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arxiv: 2605.06223 · v3 · pith:6J5G7XOFnew · submitted 2026-05-07 · 💻 cs.AI · cs.RO

ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries

Junhyuk Kwon , Seungjoon Lee , Hyejin Park , Kyle Min , Jungseul Ok This is my paper

Pith reviewed 2026-05-19 16:56 UTC · model grok-4.3

classification 💻 cs.AI cs.RO
keywords instance navigationambiguous queriescomparative judgmentcandidate pruningbinary questionsproactive agent
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The pith

ProCompNav resolves ambiguous navigation queries by asking binary questions that split candidate pools.

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

The paper introduces ProCompNav to help navigation agents handle unclear user requests for specific items among similar ones. Instead of asking for detailed descriptions or guessing early, it builds a set of possible candidates and then asks yes-or-no questions about attributes that best separate those candidates. Each answer removes all candidates that do not match, quickly narrowing to the target. This leads to higher success rates on test benchmarks and shorter replies from users compared to prior approaches that either stop too soon or require more input.

Core claim

The core discovery is that reframing disambiguation as pool-level discriminative questioning, where each binary query is chosen to split the current candidate set, allows an agent to identify the target instance more reliably and with less user effort than methods relying on individual candidate attributes or upfront detailed descriptions.

What carries the argument

The two-stage framework consisting of candidate pool construction followed by iterative selection of splitting attribute-value pairs for binary questioning and immediate pruning of inconsistent candidates.

Load-bearing premise

Reliable extraction of attribute-value pairs from candidates and that user binary answers will accurately and fully prune the candidate pool without introducing new ambiguities.

What would settle it

Observing cases where the extracted attributes fail to distinguish key distractors or where pruning leads to incorrect elimination of the true target due to inconsistent answers would falsify the effectiveness of the comparative judgment approach.

Figures

Figures reproduced from arXiv: 2605.06223 by Hyejin Park, Jungseul Ok, Junhyuk Kwon, Kyle Min, Seungjoon Lee.

Figure 1
Figure 1. Figure 1: Three strategies for instance navigation under an ambiguous user query. (a) view at source ↗
Figure 2
Figure 2. Figure 2: Recursive Comparative Judgment. At iteration t, ProCompNav splits the candidate pool Ut into a core set Gc and a remainder set Gr by similarity. It identifies a discriminative attribute a ∗ t , that is common in Gc but not in Gr. Finally, it asks whether the target has a ∗ t , and prunes the pool to obtain the next candidate pool Ut+1 based on the user’s response. Because distractors D and T ∗ share many a… view at source ↗
Figure 3
Figure 3. Figure 3: Termination-step analysis of AIUTA and ProCompNav. The x-axis shows termina￾tion steps in 100-step bins, except the max ex￾ploration step; bars (left y-axis) show number of terminated episodes, and lines (right y-axis) show cumulative number of successful episodes. To demonstrate the advantage of our collect-then￾compare strategy, we compare the episode ter￾mination steps and success rates of AIUTA and Pro… view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of Independent Matching and Comparative Judgment under a view at source ↗
Figure 5
Figure 5. Figure 5: TextNav adaptation of the Recursive Comparison Stage. In TextNav, ProCompNav pre view at source ↗
Figure 6
Figure 6. Figure 6: Examples of multi-view candidates produced by the Pool Construction Stage. For each view at source ↗
Figure 6
Figure 6. Figure 6: Examples of multi-view candidates produced by the Pool Construction Stage. For each [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Effect of the candidate pool size threshold view at source ↗
read the original abstract

Natural-language instance navigation becomes challenging when the initial user request does not uniquely specify the target instance. A practical agent should reduce the user's burden by actively asking only the information needed to distinguish the target from similar distractors, rather than requiring a detailed description upfront. Existing approaches often fall short of this goal: they may stop at the first plausible candidate before sufficiently exploring alternatives, or, even after collecting multiple candidates, ask about the target's attributes derived from individual candidates rather than questions selected to distinguish candidates in the pool. As a result, despite the dialogue, the agent may still fail to distinguish the target from distractors, leading to premature decisions and lengthy user responses. We propose Proactive Instance Navigation with Comparative Judgment (ProCompNav), a two-stage framework that first constructs a candidate pool and then identifies the target through comparative judgment. At each round, ProCompNav extracts an attribute-value pair that splits the current pool, asks a binary yes/no question, and prunes all inconsistent candidates at once. This reframes disambiguation from open-ended target description to pool-level discriminative questioning, where each question is chosen to narrow the candidate set. On CoIN-Bench, ProCompNav improves Success Rate over interactive baselines with the same minimal input and non-interactive baselines with detailed descriptions, while substantially reducing Response Length. ProCompNav also achieves state-of-the-art Success Rate on TextNav, suggesting that comparative judgment is broadly useful for instance-level navigation among similar distractors. Code is available at https://github.com/tree-jhk/procompnav.

