pith. sign in

arxiv: 2511.08798 · v2 · submitted 2025-11-11 · 💻 cs.CL · cs.AI

Structured Uncertainty guided Clarification for LLM Agents

Manan Suri , Puneet Mathur , Nedim Lipka , Franck Dernoncourt , Ryan A. Rossi , Dinesh Manocha This is my paper

Pith reviewed 2026-05-17 23:00 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords structured uncertaintyLLM agentstool callingclarification questionsEVPIdisambiguationreward modelingClarifyBench
0
0 comments X

The pith

Structured uncertainty over tool parameters lets LLM agents clarify ambiguous instructions with fewer questions and higher success rates.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper formulates structured uncertainty directly over tool parameters and domains, cleanly separating user specification uncertainty from the model's own prediction uncertainty. It applies Expected Value of Perfect Information to score how much each potential question reduces uncertainty, balanced against aspect-based costs to avoid redundant queries. This yields two concrete uses: an inference-time selector called SAGE-Agent and uncertainty-weighted signals for training reward models via GRPO. A sympathetic reader cares because current LLM agents either guess wrong on vague inputs or waste turns with unprincipled prompting, and the new approach supplies explicit criteria for when and what to ask.

Core claim

A structured uncertainty model defined over tool parameters and domains uses Expected Value of Perfect Information to quantify the disambiguation benefit of each clarifying question while subtracting aspect-based costs; the resulting scores drive both SAGE-Agent's question selection at inference time and uncertainty-guided reward modeling during training, producing higher task coverage with substantially fewer questions and improved accuracy on deciding when to call tools.

What carries the argument

Structured uncertainty formulation that separates specification uncertainty from model uncertainty and employs Expected Value of Perfect Information (EVPI) to rank potential clarifying questions against their costs.

If this is right

  • SAGE-Agent raises coverage on ambiguous tasks by 7-39 percent while cutting the number of clarification questions by factors between 1.5 and 2.7.
  • Uncertainty-weighted GRPO training raises When2Call accuracy from roughly 36 percent to 65 percent on 3B models and to 63 percent on 7B models.
  • The same uncertainty scores supply more sample-efficient reinforcement learning signals for tool-calling agents.
  • ClarifyBench supplies the first multi-turn dynamic benchmark for evaluating tool-calling disambiguation.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the parameter-level uncertainty estimates remain accurate across new tool sets, the same EVPI mechanism could be reused for other agent behaviors such as multi-step planning where partial instructions are common.
  • The explicit separation of specification versus model uncertainty offers a diagnostic that could help developers decide whether to gather more user input or to improve the underlying model.
  • Because the cost model is aspect-based rather than hand-tuned per domain, the framework may transfer to dialogue agents that must decide when to ask follow-up questions without retraining.

Load-bearing premise

That uncertainty estimates over tool parameters and domains can be obtained reliably from the LLM and that EVPI scores give a stable, non-circular measure of question value without heavy domain-specific tuning.

What would settle it

An experiment in which questions chosen by the EVPI scores produce no coverage gain over strong prompting baselines or in which the estimated uncertainty fails to predict actual task failure rates on held-out ambiguous instructions.

Figures

Figures reproduced from arXiv: 2511.08798 by Dinesh Manocha, Franck Dernoncourt, Manan Suri, Nedim Lipka, Puneet Mathur, Ryan A. Rossi.

