MindTopo, a new benchmark from Microsoft Research, evaluates whether multimodal large language models can do 3D topological reasoning and planning, not just recognize topology in single images. It tests topology across 5 categories: continuity, separation, order, enclosure, and knots, with both reasoning and planning tasks. Results show a persistent gap where models perform better on static recognition than on interactive action sequences that must preserve or change the same topological relations.
QOBLIB was established as a quantum optimization benchmarking resource through a Nature Computational Science publication and a new website that organizes and tracks comparable results. The library reports 2,000+ submitted results and 500+ instances already solved to optimality. By making benchmarks and submissions more accessible, QOBLIB is increasing community participation and raising the bar for future “quantum advantage” claims against updated classical baselines.
Knowledge profiling found that many factual mistakes in frontier LLMs are recall failures (encoded facts not reliably accessible) rather than encoding failures (facts not encoded). 95–98% of facts were encoded in Gemini-3-Pro and GPT-5, yet direct recall failed on 26–34% of facts. The implication is a shift toward interventions that improve knowledge utilization (including thinking/recovery) instead of primarily scaling to add missing knowledge.
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