Clarifying Agent in Dialogue Systems

• Clarifying agent is an autonomous system using large language models to detect and resolve ambiguity in multi-turn dialogues.
• It employs modular architectures, reinforcement learning, and rule-driven methods to ask targeted clarification questions for improved accuracy.
• Applications span conversational assistants, code generation, tool-based agents, and multimodal systems enhancing enterprise AI solutions.

A clarifying agent is an autonomous module or system, typically realized by a LLM or a combination of LLMs and domain detectors, whose primary function is to detect ambiguity, underspecification, or implicit information gaps in user inputs and proactively resolve such uncertainty by asking targeted follow-up questions. This interaction extends a single-turn user prompt into a multi-turn dialogue, allowing the agent to elicit missing preferences, parameters, or constraints essential for safe, accurate, and contextually appropriate task completion (Andukuri et al., 2024). Clarifying agents are now central to dialogue systems, QA, intent understanding, code generation, tool-based agents, multimodal assistants, and enterprise virtual assistants.

1. Formal Definition and Motivations

A clarifying agent, within the framework of LLM-based systems, is defined as a model or module that, instead of issuing an immediate response to a potentially ambiguous or incomplete user prompt, engages in active information elicitation by generating clarification questions. The agent continues this process until it has gathered sufficient information or has determined that further questions would not yield additional utility, at which point it produces a final answer or executes an action (Andukuri et al., 2024, Acikgoz et al., 15 Dec 2025, Tsvilodub et al., 2 Feb 2026).

The rationale for clarifying agents arises from the ubiquity of latent ambiguity in real-world human queries: unexpressed goals, omitted parameters, and context-dependent tasks (e.g., recipe constraints, personalized recommendations, programming requirements, domain-specific document edits) may all leave critical information unsaid. Premature or uninformed responses can lead to suboptimal, incorrect, or harmful outcomes. Clarifying agents systematically reduce such ambiguity, directly improving the utility, safety, and personalization of downstream outputs (Andukuri et al., 2024, Acikgoz et al., 15 Dec 2025, Yuan et al., 3 Feb 2026).

2. Architectures and Paradigms

Clarifying agents are implemented in a wide spectrum of architectures:

• Monolithic LLMs with prompt engineering: Single-turn or multi-turn LLMs are conditioned with task- or ambiguity-aware prompts that induce the model to ask clarifying questions if uncertainty is detected (Siro et al., 2024, Murzaku et al., 19 Mar 2025).
• Modular and Multi-Agent Systems: Assign roles for ambiguity detection, question generation, and feedback integration to distinct modules or sub-agents. Architectures such as MAC employ a hierarchical Supervisor/Expert split, where supervisor agents target domain-agnostic ambiguities and expert agents address domain-specific gaps (Acikgoz et al., 15 Dec 2025).
• Hybrid Rule-Driven and Learnable Detectors: Integration of explicit ambiguity detectors (e.g., intent disambiguation, entity linking, product classification), which provide signals that are aggregated and modulate LLM-driven clarification (Murzaku et al., 19 Mar 2025, Murzaku et al., 19 Mar 2025).
• Reinforcement-Learning and Preference-Optimization Agents: RL-based agents optimize over explicit reward signals for question-asking behavior (covering informativeness, resolution, interaction cost), typically formulated either as policy-gradient updates or as reward-weighted supervised fine-tuning (Andukuri et al., 2024, Mukherjee et al., 8 Jun 2025, Chen et al., 2024, Suri et al., 11 Nov 2025).

The dialog policy may be realized through:

• Discrete action selection (e.g., binary "Clarify" vs. "Answer" controllers) (Cao et al., 23 Jan 2026, Luo et al., 24 Dec 2025).
• POMDP-driven or uncertainty-aware policies (e.g., SAGE-Agent maximizing EVPI) (Suri et al., 11 Nov 2025, Tsvilodub et al., 2 Feb 2026).
• Quasi-online preference optimization (e.g., ACT, DPO) propagating feedback from trajectory-level contrast pairs (Chen et al., 2024).

In multimodal and egocentric systems, clarifying agents extend beyond language processing, incorporating modules for visual feedback, gesture interpretation, and cross-modal coreference to resolve deictic ambiguity (Yang et al., 12 Nov 2025).

3. Clarification Decision Algorithms and Theoretical Models

Clarifying agents formalize the ask-or-answer tradeoff as a decision problem under epistemic uncertainty and action cost. Central theoretical frameworks include:

• Expected Regret Decision Rule (Tsvilodub et al., 2 Feb 2026): Compute the expected regret of the best immediate action relative to the counterfactual with full information. Ask a clarification question exactly when expected regret exceeds a cost threshold $c$:

$$\mathrm{ExpRegret}(r^*) = \sum_{g\in G}P(g)[\max_{r'}U(g,r')-U(g,r^*)]$$

Issue a question when $\mathrm{ExpRegret}(r^*) > c$.