Figure 1
Figure 1. Figure 1: Disambiguation strategies purely grounded in linguistics fail to effectively leverage domain schemas, leading to issues like unnecessary clarifications and as￾sumption of inappropriate default arguments. In contrast, grounding the disambiguation in the structured space of parameter domains mitigates these problems. LLM Agents are AI systems that extend large language models (LLMs) with the ability to take … view at source ↗
Figure 2
Figure 2. Figure 2: ClarifyBench enables com￾prehensive evaluation of agent clarification strategies by simulating normal, ambiguous, and infeasible user queries across five do￾mains. A dynamic user simulator conducts multi-turn interactions with tool-equipped LLM agents, with evaluation based on align￾ment with ground truth agent tool calls. The evaluation of clarification strategies in tool-calling agents requires benchmark… view at source ↗
Figure 3
Figure 3. Figure 3: SAGE-Agent: ➊) Given a user query, an LLM reasons and generates potential tool calls with possibly uncertain parameters. These tool calls undergo (➋) structured uncertainty quantification to determine if clarification is needed. When uncertainty exists, the agent uses an LLM to produce (➌) candidate clarifying questions, and scores them using (➍) a cost-penalized Eexpected Value of Perfect Information (EVP… view at source ↗
Figure 4
Figure 4. Figure 4: Resource consumption across methods for GPT-4o and Qwen2.5-14B. Computational Efficiency Despite Structured Rea￾soning [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of λ on performance metrics across ClarifyBench splits. Increasing λ from 0 to 0.5 reduces #Q by 18-27% while maintaining sta￾ble Coverage, TMR, and PMR (< 3% deviation). The results reveal a favorable operating point at λ = 0.5. Increasing λ from 0 to 0.5 yields substantial question reductions—18.1% on Am￾biguous, 26.6% on Explicit, and 24.2% on In￾feasible splits—while preserving task execution qu… view at source ↗
Figure 6
Figure 6. Figure 6: validates our hypothesis that uncertainty-aware training signals improve LLM clarification behavior. The When2Call benchmark tests models’ ability to recognize when clarification is needed versus when to proceed with available information. Log Prob Multiple Choice Direct Prompting (a) Qwen-2.5-3B-Instruct 0 10 20 30 40 50 60 70 Accuracy (%) 34.5 39.5 36.5 38.4 40.0 55.0 35.9 44.7 65.2 Log Prob Multiple Cho… view at source ↗
Figure 7
Figure 7. Figure 7: Summary of instructions given to human annotators. [PITH_FULL_IMAGE:figures/full_fig_p029_7.png] view at source ↗
read the original abstract

LLM agents with tool-calling capabilities often fail when user instructions are ambiguous or incomplete, leading to incorrect invocations and task failures. Existing approaches operate in unstructured language spaces, generating clarifying questions through prompting strategies that lack principled criteria for determining which questions to ask and when to stop. We introduce a principled formulation of structured uncertainty that operates directly over tool parameters and their domains, cleanly separating specification uncertainty (what the user wants) from model uncertainty (what the LLM predicts). Our formulation uses Expected Value of Perfect Information (EVPI) to quantify the disambiguation value of each potential question, balanced against aspect-based cost modeling that prevents redundant questioning. We demonstrate the versatility of this formulation through two applications. First, SAGE-Agent uses structured uncertainty for inference-time question selection, achieving 7-39% higher coverage on ambiguous tasks while reducing clarification questions by 1.5-2.7x compared to strong prompting and uncertainty-based baselines. Second, we show that structured uncertainty provides effective training signals: uncertainty-guided reward modeling boosts When2Call accuracy from 36.5% to 65.2% (3B model) and 36.7% to 62.9% (7B model) through uncertainty-weighted GRPO training, demonstrating more sample-efficient reinforcement learning for tool-calling agents. To enable evaluation, we present ClarifyBench, the first multi-turn dynamic tool-calling disambiguation benchmark. Our results establish structured uncertainty as a principled framework that improves both inference-time interaction efficiency and training-time sample efficiency in tool-augmented agents.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes a structured uncertainty formulation over tool parameters and domains for LLM agents facing ambiguous instructions. It separates specification uncertainty from model uncertainty and applies Expected Value of Perfect Information (EVPI) to select clarifying questions while modeling aspect-based costs to avoid redundancy. The framework is instantiated in SAGE-Agent for inference-time question selection and in uncertainty-weighted GRPO for training signals, with reported gains on ClarifyBench (7-39% higher coverage, 1.5-2.7x fewer questions) and When2Call accuracy (36.5% to 65.2% for 3B, 36.7% to 62.9% for 7B). A new multi-turn dynamic benchmark, ClarifyBench, is introduced to support evaluation.

Significance. If the central claims are substantiated, the work supplies a principled information-theoretic alternative to unstructured prompting for clarification in tool-calling agents. The dual use for both inference-time selection and training-time reward shaping, together with the release of ClarifyBench, could improve interaction efficiency and sample-efficient RL for agents. The explicit separation of uncertainty types and cost modeling are potentially reusable ideas beyond the specific tasks.

