cs.MAMar 20, 2026
SaveHelix: A Dual-Helix Co-Evolutionary Multi-Agent System for Prompt Optimization and Question Reformulation
Organizations: College of Intelligence and Computing, Tianjin University, Tianjin, China
Abstract
Automated prompt optimization (APO) aims to improve large language model performance by refining prompt instructions. However, existing methods are largely constrained by fixed prompt templates, limited search spaces, or single-sided optimization that treats user questions as immutable inputs. In practice, question formulation and prompt design are inherently interdependent: clearer question structures facilitate focused reasoning and task understanding, while effective prompts reveal better ways to organize and restate queries. Ignoring this coupling fundamentally limits the effectiveness and adaptability of current APO approaches. We propose a unified multi-agent system (Helix) that jointly optimizes question reformulation and prompt instructions through a structured three-stage co-evolutionary framework. Helix integrates (1) planner-guided decomposition that breaks optimization into coupled question-prompt objectives, (2) dual-track co-evolution where specialized agents iteratively refine and critique each other to produce complementary improvements, and (3) strategy-driven question generation that instantiates high-quality reformulations for robust inference. Extensive experiments on 12 benchmarks against 6 strong baselines demonstrate the effectiveness of Helix, achieving up to 3.95% performance improvements across tasks with favorable optimization efficiency.
Figures & tables
Figure 1: Comparison of three strategies for pronoun disambiguation. Left: original question without prompt instructions yields an incorrect answer. Middle: adding a CoT prompt to the original question still fails. Right: Helix jointly optimizes question formulation and prompt instructions, producing the correct prediction.
Figure 2: Overview of the Helix framework including LLM-based agents for joint optimization of question reformulation and prompt instructions. The ① Planner decomposes the task into a sequence of helix objectives, dual-helix co-evolution alternates between ② Prompt-Architect and ③ Question-Architect with ④ Mediator validation, and the ⑤ Question-Generator together with the ⑥ Question-Judge produces validated refined questions, which are paired with the optimized prompt and fed to the target LLM for inference.
| Models | BBH | AGIEval | MMLU | MMLU-Pro | Math | Avg | |||||||
| D.QA | G.S. | F.F. | R.N. | S.U. | L.A. | C.B. | E.E. | M.T. | Hist | Phil | A.R. | ||
| Manual Prompting | |||||||||||||
| No Prompt | 53.20 | 52.40 | 77.40 | 82.80 | 78.80 | 35.09 | 89.58 | 76.55 | 88.32 | 55.12 | 54.11 | 86.61 | 69.17 |
| CoT | 74.80 | 54.80 | 82.00 | 87.20 | 80.00 | 33.22 | 91.36 | 82.07 | 90.06 | 57.74 | 57.31 | 87.40 | 73.16 |
| Automated Prompt Optimization | |||||||||||||
| APE | 56.50 | 61.20 | 82.40 | 83.60 | 81.40 | 35.61 | 89.23 | 78.23 | 89.23 | 58.40 | 58.43 | 84.32 | 71.55 |
Table 1: Accuracy (%) comparison across 12 tasks. Helix with dual optimization ( Q-Opt + P-Opt ) consistently achieves the best performance across all benchmarks. Bold denotes the highest result for each task. Abbreviations: D.QA = Disambiguation QA, G.S. = Geometric Shapes, F.F. = Formal Fallacies, R.N. = Ruin Names, S.U. = Sports Understanding, L.A. = LSAT-AR, C.B. = College Biology, E.E. = Electrical Engineering, M.T. = Marketing, Hist = History, Phil = Philosophy, A.R. = AQuA-RAT.
Figure 3: Accuracy–cost trade-off across 12 tasks for different prompt optimization methods. Bubble size denotes the number of training samples, with Helix achieving the highest accuracy with the fewest API calls using only a single sample.
