Modular Search Agents Overview

• Modular search agents are autonomous systems that decompose the search process into distinct, interoperable modules for planning, query transformation, retrieval, reflection, and answer generation.
• They leverage reinforcement learning to optimize sequential decision-making, dynamically refining query processing and evidence retrieval to improve metrics like Exact Match and F1.
• Plug-and-play modularity enables independent module upgrades and seamless integration into diverse systems, making them robust for real-world, knowledge-intensive applications.

A modular search agent is a search-capable autonomous system composed of clearly delineated, interoperable components—each implementing a specialized function such as planning, query understanding, retrieval, reasoning, or answer generation. Modular design enables plug-and-play deployment, independent optimization, and scalable integration into complex, knowledge-intensive environments. The recent QAgent framework exemplifies a modular search agent for interactive query understanding, integrating LLMs, reinforcement learning–driven multi-step reasoning, and retrieval-augmented generation (RAG) to maximize information acquisition and answer quality in real-world settings (Jiang et al., 9 Oct 2025).

1. Architectural Principles of Modular Search Agents

Modular search agents, as instantiated by QAgent, decompose the retrieval-augmented generation pipeline into interoperable modules. The agentic workflow is divided into:

• Planning Module: Generates pre-retrieval plans or instructions for decomposing the query.
• Query Transformation Module: Reformulates or decomposes the original user query into a set of atomic or context-optimized queries.
• Retrieval Module: Interfaces with an external retrieval engine (e.g., BM25, E5) to fetch supporting evidence or knowledge passages.
• Reflection Module: Performs pre-retrieval and post-retrieval reasoning, enabling dynamic adaptation of the retrieval strategy based on intermediate outcomes.
• Generator Module: Composes final answers by aggregating and synthesizing retrieved evidence.

Each module exposes a well-defined input-output interface, supporting independent modification and system-level extensibility. In deployment, these modules can be integrated as standalone middleware (i.e., “plug-and-play” in heterogeneous software infrastructures) or as submodules in larger LLM-based systems.

2. Interactive Query Understanding and Adaptive Reasoning

Unlike traditional RAG, which typically employs a static “retrieve-then-generate” paradigm, modular search agents like QAgent recast query understanding as a multi-step, interactive stochastic decision process. Given an initial user question $q$, the agent proceeds through iterative rounds, generating plans $(I^{pre}_t)$, reformulated search queries $S_t$, aggregating retrieved contexts $C_t$, and again reflecting $(I^{post}_t)$ on the sufficiency of collected information. The overall trajectory is:

$$\tau = (q, I^{pre}_1, S_1, C_1, I^{post}_1, \ldots, I^{post}_T, \hat{A})$$

where $T$ is the number of rounds and $\hat{A}$ the final answer. Each round’s actions—planning, retrieval, and reasoning—feed forward into subsequent decisions, enabling the agent to dynamically adapt its retrieval strategy. This allows decomposition of complex queries (e.g., multi-hop QA) and incremental refinements based on historical evidence.

3. Reinforcement Learning for Sequential Policy Optimization

The QAgent framework employs reinforcement learning (RL), casting the search process as a sequential decision-making task. A stochastic policy $\pi_\theta(a_t \mid s_{<t})$ determines whether the agent should further reason or issue a new retrieval command at each time step. The RL objective integrates both solution accuracy and answer format adherence:

• Stage 1 (End-to-End RL): The policy is optimized for the final answer’s strict Exact Match (EM) with ground truth and correct answer formatting:

$$R(\tau) = \mathbb{I}\{ r_{fmt}(\tau) = 1 \} \cdot EM_s(A^*, \hat{A})$$

• Stage 2 (Generalized RL): When deployed as a retrieval-optimized submodule (with a “frozen” downstream generator), the reward is based on the generator’s performance given the retrieved documents, focusing policy optimization on effective retrieval rather than overfitting to specific downstream logic.

Group Relative Policy Optimization (GRPO) is used, with reward normalization and a clipped importance ratio to ensure stable policy learning while containing policy drift via KL regularization.

4. Modularity and Plug-and-Play Deployment

QAgent’s modularity confers precise functional isolation of planning, search, reflection, and generation. This design permits:

• Independent Replacement and Upgrades: Modules can be swapped or upgraded independently (e.g., exchanging retrievers or LLM generators) without refactoring the entire pipeline.
• Pipeline Integration: The agent can be deployed as a drop-in middleware for information-intensive applications, enabling system designers to “plug in” QAgent into larger QA or conversational AI systems.
• Interoperability: The component interfaces ensure compatibility with various dense or sparse retrievers and generative LLMs.

This modular architecture supports both flexibility (rapid adaptation to evolving user tasks) and system robustness.

5. Empirical Results and Real-World Application

Extensive experiments on benchmark datasets (HotpotQA, 2WikiMHQ, MuSiQue, NaturalQA, and WebQuestions) demonstrate QAgent’s empirical gains:

• On open-domain and multi-hop QA tasks, QAgent consistently improves both Exact Match (EM) and F1 metrics over vanilla RAG and prior agentic search baselines.
• As a submodule (with a more powerful generator frozen), QAgent exhibits superior generalization in retrieval compared to direct-search or end-to-end approaches.
• Ablation studies confirm that each modular stage and the staged RL training contribute incrementally to overall system performance.
• Case studies show advanced behaviors such as human-like iterative query refinement and retrieval, with improved performance on challenging, compositional tasks.

QAgent’s plug-and-play modularity enables its deployment in diverse real-world scenarios, including open-domain QA systems, conversational agents, and systems requiring up-to-date retrieval from dynamic sources.

6. Limitations and Future Considerations

The paper identifies that while end-to-end RL fosters autonomous multi-round reasoning and retrieval, it may lead to over-optimization toward information utilization, potentially degrading retrieval quality when the agent is used as a dedicated retrieval submodule. The two-stage training strategy, which freezes the generator during the second optimization phase, is proposed as a mitigation to enhance generalization and retrieval quality.

A plausible implication is that future modular search agents may benefit from further disentangling retrieval and generation roles, and from more sophisticated inter-module coordination policies, particularly in complex or adversarial information environments.

---

QAgent formalizes the modular search agent paradigm for interactive query understanding, grounded in reinforcement learning and RAG, and substantiated by empirical improvements and real-world system compatibility. This modular agent embraces decomposability, adaptive inference, and systematic policy optimization to advance AI-assisted information retrieval and reasoning in complex deployment scenarios (Jiang et al., 9 Oct 2025).