analiza aiqbench
VEHBench introduces a stage-specific diagnostic benchmark for evaluating large language models in the iterative design of vibration energy harvesters, addressing gaps where current tools only assess final artifacts rather than workflow behaviors. By utilizing an analytical physical oracle to score 763 literature-grounded tasks across four distinct engineering roles, this resource reveals that LLM capabilities are highly dependent on specific phases of coupled physical design. This benchmark offers a critical foundation for selecting and improving verifier-grounded models in battery-free IoT applications without relying solely on final output validity.
VEHBench: A Stage-Local Diagnostic Benchmark for LLM-Assisted Vibration Energy Harvester Design
arXiv:2607.18181v1 Announce Type: new Abstract: Battery-free Internet of Things (IoT) requires iterative design of vibration energy harvesters (VEHs) under coupled physical constraints, while LLMs are emerging as interface layers for engineering workflows. However, existing engineering benchmarks primarily assess final artifact validity, offering limited insights into how LLMs behave across different stages of coupled physical design. We introduce VEHBench, an engineering-native diagnostic benchmark for LLM-assisted VEH design, featuring 763 literature-grounded tasks scored by an analytical physical oracle. VEHBench evaluates four design roles: specification triage, verifier-guided search, corrupted-state recovery, and policy-conditioned selection. Experimental results reveal that LLM capability is strongly stage-dependent: no single model consistently dominates the entire workflow, and response-control profiles expose distinct behavioral patterns across design roles. VEHBench thus provides a stage-aware foundation for evaluating, selecting, routing, and improving verifier-grounded engineering LLMs. The benchmark artifact is available at https://huggingface.co/datasets/AnonymousVehbench/vehbench
Abstract:Battery-free Internet of Things (IoT) requires iterative design of vibration energy harvesters (VEHs) under coupled physical constraints, while LLMs are emerging as interface layers for engineering workflows. However, existing engineering benchmarks primarily assess final artifact validity, offering limited insights into how LLMs behave across different stages of coupled physical design. We introduce VEHBench, an engineering-native diagnostic benchmark for LLM-assisted VEH design, featuring 763 literature-grounded tasks scored by an analytical physical oracle. VEHBench evaluates four design roles: specification triage, verifier-guided search, corrupted-state recovery, and policy-conditioned selection. Experimental results reveal that LLM capability is strongly stage-dependent: no single model consistently dominates the entire workflow, and response-control profiles expose distinct behavioral patterns across design roles. VEHBench thus provides a stage-aware foundation for evaluating, selecting, routing, and improving verifier-grounded engineering LLMs. The benchmark artifact is available at this https URL
Computation and Language (cs.CL); Software Engineering (cs.SE)
arXiv:2607.18181 [cs.CL]
(or arXiv:2607.18181v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.18181 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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