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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →A planning agent should not treat picking from a list as proof that it understands what a user wants. A useful question surfaces information that could change the plan; a good decision then uses that answer, relevant facts, and the user’s constraints to choose among viable options. Multiple-choice prompts can make preferences easier to express, but their value depends on whether the choices capture the uncertainty that matters.
Why choosing an option is not the same as making a decision
A choice records a response to the options presented. A decision connects the user’s goals and constraints to an outcome. If an agent asks whether a traveler prefers a quiet, central, or low-cost hotel, the answer can help shape an itinerary—but only if those choices capture what matters to that traveler.
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Consider an illustrative case: someone selects “lowest cost,” but the options do not ask whether they can travel before dawn or accept a long transfer. The selection is clear; the plan may still be wrong. A planning assistant must establish which information can affect the outcome, not merely collect answers.
That information is distributed between user and agent. The agent may know a city’s layout or opening hours, while the user knows their budget, priorities, and limitations. Lin and colleagues frame this as decision-oriented collaboration: the assistant should determine what its partner already knows and seek decision-relevant information, with the quality of the final decision as an outcome. Their study’s human-human reference dialogues averaged 13 messages over 8 minutes; that is a finding about those dialogues, not a recommended length for every planning exchange. Read the study on decision-oriented dialogue.
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When should a planning agent ask a follow-up question?
Ask when an unresolved uncertainty could materially change the user’s plan, and the user is the right source for resolving it. If the missing information is an external fact—such as whether a venue is open—the assistant may need to check a reliable tool or source instead of asking the user to guess.
There is no universal ask-versus-act threshold established by the cited work. Questions can interrupt or burden a user, while proceeding on a consequential assumption can produce a poor plan. The design problem is to weigh the likely effect of an answer against the cost of asking in that situation.
What makes a clarification question useful?
It targets consequential uncertainty
A question is useful when different answers could lead to meaningfully different plans. “Do you prefer quiet, central, or lowest cost?” can work if accommodation priority is genuinely unsettled and those options map to real tradeoffs. It is less useful if the answer cannot affect the available choices.
Its options represent the real decision
Multiple choice can reduce effort when preferences are bounded and the options are understandable. But a fixed list can hide a constraint, leave out a preference, or falsely imply that the user must choose one of the offered answers. A sound prompt allows an “other” or free-text response when the list may not cover the user’s situation.
The answer is used, not just collected
The agent should incorporate the reply into its plan and, where appropriate, verify relevant external facts. A clarification exchange that does not change the agent’s understanding or action is not evidence of better planning.
Deng and colleagues’ 2026 ICML paper proposes evaluating question value through information gained about the user’s intended goal. Their Information Gain Reward measures belief updates toward a ground-truth goal, with evaluation in a clarification-enhanced tau-Bench environment across five heterogeneous backbones. This offers a benchmark-specific way to think about question usefulness; it does not set a universal threshold or guarantee better real-world decisions. Read the ICML 2026 paper on information-gain clarification.
A practical clarification-to-planning loop
Ask-before-Plan describes proactive planning as predicting what needs clarification, gathering information through tools, and then generating a plan. Its authors propose a Clarification-Execution-Planning framework and evaluate it on a benchmark; it is a research proposal, not a universally established production architecture. The sequence nevertheless gives a useful way to structure an agent’s work:
- Identify what is unresolved. Separate what the user has stated from assumptions and unknown external facts.
- Check whether the uncertainty matters. Ask whether different answers would change the plan, its feasibility, or its tradeoffs.
- Ask the user or use a tool. Ask a targeted question when the missing fact concerns the user’s goals or constraints. Use suitable tools when the gap concerns external information.
- Update the plan from the evidence. Incorporate the user’s answer and any verified facts rather than treating the exchange as a standalone quiz.
- Compare viable alternatives. Explain which plan best fits the stated preferences and constraints, and what tradeoffs remain.
Read Ask-before-Plan, by Xuan Zhang and colleagues, published in Findings of EMNLP 2024.
How should agents be evaluated beyond multiple-choice accuracy?
A single answer score cannot show whether an agent noticed that it needed clarification, asked a relevant question, used the response correctly, or produced a better plan. Evaluation should distinguish those steps and connect them to the decision outcome.
- Need detection: Did the agent recognize that an important goal, constraint, or fact was missing?
- Question value: Did the question resolve consequential uncertainty rather than request irrelevant detail?
- Answer integration: Did the agent update its understanding and plan based on the reply?
- Factual support: Did it use tools appropriately for missing external information?
- Decision quality: Does the final plan fit the user’s preferences and constraints, and are its remaining uncertainties clear?
Different benchmarks test different pieces of this problem. Zhang, Lu, and Jaitly use a 20 Questions-style entity-deduction game as a probe of multi-turn reasoning and planning. It can test question sequences and how answers are used, but the game is a surrogate—not a complete representation of real-world planning. Read the ACL 2024 paper on multi-turn planning probes.
ACPBench defines seven reasoning tasks across 13 formal planning domains. In the models and tests reported in its 2025 evaluation, the authors found a capability gap: OpenAI o1 improved on multiple-choice questions but showed no notable progress on boolean questions. That result is specific to the benchmark and evaluated models; it is not a current ranking of all systems or proof that multiple-choice reasoning is useless. It illustrates why success on one question format cannot stand in for broad planning competence. Read the ACPBench paper.
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When several plans are viable, the agent should compare them against the user’s stated priorities and constraints, the consequences of unresolved uncertainty, and the factual support for each option. A recommendation is more useful when it explains why it fits better than the alternatives, not just which option it selected.
Krarup and colleagues describe explainable planning as iterative exploration of possible plans and report that users’ questions about plans are often contrastive—asking why one plan rather than another. Their finding supports explaining relevant differences between alternatives; it does not establish that every user wants a contrastive explanation in every domain. Read the study on contrastive explanations of plans.
For example, a travel assistant might explain that one itinerary costs less but requires an early departure, while another better matches a request to avoid rushed mornings. The comparison should make the consequences visible and identify any important fact that remains uncertain.
What is established—and what remains open
Research supports treating clarification as part of collaborative, multi-turn planning: agents may need to infer what is missing, ask targeted questions, gather external facts, and evaluate the resulting decision. It does not establish a validated multiple-choice curriculum for teaching agents these skills, a universally best prompt format, or a single rule for when every agent should ask.
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Testing a particular teaching method would require comparing question formats and evaluating both whether questions resolve useful uncertainty and whether the resulting plans improve. Until that evidence exists, multiple choice is best treated as one possible interface for eliciting a decision-relevant answer—not as the decision itself.
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