Figure 4: Prompt efficiency (PE) comparison on four representative BBH tasks, where Helix consistently achieves the highest performance per optimization cost.
| helix | C.B. | M.T. | E.E. | Hist | Phil |
| Full | 93.06 | 95.23 | 85.52 | 64.04 | 65.21 |
| 1 helix | 85.42 | 87.34 | 77.85 | 55.82 | 56.73 |
| (-7.64) | (-7.89) | (-7.67) | (-8.22) | (-8.48) | |
| 2 helices | 89.58 | 91.45 | 81.38 | 59.11 | 59.88 |
| (-3.48) | (-3.78) | (-4.14) | (-4.93) | (-5.33) | |
| 3 helices | 93.06 | 95.23 | 83.79 | 61.47 | 62.05 |
Table 2: Accuracy (%) of helix number on MMLU and MMLU-Pro tasks. denotes the performance drop relative to full Helix.
| Model | C.B. | E.E. | M.T. | Hist | Phil | Avg | |
| APE | 100 | 89.23 | 78.23 | 89.23 | 58.40 | 58.43 | 74.70 |
| OPRO | 50 | 91.15 | 81.45 | 90.12 | 58.84 | 59.44 | 76.20 |
| PE2 | 100 | 91.24 | 79.44 | 91.54 | 59.25 | 56.43 | 75.58 |
| MARS | 1 | 91.36 | 73.79 | 91.88 | 61.32 | 62.63 | 76.20 |
| Helix | 1 | 93.06 | 85.52 | 95.23 | 64.04 | 65.21 | 80.61 |
| Helix | 2 | 94.44 | 87.24 | 96.58 | 65.47 | 66.76 | 82.10 |
Table 3: Accuracy (%) of different training sample sizes on MMLU and MMLU-Pro tasks.
Figure 5: Training-stage dual-helix co-evolution on the Formal Fallacies task. The Planner decomposes the task into sequential helix objectives with coupled question reformulation and prompt refinement goals. Prompt-Architect and Question-Architect iteratively critique and refine each other under Mediator validation, yielding aligned optimized prompts and transferable question reformulation strategies.
Figure 6: Inference-stage strategy-driven question generation on the Formal Fallacies task. The Question-Generator reformulates the original query following the learned strategy, while the -JudgeQuestion-Judge validates semantic preservation and structural quality, producing an optimized question for final model inference.
| Variation | D.QA | G.S. | F.F. | R.N. | S.U. | Avg |
| Full | 73.20 | 73.20 | 94.05 | 93.20 | 92.40 | 85.21 |
| w/o Planner | 66.00 | 68.40 | 85.20 | 89.80 | 84.60 | 78.80 |
| (-7.20) | (-4.80) | (-8.85) | (-3.40) | (-7.80) | (-6.41) | |
| w/o P-Arch | 64.80 | 69.20 | 82.60 | 87.80 | 78.50 | 76.58 |
| (-8.40) | (-4.00) | (-11.45) | (-5.40) | (-13.90) | (-8.63) | |
| w/o Q-Arch | 66.40 | 70.80 | 84.20 | 88.60 | 75.60 | 77.12 |
Table 4: Component ablation on BBH tasks (accuracy %). denotes performance drop relative to the full Helix framework.
Appendix figures & tables56 assets
Supplementary material from the paper’s appendix.
Appendix
| Tasks | ABBR. | Train | Test |
| Bigbench | |||
| Disambiguation QA | D.QA | 1 | 249 |
| Geometric Shapes | G.S. | 1 | 249 |
| Formal Fallacies | F.F. | 1 | 249 |
| Ruin Names | R.N. | 1 | 249 |
| Sports Understanding | S.U. | 1 | 249 |
Table 5: Data split of tasks. One instance for training, remaining instances for testing.
