Complex Intent Decomposition Module

• Complex Intent Decomposition Modules (CIDMs) are systems that decompose ambiguous user inputs into discrete, semantically atomic units to ensure complete and granular interpretation.
• They employ advanced methods such as LLM-driven prompts, constraint satisfaction, and hierarchical planning to optimize tasks in search, dialogue, and multi-modal processing.
• Empirical evaluations demonstrate that CIDMs improve retrieval accuracy, dialogue performance, and interpretability by effectively managing complex and under-structured queries.

A Complex Intent Decomposition Module (CIDM) is a dedicated system component that decomposes a raw, possibly ambiguous or multifaceted user input—typically a query, instruction, trajectory, or utterance—into atomic sub-intents or sub-queries suitable for independent semantic processing, downstream modeling, or targeted execution. State-of-the-art CIDMs leverage modern LLMs, reasoning or constraint-based algorithms, and domain-specific ontologies to optimize both coverage and fidelity of user intent, and often expose structure such as hierarchical ordering, logical dependencies, or orthogonal facets. CIDMs are now core to high-accuracy search, dialogue systems, task planning, recommendation, intent mining, and multi-modal understanding.

1. Formalization and Problem Setting

The canonical formalization of complex intent decomposition frames it as mapping a natural-language input $Q$, $x$, or $I$ (depending on context) to a set of atomic sub-queries or semantic units $\{q_1, ..., q_m\}$ or $\{e_1, ..., e_M\}$ that together fully and minimally encapsulate all facets of the original intent (Zhong et al., 8 Sep 2025, Liao et al., 13 Jan 2026).

In LLM-driven frameworks, the decomposition problem is typically modeled as high-probability generation under learned or prompted conditional distributions:

$\{q_i^*\} = \arg\max_{\,\{q_i\}\,} P(\{q_i\}|Q)$

or with step-wise autoregressive decoding:

$$P(\{q_i\}|Q) \approx \prod_{i=1}^m P(q_i\,|\,Q, q_{<i})$$

(Zhong et al., 8 Sep 2025). Alternative approaches model the input as a Constraint Satisfaction Problem and pursue systematic treewidth-based decomposition (Zhou et al., 9 Oct 2025), or instantiate domain-specific element sets $E(z)$ and prerequisite graphs $\mathcal{P}$ for hierarchical/directed breakdown (Liao et al., 13 Jan 2026).

Key desiderata across systems are:

• Completeness: All semantically relevant atomic facets in the input are captured.
• Fidelity: No drift, distortion, or omission of critical intent.
• Granularity: Decomposition units suit downstream models (retrieval, execution, classification).

2. Algorithmic and Model Architectures

The design of CIDMs varies by application domain and modality, but common approaches include:

A. LLM-Based Prompted Decomposition

• LLMs (e.g., Qwen-3-8B, Gemini Pro, text-davinci-003) are prompted with chain-of-thought (CoT) style instructions to identify and articulate atomic information needs, semantic units, or sub-queries. Structured outputs (e.g., numbered lists, JSON) are parsed as the decomposition (Zhong et al., 8 Sep 2025, Lin et al., 17 Aug 2025).

B. Structured Graph/Constraint Approaches

• Task inputs are reduced to structured graphs or CSPs. Decomposition exploits tree decompositions, constraint graph heuristics, or softgoal interdependency graphs to partition the global objective into tractable sub-problems, often guided by measured complexity (e.g., treewidth bounds) (Zhou et al., 9 Oct 2025, Wang et al., 2024).

C. Multi-Perspective and Hierarchical Planning

• Meta-level planners incorporate intent taxonomies and perspectives (e.g., market, contract, time) and solve coverage/overlap optimization problems to select sub-intents, frequently used in agent-based and intent-mining contexts (Mao et al., 19 Nov 2025).

D. Modular and Sequential Decoders

• Sequence generation models (e.g., BART, mT5, mBART) are employed either end-to-end or in staged pipelines (split and rewrite), especially for utterance splitting, multi-intent dialogue, or instruction decomposition tasks (Meng et al., 2022, Jhamtani et al., 2023).

Table: Exemplary decomposition design choices

Approach	Input	Decomposition Mechanism
LLM-CoT	NL query	Prompted step-wise LLM generation
Graph/CSP	Structured Q	Tree decomposition
Taxonomy-aware	Transactions	Multi-perspective optimization
Seq2Seq	Utterance	Split + Rewrite via encoder-decoder

These structures are frequently combined with lightweight parsing, constraint checking, or scoring layers to post-process the LLM or GNN outputs and enforce format or logical consistency.

3. Training Paradigms and Loss Functions

CIDMs are trained in several modes:

• Pseudo-label supervised fine-tuning: Pseudo-labels are generated via teacher LLMs or tools, then student models are fine-tuned to predict decompositions $\{q_i\}$ from $Q$ using cross-entropy or negative log-likelihood objectives (Zhong et al., 8 Sep 2025, Meng et al., 2022).
• Joint fine-tuning: Models emit alternating sequences of sub-queries and interpretations, optimized through joint loss functions such as

$$\mathcal{L}_{\mathrm{joint}} = \frac{1}{2}\mathcal{L}_{\mathrm{decomp}} + \frac{1}{2}\mathcal{L}_{\mathrm{desc}}$$

where $\mathcal{L}_{\mathrm{desc}}$ supervises downstream interpretation tokens (Zhong et al., 8 Sep 2025).

