HULAT2 at MER-TRANS 2026: Governed Multi-Agent Simplification for Spanish Easy-to-Read Generation
arXiv:2607.02381v1 Announce Type: new Abstract: This paper describes the participation of HULAT2-UC3M in the Spanish track of MER-TRANS 2026, a shared task on multilingual Easy-to-Read translation. Three fully automatic Spanish runs were submitted. RUN1 and RUN2 used a LangGraph-based multi-agent workflow combining Gemini 2.5 Flash and RigoChat-7B-v2, parallel generation strategies, internal quality signals, Event-Condition-Action routing, controlled editing and traceable decisions. RUN1 used the base workflow, while RUN2 activated an additional lexical-support layer based on a glossary and le
The proliferation of advanced large language models (LLMs) like Gemini 2.5 Flash and RigoChat-7B-v2 is enabling sophisticated multi-agent AI systems to tackle complex linguistic tasks.
This development showcases the increasing capability of AI agents to perform nuanced, purpose-driven language generation, with potential implications for accessibility, content creation, and automated workflows.
The ability to generate 'Easy-to-Read' content via governed multi-agent systems highlights a step towards specialized, reliable AI outputs, moving beyond raw generative capabilities.
- · AI agents developers
- · Accessibility technology providers
- · Content creators
- · Language service providers
- · Monolingual content creators
- · Generic translation software
- · Manual simplification services
Improved accessibility and comprehension of complex information for broader demographics.
Increased demand for specialized AI models and governed multi-agent orchestration frameworks.
Potential for automated, culturally sensitive content adaptation across various industries and regions.
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