Siris tests whether LLMs spot invalid simulator recommendations
Original title: SIRIS: Can LLMs Tell When Simulation Errors Matter in AI-Driven Systems Research
On LLM serving design problems, spotting a simulator limitation did not guarantee the right design, and a review turn added new errors.
Siris checks whether LLMs can tell when a simulator limitation leaves a recommended design valid or makes it wrong. Perturbations create workload pairs that share candidates, a limitation and a simulator recommendation, while a reference simulator supports that recommendation on only one workload. Thirteen frontier models were tested; self-correction restored some valid choices and introduced errors in others.
Unconfirmed: Model identities beyond "thirteen frontier LLMs" are not named.
- The benchmark uses paired workloads with identical candidates and the same limitation.
- Predictions change on both workloads, but only one recommendation is reference-supported.
- Even the strongest models sometimes abandoned recommendations the reference still supported.
- A review turn helped some models and created new mistakes in several others.
From the original article
1 min read at huggingface.co
LLM agents increasingly drive systems research and rely on simulators to evaluate candidate designs at scale. Simulators are imperfect by design: their modeling limitations can distort predictions yet leave the recommended design correct, or change which design should be selected.
By Jinwoo Hwang, Sukmin Cho, Tao Zhang · huggingface.co
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