• Contrastive and coding-rate objectives: Disentanglement-based recommendations systems optimize intent-wise contrastive loss and coding-rate reduction to ensure orthogonal and interpretable intent subspaces (Wang et al., 2024).
• Constraint-regularized and reward-driven learning: Systems incorporating formal logic embed loss on coverage, overlap, or logical consistency, or use RL-style reward signals for optimal decomposition chains (Zhou et al., 9 Oct 2025, Khot et al., 2020).

4. Illustrative Applications and Case Studies

CIDMs exhibit a diverse array of applications:

• Retrieval-Oriented Decomposition: In large-scale search, a complex Q is mapped to a set of atomic sub-queries, each passed separately into interpretation and retrieval, then fused for multi-intent coverage. This pipeline has demonstrated superior performance on both sparse and dense retrieval benchmarks (Zhong et al., 8 Sep 2025).
• Multi-Perspective Transaction Mining: In DeFi transaction analysis, a meta-planner leverages a taxonomy of intent, decomposing each transaction into multiple perspective-specific subtasks, maximizing recall and maintaining precision under noisy or multimodal input (Mao et al., 19 Nov 2025).
• Dialogue and Utterance Splitting: CIDMs split composite user utterances in chat, reformulate sub-queries to enforce slot completeness and coreference expansion, and feed each to specialized intent classifiers for robust multi-intent detection (Meng et al., 2022, Gupta et al., 2021).
• Instruction Decomposition for Generation: Text-to-image generation performance is improved by an intermediate semantic decomposition stage that extracts objects, attributes, relationships, and constraints, feeding these as structured units into adaptive prompt integration blocks (Lin et al., 17 Aug 2025).
• Structured Input and Constraint Problems: Under combinatorial constraints (e.g., scheduling, SQL generation), CIDMs decompose the task using formal tree decompositions, yielding subtasks of manageable complexity and extending the frontier of solvable tasks (Zhou et al., 9 Oct 2025).

5. Quantitative Impact and Empirical Evaluation

Rigorous ablation studies and benchmarking confirm the critical contribution of decomposition:

• Retrieval: On BRIGHT and BEIR, decomposition modules consistently outperform SOTA baselines on both sparse and dense retrieval (Zhong et al., 8 Sep 2025).
• Multi-Agent Intent Mining: Ablating the meta-planner in DeFi reduces F1-micro by ≈16 percentage points and recall from 0.78 to 0.62 (Mao et al., 19 Nov 2025).
• Dialogue: CIDMs, especially in two-stage or causal variants, outperform end-to-end models in utterance splitting and rewriting by up to 15 points on sub-query exact match (Meng et al., 2022).
• Semantic Generation: Decomposition-integrated T2I systems achieve higher MLLM-rated image fidelity and coverage of compositional instructions (Lin et al., 17 Aug 2025).
• Constraint Solving and Planning: Complexity-driven decomposition boosts completion rates on challenging SATBench and Spider tasks by +9–30 percentage points over chain-of-thought or baseline splits (Zhou et al., 9 Oct 2025).

6. Integration, Limitations, and Interpretability

Complex intent decomposition is typically modular, with dedicated pipelines for interpretation and downstream execution. Integration involves lightweight parsing of LLM outputs, handling of slot co-reference or ambiguity, and, in visual tasks, mapping prototype activations to model decisions for interpretability (Tang et al., 25 Apr 2025, Wang et al., 2024).

Limitations identified include propagation of upstream errors, prompt drift, sensitivity to hyperparameter settings (e.g., temperature, treewidth), and reliance on proprietary LLMs in some solutions (Zhong et al., 8 Sep 2025, Liao et al., 13 Jan 2026, Lin et al., 17 Aug 2025). Nonetheless, decomposition is a robust driver of both accuracy and interpretability, especially where user inputs are high-dimensional, under-structured, or specification-rich.

7. Outlook and Future Extensions

Ongoing research on CIDMs explores:

• Distilling staged decompositions into end-to-end models for latency reduction (Cohen et al., 15 Sep 2025).
• Extending decomposition logic to reinforcement learning and interaction optimization (Wang et al., 2024).
• Systematic coverage of logical dependencies and requirements graphs in dialogue and planning (Liao et al., 13 Jan 2026).
• Closing the gap between LLM-supervised and open-source/student models via pseudo-labeling and retrieval-augmented generation (Zhong et al., 8 Sep 2025, Cohen et al., 15 Sep 2025).

The modular and adaptable nature of complex intent decomposition is rapidly broadening the class of problems tractable by LLM-based and agentic AI systems, with significant advances anticipated as decomposition strategies are further integrated with reasoning, retrieval, execution, and constraint satisfaction paradigms.