• POMDP/EVPI Criteria (Suri et al., 11 Nov 2025): Model tool-use or task-decision as a Partially Observable MDP over belief $b$; questions are chosen to maximize EVPI:

$$EVPI(q, b) = \mathbb{E}_{r\sim P(r|q,b)}\!\Big[\max_{c\in \mathcal{C}}\pi(c|q,r)\Big] - \max_{c\in \mathcal{C}}\pi(c)$$

Incorporate aspect-based cost to penalize redundancy and stop clarifying when marginal EVPI does not justify added cost.

• Slot-State and FSM-Based Tracking (Luo et al., 24 Dec 2025, Gan et al., 2024): Percept modules extract slot-filling states (unfilled, filled, conflict) per turn, and a finite-state machine tracks uncertainty progression. The planner applies a simple stopping rule based on the completeness and consistency of slot states.
• Uncertainty-Weighted RL: Certainty over action candidates modulates the reinforcement learning reward; asking is rewarded only when belief uncertainty is high, and executing is rewarded only when sufficiently certain (Suri et al., 11 Nov 2025).
• Ablation-confirmed Modular Necessity: Empirical ablations demonstrate that explicit ambiguity-tracking, planning, and user-behavior forecasting modules are all required for robust clarification performance, especially with non-cooperative or noisy users (Luo et al., 24 Dec 2025).

4. Training, Optimization, and Benchmarking

Clarifying agents are developed using a suite of synthetic and real-world ambiguous datasets, modular scoring, and advanced optimization methods:

• Synthetic Data Generation: For code, dialog, and QA tasks, ambiguity is artificially injected (removing constraints, introducing conflicts) and paired with gold clarifications, yielding large, labeled datasets for training and controlled evaluation (Andukuri et al., 2024, Wu et al., 23 Apr 2025, Chen et al., 2024).
• Iterative Self-Improvement: Agents are fine-tuned with expert trajectories (highest-probability rollouts under a base answer model), self-improving their question-asking policy via reward signals tied to downstream answer utility (Andukuri et al., 2024).
• Offline RL and Reward-Weighted SFT: Dialogue quality is estimated via LLM critics and used as sample weights for supervised cross-entropy losses (Mukherjee et al., 8 Jun 2025).
• Contrastive Preference Optimization: Action-based contrast pairs (CLARIFY vs ANSWER) collected from on-policy and off-policy traces, updated quasi-online, with DPO-style losses enabling sample-efficient learning even in scarce-data regimes (Chen et al., 2024).
• Evaluation Benchmarks: Task-specific suites, e.g., ClarifyMT-Bench (multi-turn ambiguity taxonomy, noisy personas) (Luo et al., 24 Dec 2025), HumanEvalComm (code requirement ambiguity) (Wu et al., 2024), ClarQ-LLM (task completion under multilingual functional uncertainties) (Gan et al., 2024).
• Metrics: Include clarification rate, good-question rate, downstream accuracy, preference win-rate, information recovery, over-/under-questioning, and dialogue efficiency (turn count, query discrepancy).

Empirical results consistently show significant performance gains for clarifying agents over strong prompting and supervised learning baselines—e.g., up to +83% in VQA accuracy with CoA (Cao et al., 23 Jan 2026), 72% dialog preference win-rate for STaR-GATE (Andukuri et al., 2024), and 7–39% coverage improvement with SAGE-Agent in tool-augmented tasks (Suri et al., 11 Nov 2025).

5. Application Domains and Case Studies

Clarifying agents have been instantiated for:

