Mimir: Physics-Grounded LLM Agents for Long-Horizon Irrigation Control
Quick summary
arXiv:2610.02038v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly combine reasoning, tool use, and action, but most evidence comes from episodic tasks with relatively immediate feedback and reset failures. Long-running physical control operates in a different regime: actions alter future states, errors compound across decisions, and an agent must improve from experience without being allowed to rewrite the physical rules that make execution safe. We study this regime through irrigation, where daily decisions interact with soil-water dynamics over entire growing sea
Key takeaways
- arXiv:2610.02038v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly combine reasoning, tool use, and action, but most evidence comes from episodic tasks with relatively immediate feedback and reset failures.
- Long-running physical control operates in a different regime: actions alter future states, errors compound across decisions, and an agent must improve from experience without being allowed to rewrite the physical rules that make execution safe.
- We study this regime through irrigation, where daily decisions interact with soil-water dynamics over entire growing sea
Why it matters
“Mimir: Physics-Grounded LLM Agents for Long-Horizon Irrigation Control” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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