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

2 major / 1 minor

Summary. The paper proposes ProCompNav, a two-stage framework for instance navigation under ambiguous queries. It first builds a candidate pool from minimal user input and then iteratively extracts attribute-value pairs from the pool, poses binary yes/no questions, and prunes inconsistent candidates until the target is isolated. The authors claim higher Success Rate than both interactive baselines (same minimal input) and non-interactive baselines (detailed descriptions) on CoIN-Bench, substantially shorter Response Length, and state-of-the-art Success Rate on TextNav.

Significance. If the empirical results are robust, the comparative-judgment loop offers a principled way to reduce user burden in disambiguation tasks by replacing open-ended descriptions with pool-level discriminative questions. The public code release is a clear strength that enables direct reproduction and extension.

major comments (2)
  1. [Experiments] Experiments section: the reported Success Rate gains and Response Length reductions on CoIN-Bench (and SOTA on TextNav) are presented without error bars, dataset statistics, ablation tables, or quantitative measurements of attribute-extraction accuracy; these omissions leave the central performance claims unsupported.
  2. [Method] Method (§3): the framework rests on the assumption that attribute-value extraction is reliable and that each binary answer correctly and exhaustively prunes only inconsistent candidates without false negatives (discarding the target) or false positives (retaining distractors); no error-rate analysis, noisy-answer ablation, or fallback mechanism is described, which directly undermines the claimed advantage over baselines.
minor comments (1)
  1. [Abstract] Abstract: the phrase 'substantially reducing Response Length' is not accompanied by the precise metric or the exact baseline values being compared.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our work. We address each major comment below and have made revisions to the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: [Experiments] Experiments section: the reported Success Rate gains and Response Length reductions on CoIN-Bench (and SOTA on TextNav) are presented without error bars, dataset statistics, ablation tables, or quantitative measurements of attribute-extraction accuracy; these omissions leave the central performance claims unsupported.

    Authors: We agree with the referee that the experimental results would benefit from additional statistical rigor and supporting analyses. In the revised version, we have included error bars for the Success Rate and Response Length metrics on CoIN-Bench, calculated across five independent runs with different random seeds. We have also added a table presenting key dataset statistics for both CoIN-Bench and TextNav. Furthermore, we now include an ablation table that evaluates the contribution of each component, including quantitative measurements of attribute-extraction accuracy using precision and recall on a validation set. These additions provide better support for the central performance claims. revision: yes

  2. Referee: [Method] Method (§3): the framework rests on the assumption that attribute-value extraction is reliable and that each binary answer correctly and exhaustively prunes only inconsistent candidates without false negatives (discarding the target) or false positives (retaining distractors); no error-rate analysis, noisy-answer ablation, or fallback mechanism is described, which directly undermines the claimed advantage over baselines.

    Authors: The referee correctly identifies that our framework relies on the reliability of attribute-value extraction and the correctness of the pruning process. To strengthen the method section, we have expanded §3 with a discussion of these assumptions. We now report the accuracy of the attribute extraction module on a held-out portion of the data. Additionally, we present results from a noisy-answer ablation study, where we introduce simulated errors in user responses at varying rates and measure the impact on success rate. We also describe a fallback strategy: if the candidate pool size does not decrease below a threshold after a fixed number of questions, the system requests a more detailed description from the user. These revisions address the potential issues of false negatives and false positives and better substantiate the advantages over baselines. revision: yes

Circularity Check

0 steps flagged

No circularity: algorithmic procedure evaluated on external benchmarks

full rationale

The paper describes ProCompNav as a two-stage algorithmic procedure (candidate pool construction followed by iterative attribute-value extraction, binary questioning, and pruning) whose performance is measured against external benchmarks (CoIN-Bench, TextNav) and baselines. No equations, fitted parameters, or self-referential quantities appear in the provided text. Claims of improved Success Rate and reduced Response Length rest on empirical results rather than any derivation that reduces to its own inputs by construction. No self-citation load-bearing steps, uniqueness theorems, or ansatzes are invoked in a manner that creates circularity. The framework is self-contained as a method whose validity is externally falsifiable.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The method relies on standard assumptions about attribute extraction accuracy and question-answering reliability in navigation agents; no new free parameters, physical entities, or ad-hoc axioms are introduced in the abstract.

axioms (1)
  • domain assumption Attribute-value pairs can be extracted from scene candidates with sufficient accuracy to enable reliable pool splitting.
    The comparative judgment step depends on this extraction step being effective.

pith-pipeline@v0.9.0 · 6860 in / 1035 out tokens · 47663 ms · 2026-05-19T16:56:40.611075+00:00 · methodology

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