agent/papers/items/2026-2607-00454-agri-sage-simulation-grounded-multi-agent-llm-for-context-aware-agricultural-adv.md

Paper: Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation

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type: paper title: "Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation" authors: Vedant Balasubramaniam, Geetha Charan, Manojkumar Patil, Rohit P Suresh, V Priyanka, Kodur Sai Vinay Sathvik, Y. Narahari year: 2026 venue: arXiv url: https://arxiv.org/abs/2607.00454 code_url: source: arxiv collected_at: 2026-07-08 published_at: 2026-07-01 updated_at: 2026-07-01 status: queued relevance: high topics:

• agent-evaluation
• agent-safety
• computer-use
• memory
• multi-agent
• planning
• rag
• reasoning
• world-model methods:

benchmarks:

models:

datasets:

• cs.AI
• cs.MA related_concepts:

related_jobs:

related_experiments:

related_projects:

collection_score: 20 collection_queries: multi-agent-llm

One-line Takeaway

Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.

Why Collected

• matched queries: multi-agent-llm
• inferred topics: agent-evaluation, agent-safety, computer-use, memory, multi-agent, planning, rag, reasoning, world-model
• arXiv categories: cs.AI, cs.MA
• collection score: 20

Review Checklist

• Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
• Does it include a benchmark, dataset, code, or reproducible experimental setup?
• Should it be promoted from queued to skimmed or summarized?

Links

• arXiv: https://arxiv.org/abs/2607.00454