Task-Agnostic Multi-Prompt Generation

• Task-agnostic multi-prompt generation is an automated framework that constructs diverse, optimized prompts applicable across multiple tasks without manual intervention.
• It leverages techniques such as prompt vector arithmetic and hierarchical multi-agent synthesis, achieving significant performance gains like a 72.4% improvement in dialogue tasks.
• The framework enhances system robustness, transferability, and sample efficiency while mitigating issues like memory bloat and catastrophic forgetting in continual learning.

Task-agnostic multi-prompt generation refers to the automated construction, selection, and optimization of diverse sets of prompts applicable across broad families of tasks, with no presupposition of task labels or manual task-specific intervention. This paradigm underpins robust model evaluation, parameter-efficient continual learning, and general-purpose system reliability, by exploiting the sensitivity of LLMs to prompt formulation, and by moving beyond reliance on a single, static prompt per task or example.

1. Principles and Motivation

Task-agnostic multi-prompt generation arises from two interlocking observations. First, LLMs exhibit substantial variation in output correctness and style in response to semantically equivalent, but syntactically varied, prompts. Single-prompt evaluation protocols are unreliable: minuscule changes in wording, format, or demonstration order can result in swings exceeding 20–30 percentage points in performance, often outweighing improvements from model size or pretraining (Habba et al., 20 Jul 2025). Second, prompt tuning and optimization methods originally developed for single-task or task-aware regimes are brittle when generalized to diverse, heterogeneous, or previously unseen tasks. Task-agnostic mechanisms seek to systematically explore prompt space, either via explicit combinatorial perturbations or by learning transferable representations and policies, enabling robust downstream adaptation and continual extension (Belanec et al., 2024, Tiwari et al., 19 Jul 2025).

The core ambition is to (1) generate and manipulate prompt sets covering key axes of prompt sensitivity, (2) avoid the need for repeated costly retraining for new tasks, and (3) facilitate evaluation, transfer, composition, and continual learning without task-specific supervision.

2. Foundational Algorithms and Formulations

Prompt Vectors and Arithmetic

"Task Prompt Vectors" represent one canonical approach: for each source task $t$, prompt tuning yields a prompt embedding matrix $P_t\in\mathbb{R}^{L\times d}$, whose difference from a random initialization $P_0$ defines a task prompt vector, $\Delta_t = P_t - P_0$ (Belanec et al., 2024). These vectors are modular, initialization-agnostic, and can be linearly combined across tasks:

$$P_{\text{new}} = P_0 + \sum_{i=1}^k \alpha_i \Delta_{t_i},$$

where $\alpha_i$ are combination weights. Cosine similarity analyses show that $\Delta_t$ vectors align along task-consistent directions and support prompt arithmetic superior to mere prompt concatenation or random initialization: they can initialize new tasks in few-shot/zero-shot, accelerate convergence, and enable prompt libraries transferable between models and seeds.

Multi-Agent and Hierarchical Prompt Synthesis

Zero-shot, per-query prompt construction can be framed as a multi-level cooperative workflow. Hierarchical Multi-Agent Workflows (HMAW) structure prompt design into agent layers—CEO, Manager, Worker—where each LLM agent receives both the raw query and context from the previous agent, sequentially refining high-level goals into executable prompt instructions (Liu et al., 2024). The per-query pipeline is formalized as:

• $p^c = f^c(\text{Ctx}_{\text{CEO}}, q)$
• $q^c = M^c(p^c)$
• $p^m = f^m(\text{Ctx}_{\text{Mgr}}, q, q^c)$, and so forth,

with skip connections preserving user intent and preventing drifting. Quantitative results show substantial average preference and accuracy gains (up to 72.4% absolute improvement in FED dialog) over baselines.

Gradient-Driven Continual Pool Compression

Prompt-based continual learning at scale is challenged by memory bloat and catastrophic forgetting. GRID (Tiwari et al., 19 Jul 2025) employs a gradient-norm-based selection mechanism to (i) partition prompt pools into high- and low-importance subsets (based on relevance to new tasks), and (ii) compress redundant prompts via gradient-weighted averaging. Task-agnostic inference is supported by automatic task identification and constrained (vocabulary-masked) decoding, enabling robust retention (up to 54% BWT reduction) and memory efficiency across up to 15 tasks.

3. Modular and Extensible Generation Frameworks

Systems like PromptSuite formalize the decomposition of prompt templates into modular components: instruction, format, demonstration, and instance (Habba et al., 20 Jul 2025). Perturbation functions operate across components—paraphrase, formatting, demonstration shuffling—yielding the prompt set:

$$p = I(t_I, \lambda_I) \Vert F(t_F, \lambda_F) \Vert D(t_D, \lambda_D) \Vert C(x, \lambda_C)$$

where $t$ denotes component templates, $\lambda$ denotes perturbation parameters, and $\Vert$ denotes concatenation. This structure supports automated, scalable multi-prompt generation agnostic to task typology (NLP, QA, code), and is accessible via open-source APIs and web interfaces. Empirical evaluation shows that per-example accuracy variance can exceed 20–30 points across prompt variants, highlighting the necessity of multiprompt evaluation for reliable benchmarking.

