Recursive Transformers depend not only on how many parameters are shared, but also on how shared parameters are organized across depth. Existing static schemes adopt two extreme reuse topologies: layer-wise repetition, where each layer is reused independently, and global cyclic reuse, where the entire stack is repeatedly applied as a single shared function. We argue that sharing granularity is an important factor in recursive parameter sharing. This study proposes Block Recursive Transformers (BRT), which partition layers into contiguous blocks and recursively reuse each block before moving to the next. This intermediate topology enables local refinement within blocks while preserving depth-wise transitions, without adding routing modules, auxiliary states, or extra training objectives. Across decoder-only models up to 1.7B parameters, with additional encoder-decoder and encoder-only studies, BRT improves validation negative log-likelihood and downstream accuracy over static recursive sharing baselines. Ablations and representation analyses further show that intermediate block granularity better balances local refinement and depth-wise differentiation. Project website: https://aim-skku.github.io/BRT

Most existing benchmarks evaluate social bias in large language models (LLMs) using multiple-choice questions (MCQ) grounded in stereotypical contexts. While MCQ formats enable straightforward quantification, how LLMs manifest social bias in task-specific contexts and under different output constraints remains underexplored. We introduce SCENE, a benchmark for measuring the social bias shown by LLMs in scenario-completion tasks. Given short scenarios involving social groups, models must complete key elements of the situation through two tasks: (i) Character Specification, which asks models to assign social attributes to characters, and (ii) Plot Continuation, which asks models to select the most plausible narrative continuation. Combining templates adapted from prior benchmarks with newly collected templates through a university-wide competition, we construct a dataset of over 75,000 Korean instances. Experiments show that bias measurements are highly sensitive to task context and output constraints, even for identical underlying scenarios, suggesting that widely used MCQ-based evaluations may not fully capture certain manifestations of stereotypes. SCENE is publicly available at https://github.com/DSAIL-SKKU/SCENE-EMNLP-2026.