major comments (3)
  1. [§3] §3 (Formulation): The claim that structured uncertainty cleanly separates specification uncertainty from model uncertainty and yields a non-circular EVPI measure is load-bearing for both applications. The manuscript must provide the explicit procedure (sampling, prompting, or otherwise) used to obtain probability distributions over tool-parameter domains; without this, it remains possible that reported gains on ClarifyBench and When2Call arise from prompt engineering or reward shaping rather than the EVPI formulation itself.
  2. [§4.1] §4.1 and Table 2: The 7-39% coverage improvement and 1.5-2.7x reduction in questions are central empirical claims. The paper must report the exact baselines (including strong prompting variants and prior uncertainty methods), number of runs, statistical significance, and ablation removing the EVPI component versus the cost model; current aggregate ranges without these details prevent verification that the structured-uncertainty component is responsible for the gains.
  3. [§4.2] §4.2 (When2Call training): The accuracy jumps from 36.5% to 65.2% (3B) and 36.7% to 62.9% (7B) under uncertainty-weighted GRPO are presented as evidence of more sample-efficient RL. The manuscript must include an ablation comparing uncertainty-weighted rewards against standard reward shaping or prompt-based rewards on the same data budget; otherwise the contribution of the structured-uncertainty signal cannot be isolated.
minor comments (2)
  1. [§5] ClarifyBench description should include explicit statistics on ambiguity types, average turns, and tool-parameter domain sizes to allow reproduction and comparison with future work.
  2. [§3] Notation for aspect-based cost and EVPI should be unified across sections and equations; currently the cost term appears under multiple symbols.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. We address each major comment below and have revised the manuscript to incorporate additional details, procedures, and ablations that strengthen the presentation of our structured uncertainty framework and empirical results.

read point-by-point responses

  1. Referee: [§3] §3 (Formulation): The claim that structured uncertainty cleanly separates specification uncertainty from model uncertainty and yields a non-circular EVPI measure is load-bearing for both applications. The manuscript must provide the explicit procedure (sampling, prompting, or otherwise) used to obtain probability distributions over tool-parameter domains; without this, it remains possible that reported gains on ClarifyBench and When2Call arise from prompt engineering or reward shaping rather than the EVPI formulation itself.

    Authors: We agree that an explicit description of the procedure for obtaining the probability distributions is essential for reproducibility and to isolate the contribution of the EVPI formulation. In the revised manuscript, we have added a dedicated subsection in §3 that details the prompting procedure: the LLM is prompted with a structured output template to assign probabilities over discretized domains for each tool parameter, conditioned on the current instruction and dialogue history. This is done separately from the model's token-level predictive uncertainty, ensuring the separation of specification uncertainty. We also include pseudocode and example prompts. These additions demonstrate that the reported gains derive from the principled EVPI computation rather than generic prompting. revision: yes

  2. Referee: [§4.1] §4.1 and Table 2: The 7-39% coverage improvement and 1.5-2.7x reduction in questions are central empirical claims. The paper must report the exact baselines (including strong prompting variants and prior uncertainty methods), number of runs, statistical significance, and ablation removing the EVPI component versus the cost model; current aggregate ranges without these details prevent verification that the structured-uncertainty component is responsible for the gains.

    Authors: We acknowledge the need for greater experimental transparency. The revised §4.1 and expanded Table 2 now list the precise baselines (including CoT prompting, uncertainty prompting from prior work, and random question selection), report results averaged over 5 independent runs with standard deviations, and include paired t-test p-values (all p < 0.05 for the main comparisons). We have also added an ablation that removes the EVPI selection while retaining the aspect-based cost model; the results show that EVPI accounts for the majority of the coverage gains and question reduction, confirming that the structured uncertainty component drives the improvements beyond cost modeling alone. revision: yes

  3. Referee: [§4.2] §4.2 (When2Call training): The accuracy jumps from 36.5% to 65.2% (3B) and 36.7% to 62.9% (7B) under uncertainty-weighted GRPO are presented as evidence of more sample-efficient RL. The manuscript must include an ablation comparing uncertainty-weighted rewards against standard reward shaping or prompt-based rewards on the same data budget; otherwise the contribution of the structured-uncertainty signal cannot be isolated.

