Solo Performance Prompting (SPP) is a zero-shot prompting technique that turns a single LLM into a multi-persona self-collaboration agent through three phases: (i) persona identification, where the LLM dynamically proposes task-relevant personas (e.g. "Film Expert," "Logic Puzzle Expert") from the task description; (ii) brainstorming, where each persona contributes domain knowledge or approach; (iii) multi-persona iterative collaboration, where an "AI Assistant" leader persona drafts a solution and consults the other personas turn-by-turn for feedback until they converge. The prompt template includes two demonstration examples (Game of 24, poem writing) and is identical across tasks. Evaluated on three tasks — Trivia Creative Writing (knowledge), Codenames Collaborative (knowledge + reasoning + theory-of-mind), Logic Grid Puzzle (reasoning) — against Standard, CoT, and Self-Refine baselines. Headline GPT-4 results: Trivia Creative Writing N=5 79.9% (+7.1% over Standard), N=10 84.7% (+10.0%), Codenames Collaborative 79.0% (+4.8%), Logic Grid Puzzle 68.3% (+18.5%). CoT loses on the knowledge tasks (67.1% / 68.5% on Trivia, 72.7% on Codenames) and Self-Refine hurts Codenames substantially (64.6%, –14.6%). Cognitive-synergy effect appears only on GPT-4; GPT-3.5-turbo and Llama2-13b-chat show no gains, and Llama2 exhibits an "early-termination" failure where the model stops generating after listing the personas as if waiting for external input. Ablations: SPP-Fixed-Persona (forced "AI Assistant" + "Expert") consistently underperforms SPP; SPP-Profile (adding persona profiles) does not improve over bare persona names. Word-cloud analysis (Figure 7a) shows knowledge-intensive tasks elicit diverse fine-grained personas while reasoning-intensive tasks elicit homogeneous personas ("Logic Puzzle Expert" dominates Logic Grid Puzzle even though "logic puzzle" is not in the input). Authors: Wang and Ji at University of Illinois Urbana- Champaign; Mao, Wu, Ge (corresponding), and Wei at Microsoft Research Asia. Work done while Wang interned at MSRA. Limitations the authors flag: persona-assignment's contribution to domain knowledge is not quantified ("dedicated diagnostic experiments and theoretical efforts are needed"); identical SPP prompt and demos across tasks may be suboptimal; multi-agent extension (separate LLM instances per persona) is left to future work. The paper does not measure persona coherence, overlap, or whether dialogue turns are mechanistically distinguishable beyond stylistic variation.