We introduce MineCEraft (Minecraft Construction Engineering Benchmark, pronounced mine-see-ee-raft), an easy-to-use, open-source benchmark designed to systematically evaluate the reliability and limitations of LLMs for construction tasks in Minecraft. The MineCEraft benchmark comprises 723 domain-expert hand-crafted natural-language instructions with programmatically verifiable evaluation, spanning 17 distinct task categories, providing a safe and controllable experimental environment for assessing LLMs’ ability to perform realistic construction engineering tasks. With this benchmark, we conduct an in-depth evaluation of state-of-the-art LLMs and perform a detailed error analysis, revealing key failure modes and practical challenges in applying LLMs to construction engineering tasks.

In this work, we identify three representation-level dynamics shared across diverse large language models (LLMs) during the processing of few-shot prompts. These structural behaviors are not explicitly trained, yet they emerge reliably across model families and scales to systematically shape internal representations. Using Sparse Autoencoders (SAEs) as an analytical lens, we uncover three primary phenomena: (1) a decimal periodicity effect, where specific features exhibit a repeating modulo-10 activation pattern; (2) local feature homeostasis, a compensatory push–pull dynamic in which current representations are inversely modulated by prior exemplars; and (3) adaptive feature redistribution, wherein specific features flexibly shift their semantic profiles in response to contextual constraints. We validate these mechanisms across multiple model families and prompt formats. Together, they offer fresh insights into the internal dynamics of in-context learning and raise important caveats for future SAE-based interpretability studies.

The term “linear representation hypothesis” (LRH) has appeared across diverse subfields of artificial intelligence, neuroscience, and cognitive science. But previous works have not consistently treated the LRH as a falsifiable scientific hypothesis; we analyze these inconsistencies and examine their implications for how prior theoretical and methodological results should be interpreted. Based on this analysis, we argue that claims regarding linear representations become well-defined only through careful examination of the model, representation location, feature definition, and evaluation dataset. We therefore propose a more rigorous formalization of the LRH that makes these dependencies explicit and allows the hypothesis to be evaluated as a falsifiable scientific claim. Finally, we identify some non-trivial open problems that warrant further attention from the research community.