experiments on the M2E2 benchmark show that ECHO consistently outperforms prior state-of-the-art approaches, achieving gains of 7.3 and 15.5 F1 points on event mention and argument role, respectively.