Interactive Prompting & Modular Reasoning

Updated 24 October 2025

Interactive prompting and modular reasoning are strategies that decompose complex tasks into simpler modules to enhance LLM reasoning and performance.
These methods utilize iterative feedback and modular pipelines, such as Progressive-Hint Prompting and Test-time Prompt Intervention, to improve accuracy and control error propagation.
Integrating neural and symbolic components within these architectures enables transparent, scalable AI systems applicable in areas like code generation, mathematical reasoning, and multi-modal tasks.

Interactive prompting and modular reasoning are methodological paradigms and architectural strategies for eliciting, structuring, and improving the reasoning abilities of LLMs. These approaches address core limitations of monolithic few-shot or chain-of-thought (CoT) prompting by decomposing complex tasks into simpler, independently optimizable modules and facilitating stepwise, interpretable, and interactive execution. Contemporary developments in this domain span fields from mathematical reasoning and code generation to multi-modal and multi-agent systems, resulting in improved generalization, error isolation, and system transparency.

1. Principles and Definitions

Interactive prompting refers to the iterative engagement of a LLM (or a set of models) with tasks or subtasks, where outputs at each step condition or refine subsequent prompts. This may involve user feedback, intermediate hint injection, rationale verification, or programmatic orchestration of sub-task execution. Modular reasoning denotes the decomposition of a reasoning pipeline into discrete, functionally or semantically distinct modules—each addressing a subproblem with independent prompts, handlers, or even symbolic APIs. The overarching goal is to transform monolithic, entangled reasoning into modular, reconfigurable workflows that can incorporate neural and symbolic components, intermediate verifications, and targeted interventions.

2. Decomposed Prompting Architectures

The decomposed prompting paradigm (Khot et al., 2022) establishes a canonical modular framework where a central "decomposer" LLM parses an input query $Q$ into a sequence of sub-tasks, represented by a structured program:

$P = \left( (f_1, Q_1, A_1), (f_2, Q_2, A_2), \dots, (f_k, Q_k, A_k) \right),$

where each $f_i$ is a function drawn from a library $\mathcal{F}$ (e.g., split, retrieve, merge, symbolic function), $Q_i$ is a sub-query, and $A_i$ is the corresponding answer. Each sub-task can be handled by a distinct prompt, a specialized LLM, or an external (symbolic) handler, and sub-tasks can themselves be recursively decomposed. Variants of this approach support recursive and hierarchical task decomposition (e.g., divide-and-conquer list reversal), iterative "foreach" operators, and plug-in of symbolic modules. This modularity affords flexibility, better generalization, error isolation, and the ability to integrate information retrieval, arithmetic, or external APIs directly into the reasoning workflow.

3. Interactive Prompting Mechanisms

Recent frameworks have enriched the interactivity of prompting in several ways:

Progressive-Hint Prompting (PHP) (Zheng et al., 2023): Iteratively re-prompts the LLM with previously generated answers as hints, enabling the model to refine and "double-check" its answers until convergence, with significant increases in accuracy (e.g., 4.2% improvement on GSM8K over Complex CoT).
R³ Prompting (Tian et al., 2023): Structures reasoning into Review (key sentence extraction), Rephrase (variable declaration), and Resolve (final calculation) stages, each providing intermediate cues for the next. This staged, multi-round interaction is particularly robust to noisy input and outperforms single-turn prompting by up to 3.7% in challenging benchmarks.
Self-Polish (SP) (Xi et al., 2023): Focuses interactivity on the problem statement itself; the model iteratively refines the question for clarity and relevance, prior to reasoning, and halts when answer convergence is achieved. SP is notably orthogonal to answer-side prompting and improves both performance and robustness, especially on adversarial benchmarks (e.g., GSM-IC).
Test-time Prompt Intervention (PI) (Yang et al., 4 Aug 2025): Provides an explicit intervention interface at inference, separating the intervention process into When (trigger by token entropy), How (type of action: progression, summarization, verification), and Which (selection via scoring function mixing perplexity and reasoning depth) modules. This controls chain-of-thought redundancy, reduces hallucination, and enables expert correction during generation.

4. Modular Pipelines and Neuro-Symbolic Integration

Architectures such as MURMUR (Saha et al., 2022), MoReVQA (Min et al., 9 Apr 2024), and reasoning frameworks for NLI (Li et al., 10 May 2025) evidence the scope of modular reasoning beyond single-domain LLMs:

MURMUR composes neural (e.g., linguistic realization, text fusion) and symbolic (e.g., max, filter, aggregation) modules into reasoning paths, validated at each step by value functions that combine LLM scoring and entailment-based semantic consistency:

$S(G_r, y_r) = \alpha S_f(y_r) + (1-\alpha) S_\mathrm{sc}(G_r, y_r)$

This alignment of modular neural and symbolic capabilities notably increases logical consistency (26% more on LogicNLG).