| Models | BBH | AGIEval | MMLU ∗ 7 7 7 MMLU ∗ denotes average performance across MMLU and MMLU-Pro tasks. | AQuA-RAT | Avg |
| No Prompt | 70.16 | 45.65 | 69.72 | 86.61 | 68.04 |
| CoT | 71.36 | 45.22 | 72.68 | 87.40 | 69.17 |
| APE | 72.45 | 43.44 | 71.21 | 85.92 | 68.26 |
| OPRO | 74.12 | 44.35 | 73.40 | 86.78 | 69.66 |
| PE2 | 75.53 | 46.78 | 74.40 | 88.23 | 71.24 |
| MARS | 77.22 | 45.33 | 74.27 | 89.15 | 71.49 |
Table 6: Performance comparison when using Gemini-2.5-Pro for all Helix agents, with Gemini-2.0-Flash as target LLM (accuracy %).
| Method | GPT-3.5 | GPT-4 | GPT-4o | Avg |
| No Prompt | 82.64 | 84.72 | 89.58 | 85.65 |
| CoT | 85.42 | 87.85 | 92.71 | 88.66 |
| MARS | 86.11 | 88.54 | 93.40 | 89.35 |
| Helix | 87.85 | 90.28 | 95.14 | 91.09 |
Table 7: Performance comparison on MMLU tasks across different target LLMs (accuracy %). All Helix agents were implemented using GPT-4o. Results are averaged across C.B., E.E., and M.T. tasks.
| Input Task Type: Formal Fallacies Task Description: Evaluate arguments to determine if they are deductively valid or invalid. Problems present arguments with multiple premises and a conclusion, requiring extraction of logical components, tracking of negations and logical operators, evaluation of whether the conclusion necessarily follows from the premises, and binary classification (valid/invalid). Expected Output Format: A letter answer in parentheses (e.g., ”(A)” for valid or ”(B)” for invalid) I want to optimize both the question presentation and the solving prompt to achieve the highest correctness rate on this type of problem. |
| Planner Split the task into detailed helix objectives for co-evolution of question presentation and prompt instructions. For example, for the Disambiguation QA task, the optimization is planned as follows: Total helices: 4. helix 1: Pronoun Highlighting Pronoun Identification, where the question goal is to visually highlight the pronoun to identify, the prompt goal is to direct immediate pronoun identification without search, and the connection is that highlighted pronouns enable prompts to skip search steps. helix 2: Antecedent Clarity Antecedent Filtering, where the question goal is to present potential antecedents clearly in context, the prompt goal is to guide systematic filtering based on grammatical agreement, and the connection is that clear noun visibility supports efficient antecedent filtering… |
| Prompt-Architect Prompt-Design: You are a prompt designer in the Helix framework. Your task is to design an improved prompt that achieves the prompt goal and works with the question strategy… Question-Critique: You are a question strategy critic in the Helix framework. Your task is to evaluate proposed question strategy to ensure it supports effective prompting… |
| Question-Architect Question-Design: You are a question designer in the Helix framework. Your task is to design an improved question strategy that achieves the question goal and works with the prompt… Prompt-Critique: You are a prompt critic in the Helix framework. Your task is to evaluate proposed prompt to ensure it works effectively with the question strategy… |
| Question-Generator You are a question modifier in the Helix framework. Your task is to apply a presentation strategy to an original question, creating a modified version that is clearer while preserving all original content… |
| Question-Judge You are a question quality judge in the Helix framework. Your task is to verify that a modified question follows the question strategy and preserves the original meaning… |
| Mediator You are the mediator in the Helix framework. Your task is to judge if the optimized prompt satisfies the prompt goal, if their connection works synergistically as planned… |
Table 8: Summary of agent prompts in the Helix framework. The examples are from the Formal Fallacies task of the BBH dataset.
Figure 7: Training process of Helix (Part 1/2): Planner-guided decomposition and dual-helix co-evolution Round 1. (Continued in Figure 8 )
Figure 8: Training process of Helix (Part 2/2): Dual-helix co-evolution Round 2.
Figure 9: Inference process of Helix (Part 1/2): Strategy-driven question generation Round 1. (Continued in Figure 10 )
Figure 10: Inference process of Helix (Part 2/2): Strategy-driven question generation Round 2.
