wuyang
68 lines
1.6 KiB
Markdown
68 lines
1.6 KiB
Markdown
# Paper: Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation
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---
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type: paper
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title: "Agri-SAGE: Simulation-Grounded Multi-Agent LLM for Context-Aware Agricultural Advisory Generation"
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authors: Vedant Balasubramaniam, Geetha Charan, Manojkumar Patil, Rohit P Suresh, V Priyanka, Kodur Sai Vinay Sathvik, Y. Narahari
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year: 2026
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venue: arXiv
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url: https://arxiv.org/abs/2607.00454
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code_url:
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source: arxiv
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collected_at: 2026-07-08
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published_at: 2026-07-01
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updated_at: 2026-07-01
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status: queued
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relevance: high
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topics:
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- agent-evaluation
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- agent-safety
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- computer-use
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- memory
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- multi-agent
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- planning
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- rag
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- reasoning
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- world-model
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methods:
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benchmarks:
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models:
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datasets:
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- cs.AI
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- cs.MA
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related_concepts:
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related_jobs:
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related_experiments:
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related_projects:
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collection_score: 20
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collection_queries: multi-agent-llm
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---
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## One-line Takeaway
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Auto-collected from arXiv because it matched the Agent collection queries. Needs human skim.
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## Why Collected
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- matched queries: multi-agent-llm
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- inferred topics: agent-evaluation, agent-safety, computer-use, memory, multi-agent, planning, rag, reasoning, world-model
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- arXiv categories: cs.AI, cs.MA
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- collection score: 20
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## Review Checklist
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- Does this paper directly inform Agent architecture, evaluation, memory, tools, safety, coding agents, GUI/browser agents, or multi-agent workflows?
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- Does it include a benchmark, dataset, code, or reproducible experimental setup?
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- Should it be promoted from `queued` to `skimmed` or `summarized`?
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## Links
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- arXiv: https://arxiv.org/abs/2607.00454
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