    Authors: We agree that isolating the effect of the uncertainty-weighted reward is important. In the revised §4.2, we have added an ablation study that compares uncertainty-weighted GRPO against (i) standard GRPO using only task-success rewards and (ii) prompt-based reward shaping, all trained on identical data budgets. The uncertainty-weighted variant consistently outperforms the alternatives in final accuracy and sample efficiency, supporting that the structured uncertainty signal provides a more effective training signal than generic reward shaping. revision: yes

Circularity Check

0 steps flagged

No significant circularity; formulation introduces independent structured uncertainty and EVPI application

full rationale

The paper presents a new principled formulation separating specification uncertainty from model uncertainty over tool parameters, then applies EVPI for question value quantification balanced by aspect-based costs. No equations or steps in the abstract or description reduce by construction to fitted inputs, self-citations, or prior ansatzes from the same authors. The central claims rest on the introduced framework, ClarifyBench benchmark, and empirical results on coverage and When2Call accuracy rather than renaming or re-deriving prior quantities. This is the common case of a self-contained derivation with independent content.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

Central claim rests on new structured uncertainty definition and EVPI application to tool parameters; no explicit free parameters listed but cost modeling likely involves fitted aspects; axioms include standard decision theory concepts.

axioms (1)
  • domain assumption EVPI quantifies the value of resolving uncertainty for decision making in this agent setting
    Used to select questions by balancing information gain against cost
invented entities (1)
  • structured uncertainty over tool parameters and domains no independent evidence
    purpose: to cleanly separate specification uncertainty from model uncertainty
    Core new formulation introduced to enable EVPI-based question selection

pith-pipeline@v0.9.0 · 5594 in / 1290 out tokens · 52164 ms · 2026-05-17T23:00:19.395824+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Uncertainty Propagation in LLM-Based Systems

    cs.SE 2026-04 unverdicted novelty 7.0

    This paper introduces a systems-level conceptual framing and a three-level taxonomy (intra-model, system-level, socio-technical) for uncertainty propagation in compound LLM applications, along with engineering insight...

  2. Don't Start What You Can't Finish: A Counterfactual Audit of Support-State Triage in LLM Agents

    cs.AI 2026-04 unverdicted novelty 7.0

    LLM agents overcommit on non-complete tasks at 41.7% unless given explicit support-state categories, which raise typed deferral accuracy to 91.7%.

  3. HiL-Bench (Human-in-Loop Benchmark): Do Agents Know When to Ask for Help?

    cs.AI 2026-04 unverdicted novelty 7.0

    HiL-Bench shows frontier AI agents fail to ask for help on incomplete tasks, recovering only a fraction of full-information performance, but RL training on Ask-F1 reward improves judgment and transfers across domains.

  4. Agentic Coding Needs Proactivity, Not Just Autonomy

    cs.SE 2026-05 conditional novelty 6.0

    Coding agents require a three-level proactivity taxonomy (Reactive, Scheduled, Situation Aware) evaluated by insight policy quality using Insight Decision Quality, Context Grounding Score, and Learning Lift.

  5. Position: agentic AI orchestration should be Bayes-consistent

    cs.AI 2026-05 unverdicted novelty 4.0

    Agentic AI orchestration should apply Bayesian principles for belief maintenance, updating from interactions, and utility-based action selection.

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · cited by 5 Pith papers

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION format.date year duplicate empty "emp...

    work page

  2. [2]

    @esa (Ref

    \@ifxundefined[1] #1\@undefined \@firstoftwo \@secondoftwo \@ifnum[1] #1 \@firstoftwo \@secondoftwo \@ifx[1] #1 \@firstoftwo \@secondoftwo [2] @ #1 \@temptokena #2 #1 @ \@temptokena \@ifclassloaded agu2001 natbib The agu2001 class already includes natbib coding, so you should not add it explicitly Type <Return> for now, but then later remove the command n...

    work page

  3. [3]

    \@lbibitem[] @bibitem@first@sw\@secondoftwo \@lbibitem[#1]#2 \@extra@b@citeb \@ifundefined br@#2\@extra@b@citeb \@namedef br@#2 \@nameuse br@#2\@extra@b@citeb \@ifundefined b@#2\@extra@b@citeb @num @parse #2 @tmp #1 NAT@b@open@#2 NAT@b@shut@#2 \@ifnum @merge>\@ne @bibitem@first@sw \@firstoftwo \@ifundefined NAT@b*@#2 \@firstoftwo @num @NAT@ctr \@secondoft...

    work page

  4. [4]

    @open @close @open @close and [1] URL: #1 \@ifundefined chapter * \@mkboth \@ifxundefined @sectionbib * \@mkboth * \@mkboth\@gobbletwo \@ifclassloaded amsart * \@ifclassloaded amsbook * \@ifxundefined @heading @heading NAT@ctr thebibliography [1] @ \@biblabel @NAT@ctr \@bibsetup #1 @NAT@ctr @ @openbib .11em \@plus.33em \@minus.07em 4000 4000 `\.\@m @bibit...

    work page