Table 9: Optimized prompt for the Disambiguation QA task generated by Helix through dual-helix co-evolution.
Table 10: Optimized question strategy for the Disambiguation QA task generated by Helix through dual-helix co-evolution.
Table 11: Original question from the Disambiguation QA task before optimization.
Table 12: Optimized question for the Disambiguation QA task with the strategy applied.
Table 13: Optimized prompt for the Geometric Shapes task generated by Helix through dual-helix co-evolution.
Table 14: Optimized question strategy for the Geometric Shapes task generated by Helix through dual-helix co-evolution.
Table 15: Original question from the Geometric Shapes task before optimization.
Table 16: Optimized question for the Geometric Shapes task with the strategy applied.
Table 17: Optimized prompt for the Formal Fallacies task generated by Helix through dual-helix co-evolution.
Table 18: Optimized question strategy for the Formal Fallacies task generated by Helix through dual-helix co-evolution.
Table 19: Original question from the Formal Fallacies task before optimization.
Table 20: Optimized question for the Formal Fallacies task with the strategy applied.
Table 21: Optimized prompt for the Ruin Names task generated by Helix through dual-helix co-evolution.
Table 22: Optimized question strategy for the Ruin Names task generated by Helix through dual-helix co-evolution.
Table 23: Original question from the Ruin Names task before optimization.
Table 24: Optimized question for the Ruin Names task with the strategy applied.
Table 25: Optimized prompt for the Sports Understanding task generated by Helix through dual-helix co-evolution.
Table 26: Optimized question strategy for the Sports Understanding task generated by Helix through dual-helix co-evolution.
Table 27: Original question from the Sports Understanding task before optimization.
Table 28: Optimized question for the Sports Understanding task with the strategy applied.
Table 29: Optimized prompt for the LSAT-AR task generated by Helix through dual-helix co-evolution.
Table 30: Optimized question strategy for the LSAT-AR task generated by Helix through dual-helix co-evolution.
Table 31: Original question from the LSAT-AR task before optimization.
Table 32: Optimized question for the LSAT-AR task with the strategy applied.
Table 33: Optimized prompt for the College Biology task generated by Helix through dual-helix co-evolution.
Table 34: Optimized question strategy for the College Biology task generated by Helix through dual-helix co-evolution.
Table 35: Original question from the College Biology task before optimization.
Table 36: Optimized question for the College Biology task with the strategy applied.
Table 37: Optimized prompt for the Electrical Engineering task generated by Helix through dual-helix co-evolution.
Table 38: Optimized question strategy for the Electrical Engineering task generated by Helix through dual-helix co-evolution.
Table 39: Original question from the Electrical Engineering task before optimization.
Table 40: Optimized question for the Electrical Engineering task with the strategy applied.
Table 41: Optimized prompt for the Marketing task generated by Helix through dual-helix co-evolution.
Table 42: Optimized question strategy for the Marketing task generated by Helix through dual-helix co-evolution.
Table 43: Original question from the Marketing task before optimization.
Table 44: Optimized question for the Marketing task with the strategy applied.
Table 45: Optimized prompt for the History task generated by Helix through dual-helix co-evolution.
Table 46: Optimized question strategy for the History task generated by Helix through dual-helix co-evolution.
Table 47: Original question from the History task before optimization.
Table 48: Optimized question for the History task with the strategy applied.
Table 49: Optimized prompt for the Philosophy task generated by Helix through dual-helix co-evolution.
Table 50: Optimized question strategy for the Philosophy task generated by Helix through dual-helix co-evolution.
Table 51: Original question from the Philosophy task before optimization.
Table 52: Optimized question for the Philosophy task with the strategy applied.
Table 53: Optimized prompt for the AQuA-RAT task generated by Helix through dual-helix co-evolution.
Table 54: Optimized question strategy for the AQuA-RAT task generated by Helix through dual-helix co-evolution.
Table 55: Original question from the AQuA-RAT task before optimization.
Table 56: Optimized question for the AQuA-RAT task with the strategy applied.
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