A model can know when two events occurred yet still answer which came first incorrectly. Multi-hop QA accuracy conflates such composition errors with missing facts. We introduce a double-gate protocol for temporal factual chains and evaluate a case for composition only if the model (i) answers every constituent fact correctly across paraphrased probes and (ii) answers every aligned sub-question correctly in isolation. If the original multi-hop answer remains incorrect, the case is a residual composition failure; we call the systematic occurrence of such failures composition collapse. On D4v2, a benchmark of 390 temporal chains, two post-trained models differ by only 0.3 percentage points in atomic stability yet by 47.4 points in depth-2 (two-fact) residual failure. In a controlled same-base comparison, outcome-verified reinforcement learning reduces depth-4 residual failure by 54 points relative to reasoning-trace SFT under matched data and LoRA budget. On 80 examples that pass both gates for every GRPO variant, failure decreases monotonically, showing that model-specific gate subsets are unlikely to drive the trend. Finally, chain-of-thought decoding recovers 70–75% of gate-passing failures, consistent with an important role for inference-time computation. These results motivate reporting atomic stability and gate-controlled composition separately when evaluating multi-hop reasoning.

Creativity has become a core competence in the era of LLMs and human–AI collaboration, underpinning innovation in real-world problem solving. Crucially, the systematic improvement of creativity necessitates scientifically valid assessment instruments. Psychometric research recognizes context-based assessment as an effective way to measure creative thinking. However, high-quality expert-designed contexts remain scarce. Existing LLM-based generators often struggle with insufficient assessment cues, weak narrative coherence, limited stylistic diversity, and poor support for creative thinking. To address these challenges, we propose AlphaContext, an evolutionary tree-based psychometric context generator for creativity assessment. First, the HyperTree Outline Planner formalizes expert-designed outlining as a rule-guided hypertree and performs top-down hierarchical planning. The MCTS-based Context Generator fills the outline via MCTS to balance global structure and local quality. Then, the Evolutionary Context Optimizer evolves contexts with MAP-Elites by repeatedly updating niche elites to jointly improve diversity and quality. Finally, the Assessment-Guided Evolution Refiner simulates virtual participants with diverse styles and recycles weak contexts for further evolution. Experiments show that AlphaContext yields an average improvement of 8% over competitive methods across 6 quality metrics.