MoReVQA (Min et al., 9 Apr 2024) decomposes videoQA into staged event parsing, visual grounding (using VLMs like CLIP), and multi-hop reasoning, all mediated by a shared external memory. Each module is training-free and driven by stage-specific prompts, with interpretable intermediate outputs and superior performance on videoQA benchmarks.
LPML (Yamauchi et al., 2023): Integrates chain-of-thought reasoning in an XML-like markup, where <THINK> and <PYTHON> tags are distinguished, and Python REPL output is used to correct the LLM’s logical chain, ensuring modular correction of reasoning and computation.
Neuro-symbolic automata (RetoMaton) (Mamidala et al., 22 Aug 2025): LLMs are augmented with a Weighted Finite Automaton (WFA) overlay generated from task corpora, enabling transparent, modular retrieval and verifiable trace alignment between neural states and symbolic transitions.

5. Advancements in Dynamic, Human-in-the-Loop, and Multi-Agent Modularity

Vis-CoT (Pather et al., 1 Sep 2025): Parses LLM’s linear chain-of-thought into a reasoning graph (DAG), enabling users to view, prune, and graft individual reasoning steps. Modular interventions can correct flawed nodes without requiring full regeneration, improving both transparency and final accuracy (e.g., up to 24 percentage point gains).
Adaptive Prompting (R, 10 Oct 2024): Prompt templates are dynamically adjusted in real time, guided by iterative validation and error correction. Each reasoning step is modularized, allowing for efficient self-correction in smaller models, which can approach or match the reasoning accuracy of models several times larger.
Multi-Agent Prompt Orchestration (Dhrif, 30 Sep 2025): Formulates agent states as tuples of prompt template, reasoning context, and capability matrix, with distributed consensus mechanisms to guarantee logical consistency and convergence in multi-agent reasoning. The system achieves reduced latency, improved context preservation, and high success rates in collaborative multi-agent workflows, though resource limits emerge at extreme scales.

6. Application Domains, Limitations, and Extensions

Interactive prompting and modular reasoning, as surveyed, have achieved state-of-the-art results across:

Symbolic manipulation, mathematical and logical reasoning, code generation (MoT (Pan et al., 16 Mar 2025)), video and multimodal QA, data-to-text generation from graphs/tables, tutoring, and consensus-based multi-agent systems.
Each approach highlights trade-offs: increased modularity can incur higher inference latency (more LLM or API calls), and the need for strict output formatting between modules can introduce new points of failure. As identified in (Khot et al., 2022), integrating diverse modules (neural and symbolic) requires careful interface design to prevent error cascades.
There is growing emphasis on externalized, interpretable, and human-in-the-loop components—separating boundary (role/tone) prompts from adaptive control schemas (Figueiredo, 8 Aug 2025), and introducing symbolic overlays for stable, verifiable reasoning (Mamidala et al., 22 Aug 2025).

Framework/Method	Modularity Mechanism	Notable Result/Claim
Decomposed Prompting	Recursive program decomposition	+14–17pt over CoT on multi-step tasks (Khot et al., 2022)
MURMUR	Neural+symbolic modules, grammar	+26% logical consistency on LogicNLG (Saha et al., 2022)
Vis-CoT	Graph structuring, user pruning	+24pts accuracy, ↑ usability and trust (Pather et al., 1 Sep 2025)
MoT (code gen)	Hierarchical MLR graph	Up to 95.1% Pass@1, beats CoT/SCoT (Pan et al., 16 Mar 2025)
Progressive-Hint	Iterative hints/refinement	+4.2% GSM8K, –46% sample paths (Zheng et al., 2023)
PI (Prompt Intervention)	Dynamic When/How/Which modules	~50% shorter CoTs, ↓ hallucination (Yang et al., 4 Aug 2025)

7. Outlook and Research Directions

The contemporary trajectory for interactive prompting and modular reasoning points toward systems capable of:

Plug-and-play module integration, allowing symbolic, neural, retrieval, or user modules to be inserted or swapped without retraining.
Human–AI collaboration frameworks that support targeted interventions, traceable reasoning graphs, and explanatory feedback loops.
Dynamically adaptive workflows, where prompt orchestration and modular handoffs enable fault tolerance and scalable distributed reasoning (Dhrif, 30 Sep 2025).
Incorporation of cognitive scaffolding, fuzzy logic, and advanced process reward paradigms for learning under uncertainty, with safe and interpretable control schemas (Figueiredo, 8 Aug 2025).
Extending modular reasoning to multi-modal domains and real-world settings where interpretability, interaction, and robustness are essential.

Despite clear gains in accuracy, interpretability, and robustness, the field recognizes open challenges in interface standardization, error propagation control, inference cost, and module coordination. Nevertheless, modular and interactive prompting constitutes a foundational methodology for building advanced, trustworthy, and scalable AI reasoning systems across knowledge domains.