Agri-SAGE is a multi-agent LLM framework grounded in APSIM biophysical simulations that outperforms static Package-of-Practice baselines across three reasoning strategies in a 10-year retrospective agricultural evaluation.
InThe Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track
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Presents CultivAgents, a relationship-centered multi-agent system for socio-culturally grounded gardening support, with a mixed-methods evaluation showing modest gains in gardener confidence and motivation.
A survey of 232 papers frames the 'last mile' challenge of deploying multilingual language models under edge hardware constraints for Global South communities.
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Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation
Agri-SAGE is a multi-agent LLM framework grounded in APSIM biophysical simulations that outperforms static Package-of-Practice baselines across three reasoning strategies in a 10-year retrospective agricultural evaluation.
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CultivAgents: Cultivating Relationship-Centered Multi-Agent Systems for Personalized Gardening
Presents CultivAgents, a relationship-centered multi-agent system for socio-culturally grounded gardening support, with a mixed-methods evaluation showing modest gains in gardener confidence and motivation.
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Multilinguality at the Edge: Developing Language Models for the Global South
A survey of 232 papers frames the 'last mile' challenge of deploying multilingual language models under edge hardware constraints for Global South communities.
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