4. Optimization, Adaptation, and Stability

Joint Optimization of Prompt Components

Holistic frameworks such as P3 (Zhang et al., 21 Jul 2025) demonstrate that offline, joint optimization of both system and user-complement prompts, followed by online retrieval- or model-driven adaptation, achieves superior performance across general and reasoning benchmarks (e.g., +3.5 pp GSM8K over strongest baselines). The procedure alternates population-based user-complement search and system prompt refinement, leveraging LLM-judge feedback, with query-dependent few-shot adaptation at inference.

Stability-Aware Prompting

Promptor (Chen et al., 19 May 2025) introduces semantic stability $S(p)$—the average pairwise cosine similarity among response embeddings for a prompt—as the core criterion for determining a prompt's reliability in general-purpose, multi-agent systems. A learned LLaMA-based stability evaluator predicts $S(p)$, allowing iterative refinement of unstable prompt components (role, requirements, etc.) via review and diagnosis. Theoretical analysis links prompt-level stability to global system reliability: bounding the deviation between system output and planner intent by the variance (hence stability) of component prompts.

5. Applications: Decoding, Ensembling, and Continual Learning

Multi-Prompt Bayes Risk Decoding

For conditional generation tasks, constructing a diverse prompt bank and sampling candidates across these prompts enables Minimum Bayes Risk (MBR) decoding—selecting the output maximizing expected utility relative to others—yielding higher diversity and oracle coverage, and improving robustness across code, simplification, and translation (Heineman et al., 2024). Heuristic prompt selection (clustering, performance ranking) cannot match the gains from full prompt sampling, further confirming that multi-prompt spaces are necessary for properly estimating LLM capabilities.

Mixtures and Gating

Mixture-of-Prompts (MoP) architectures (Dun et al., 2023) maintain expert prompt embeddings, dynamically weighted by a gating network conditioned on input representations at intermediate model layers. This gating mitigates task and data interference, stabilizes adaptation across heterogeneous scenarios (federated/centralized), and empirically supports perplexity reductions (up to 70%) with minimal architectural assumptions or retraining.

Policy-Based Universal Prompt Generators

Reinforcement learning-based prompt generators learn policies $\pi_\theta(p | s)$ on multi-task settings, where prompt embeddings are optimized for arbitrary, scalar rewards (measured by black-box evaluators) (Su et al., 2022). Joint sampling from multiple tasks prevents forgetting and enables few-shot generalization to unseen control factors, with empirical superiority over independent or hand-tuned prompts.

6. Theoretical and Empirical Results

Empirical studies demonstrate that task-agnostic multi-prompt generation consistently outperforms single-prompt or task-aware protocols in accuracy, robustness, memory, and sample complexity across benchmarks spanning NLU, code generation, dialogue, QA, and multi-agent workflows (Belanec et al., 2024, Habba et al., 20 Jul 2025, Chen et al., 19 May 2025, Liu et al., 2024, Tiwari et al., 19 Jul 2025, Dun et al., 2023). Theoretical analyses supply convergence guarantees (e.g., $O(1/\sqrt{T})$ for multi-agent collaborative optimization (Han et al., 14 Sep 2025)), formal links between prompt stability and system-level error (Chen et al., 19 May 2025), and task-transfer metrics quantifying forward and backward knowledge transfer (Tiwari et al., 19 Jul 2025). All frameworks highlight modularity, extensibility, and independence from manual task annotation as essential advantages.

7. Limitations, Open Challenges, and Future Directions

While task-agnostic multi-prompt frameworks have demonstrated remarkable flexibility and empirical gains, several open challenges remain. Stability evaluation depends on the fidelity of learned evaluators and incurs nontrivial computational overhead. Multi-agent optimization introduces increased latency. Bandit-based and population-based discovery methods require carefully tuned exploration-exploitation parameters. Task transfer and scaling to highly structured tasks (e.g., SQL, schema QA) necessitate new prompt component modules. Real-world deployment further requires formal guarantees of coverage, efficient approximation algorithms for large prompt banks, and automated calibration of diversity and cost. Prospective work targets meta-learning of agent specializations, meta-selectors for prompt banks, and integration with hybrid (continuous-discrete) search for prompt representations (Han et al., 14 Sep 2025, Habba et al., 20 Jul 2025, Chen et al., 19 May 2025).

In summary, task-agnostic multi-prompt generation has established itself as the foundation for robust, transferable, and scalable LLM-based systems, with mature methodologies spanning modular generation, prompt arithmetic, collaborative optimization, stability diagnostics, and continual adaptation across domains and use cases (Belanec et al., 2024, Liu et al., 2024, Habba et al., 20 Jul 2025, Tiwari et al., 19 Jul 2025, Zhang et al., 21 Jul 2025, Han et al., 14 Sep 2025, Chen et al., 19 May 2025, Heineman et al., 2024, Dun et al., 2023, Su et al., 2022).