SCICONVBENCH is a new benchmark evaluating LLMs on multi-turn disambiguation and inconsistency resolution for task formulation in computational science, with frontier models reaching only 52.7% success on fluid mechanics disambiguation cases.
Matscibench: Benchmark- ing the reasoning ability of large language models in materi- als science
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PolyReal benchmark shows leading MLLMs perform well on polymer knowledge reasoning but drop sharply on practical tasks like lab safety analysis and raw data extraction.
ThermoQA benchmark shows top LLMs reach 92-94% overall on thermodynamics problems but degrade sharply on full cycle analysis, confirming that property knowledge does not equal reasoning ability.
citing papers explorer
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SCICONVBENCH: Benchmarking LLMs on Multi-Turn Clarification for Task Formulation in Computational Science
SCICONVBENCH is a new benchmark evaluating LLMs on multi-turn disambiguation and inconsistency resolution for task formulation in computational science, with frontier models reaching only 52.7% success on fluid mechanics disambiguation cases.
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PolyReal: A Benchmark for Real-World Polymer Science Workflows
PolyReal benchmark shows leading MLLMs perform well on polymer knowledge reasoning but drop sharply on practical tasks like lab safety analysis and raw data extraction.
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ThermoQA: A Three-Tier Benchmark for Evaluating Thermodynamic Reasoning in Large Language Models
ThermoQA benchmark shows top LLMs reach 92-94% overall on thermodynamics problems but degrade sharply on full cycle analysis, confirming that property knowledge does not equal reasoning ability.