• Conversational Assistants: Multi-turn dialog agents for open-domain and task-oriented interactions, systematically resolving ambiguous user needs through persona-aware elicitation and structured slot tracking (Luo et al., 24 Dec 2025, Acikgoz et al., 15 Dec 2025, Murzaku et al., 19 Mar 2025).
• QA and VQA Systems: Visual and textual QA agents that interleave question generation and clarification with answering, explicitly modeling context under-specification (Cao et al., 23 Jan 2026).
• Program Synthesis and Code Generation: Code LLMs (ClarifyCoder) trained to detect incomplete programming specifications and prompt for missing details, with notable improvement in communication and correctness on ambiguous HumanEvalComm tasks (Wu et al., 23 Apr 2025, Wu et al., 2024).
• Tool-Calling and API-Oriented Agents: LLM agents with tool APIs modeled as POMDPs over parameter spaces, using clarification to maximally reduce uncertainties prior to tool execution (Suri et al., 11 Nov 2025).
• Multimodal/Egocentric Agents: Wearable/AR agents resolving user intent ambiguity through a modular pipeline of language, vision, and gesture clarifiers, achieving >30% accuracy gains even in resource-poor LLMs (Yang et al., 12 Nov 2025).
• Enterprise AI Support: Modular clarify agents (ECLAIR) aggregate specialized ambiguity detectors and domain-grounded modules for customer-facing applications, achieving higher F₁ and better clarification question precision than few-shot LLM baselines (Murzaku et al., 19 Mar 2025, Murzaku et al., 19 Mar 2025).
• Conversational Search and Retrieval: LLM frameworks generating and vetting clarifying questions in retrieval pipelines (AGENT-CQ), yielding superior retrieval precision compared to human or template-driven questions (Siro et al., 2024).

6. Practical Considerations, Limitations, and Future Directions

While clarifying agents demonstrate strong empirical benefits, several design and deployment considerations remain:

• Interaction Overhead: Multi-turn clarification improves accuracy but may introduce user friction or inefficiency; systems employ budgeted turns, cost-sensitive stopping, or single-turn clarification as trade-offs (Cao et al., 23 Jan 2026, Yuan et al., 3 Feb 2026).
• Reliance on Synthetic and Simulated Data: Many current approaches rely on LLM-simulated users, scripted ambiguities, or gold-oracle answers, which may diverge from real-world dialog dynamics or error modes (Andukuri et al., 2024, Wu et al., 23 Apr 2025, Chen et al., 2024).
• Ambiguity Detection Robustness: Challenges remain in accurately detecting latent or indirect ambiguities, especially with diverse or adversarial user behaviors (Luo et al., 24 Dec 2025, Murzaku et al., 19 Mar 2025).
• Redundancy and Over-Clarification: Structured cost models (e.g., aspect-based penalization, EVPI termination) are critical in preventing repetitive or unnecessary questioning (Suri et al., 11 Nov 2025).
• Generalization Across Agents and Domains: Performance for single-roleplayer-trained agents drops under cross-agent or cross-domain evaluation; robust training across a diversity of ambiguity types and user simulators is an open direction (Andukuri et al., 2024, Acikgoz et al., 15 Dec 2025).
• Integration with Retrieval, Tools, and External Knowledge: Future clarifying agents are expected to integrate retrieval augmentation, multimodal signals, and tool interactions within unified reasoning loops (Yang et al., 12 Nov 2025, Suri et al., 11 Nov 2025).

Potential avenues for future research include: reinforcement learning variants that optimize open-ended clarification strategies, human-in-the-loop clarification quality evaluation, adaptation to continuous/discovery uncertainty spaces, and large-scale deployment studies in live user settings (Andukuri et al., 2024, Suri et al., 11 Nov 2025, Tsvilodub et al., 2 Feb 2026).

7. Representative Systems and Results

The following table highlights several prototypical clarifying agents, their domains, approaches, and principal gains:

System	Domain	Approach	Key Gains	Reference
STaR-GATE	Open-domain dialog	Iterative SFT/self-improvement	72% human preference	(Andukuri et al., 2024)
SAGE-Agent	Tool-calling, APIs	Uncertainty/EVPI POMDP	+7–39% coverage, 1.5–2.7x fewer questions	(Suri et al., 11 Nov 2025)
ClarifyCoder	Program synthesis	Clarification-aware SFT	Comm. rate ↑24.1→63.6%	(Wu et al., 23 Apr 2025)
CoA	Visual QA	Ask-or-answer RL, GRPO	+15.3 points accuracy	(Cao et al., 23 Jan 2026)
AGENT-CQ	Conversational search	LLM prompt/gen+CrowdLLM eval	Outperforms human Q/A	(Siro et al., 2024)
MAC	Task-oriented dialog	Multi-agent, Taxonomy-based	Success 54.5→62.3%	(Acikgoz et al., 15 Dec 2025)
ClarifyAgent	Multi-turn dialog	Perceiver-Tracker-Forecaster	Accuracy ↑~20 points	(Luo et al., 24 Dec 2025)

These systems collectively demonstrate that principled uncertainty modeling, explicit ambiguity tracking, modular architectures, and targeted data protocols are essential to effective clarification. Carefully designed clarifying agents yield substantial improvements in both user-facing quality and operational robustness across the full range of language-driven artificial intelligence systems.