Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents (May 2026)

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First forced-injection framework measuring how clarification value changes over the execution trajectory across goal/input/constraint/context dimensions; 6,000+ runs, 4 frontier models, 3 benchmarks; finds goal clarifications lose nearly all value after 10% execution, input clarifications retain val

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About Ask Early, Ask Late, Ask Right: When Does Clarification Timing Matter for Long-Horizon Agents (May 2026)

This paper introduces a forced-injection framework to measure how the value of asking for clarification changes over the execution trajectory of long-horizon AI agents. The framework injects ground-truth clarifications at controlled points across four information dimensions (goal, input, constraint, context) and evaluates performance on three benchmarks with four frontier models, totaling over 6,000 runs. Key findings include that goal clarifications lose nearly all value after 10% of execution, input clarifications retain value through roughly 50%, and deferring any clarification past mid-trajectory degrades performance below never asking at all. Cross-model correlations (Kendall tau 0.78–0.87) indicate task-intrinsic timing curves. A complementary study of 300 unscripted sessions reveals that no current frontier model asks within the empirically optimal window, with strategies ranging from over-asking to never asking. The paper provides quantitative design targets for timing-aware clarification policies and will release code and data.

Key Features

Forced-injection framework for controlled clarification timing

Four information dimensions: goal, input, constraint, context

Evaluated on 3 agent benchmarks with 4 frontier models

84 task variants and over 6,000 runs

Cross-model Kendall tau correlations 0.78–0.87

300 unscripted sessions analyzing current model strategies

Code and data will be publicly released

Pros & Cons

Pros

Provides the first quantitative framework for measuring clarification value over time
Reveals that clarification timing curves are largely task-intrinsic and transferable across models
Empirically identifies when each information type retains value (e.g., goal early, input mid)
Includes a complementary unscripted study showing current models fail to ask optimally

Cons

Forced-injection methodology may not fully capture natural agent behavior
Limited to three benchmarks and four models; scalability not fully explored
No current model asks within the optimal window, highlighting a gap but not a solution
Framework does not address how to train agents to ask autonomously at the right time

Best For

Designing timing-aware clarification policies for long-horizon agentsImproving agent performance by determining optimal clarification windowsEvaluating and comparing clarification strategies in AI agentsInforming training objectives for models to ask at empirically optimal times

FAQ

What is the main finding of the paper?

The value of asking for clarification depends on what information is missing: goal clarifications lose nearly all value after 10% of execution, input clarifications retain value through roughly 50%, and deferring any clarification past mid-trajectory degrades performance below never asking.

What models and benchmarks were used?

Four frontier models were tested on three agent benchmarks, with three models per benchmark and one model used on a single benchmark. A total of 84 task variants and over 6,000 runs were conducted.

Will the code and data be released?

Yes, the paper states that code and data will be publicly released.