arxiv: 2605.06223 · v2 · submitted 2026-05-07 · 💻 cs.AI · cs.RO

Recognition: no theorem link

Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries

Hyejin Park, Jungseul Ok, Junhyuk Kwon, Kyle Min, Seungjoon Lee

Pith reviewed 2026-05-11 02:17 UTC · model grok-4.3

classification 💻 cs.AI cs.RO

keywords ambiguous queriesinstance navigationcomparative judgmentcandidate pooldialogue agentssuccess rateresponse length

0 comments

The pith

ProCompNav resolves ambiguous instance navigation by asking yes/no questions that split a candidate pool and prune mismatches at once.

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

Natural-language requests for a specific object often leave many similar distractors possible, so agents must decide how to gather the missing distinctions without burdening the user. ProCompNav first assembles a pool of candidates from the short initial query, then repeatedly extracts an attribute-value pair that divides the pool, poses the corresponding binary question, and removes every candidate inconsistent with the answer. The result is that each user reply eliminates many wrong options simultaneously instead of describing the target in detail. A sympathetic reader would care because this turns a vague request into a short sequence of simple distinctions while raising the chance of correctly identifying the intended instance.

Core claim

ProCompNav is a two-stage framework that first constructs a candidate pool from an ambiguous query and then identifies the target through comparative judgment: at each round it selects an attribute-value pair that splits the current pool, asks the corresponding yes/no question, and prunes all inconsistent candidates. This reframes disambiguation as pool-level discriminative questioning rather than open-ended target description or premature selection of a single plausible item.

What carries the argument

Comparative judgment, the mechanism that extracts an attribute-value pair to split the current candidate pool, poses the binary question, and removes every mismatched candidate after the answer.

If this is right

On CoIN-Bench the method raises success rate above both interactive baselines that receive only minimal input and non-interactive baselines that receive detailed descriptions.
User response length drops substantially on the same benchmark.
State-of-the-art success rate is reached on TextNav.
Each question narrows the candidate set by eliminating multiple distractors simultaneously rather than one at a time.

Where Pith is reading between the lines

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

The pool-splitting strategy could be tested in recommendation or search settings where users start with vague preferences among similar items.
If the initial pool construction step is made robust, the same comparative-question loop may reduce user effort in voice-based or multi-modal navigation tasks.
Performance gains would be expected to shrink on domains where reliable attribute extraction from short queries is difficult.

Load-bearing premise

A reliable candidate pool can be built from the initial ambiguous query and attribute-value pairs can be extracted that produce questions capable of splitting the pool effectively.

What would settle it

An experiment on a new benchmark in which the candidate pools constructed from short queries frequently exclude the true target or contain too many near-identical distractors, causing success rate and response-length gains to disappear.

Figures

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

**Figure 1.** Figure 1: Three strategies for instance navigation under an ambiguous user query. (a) view at source ↗

**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: Termination-step analysis of AIUTA and ProCompNav. The x-axis shows termination steps in 100-step bins, except the max exploration 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-thencompare strategy, we compare the episode termination steps and success rates of AIUTA and Pro… view at source ↗

**Figure 4.** Figure 4: Qualitative comparison of Independent Matching and Comparative Judgment under a view at source ↗

**Figure 5.** Figure 5: TextNav adaptation of the Recursive Comparison Stage. In TextNav, ProCompNav pre view at source ↗

**Figure 6.** Figure 6: Examples of multi-view candidates produced by the Pool Construction Stage. For each view at source ↗

**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.

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.

Desk Editor's Note private letter to a colleague

ProCompNav's pool-then-compare approach is a sensible way to cut down on vague-query failures in navigation, but the gains hinge on unexamined retrieval quality.

read the letter

The core move here is to split disambiguation into two stages: first grab a pool of candidates from the ambiguous query, then pick binary questions that actually split that pool by extracting attribute-value pairs. That differs from earlier interactive baselines that either stop too soon or ask questions that don't prune effectively, and it shows up in the reported shorter response lengths plus higher success rates on CoIN-Bench and TextNav. The idea is practical for real robotics or search settings where users hate giving long descriptions upfront. Credit to the authors for framing the problem around pool-level discrimination rather than per-candidate attributes. The numbers they cite look like a step up from the baselines they compare against. The soft spot is exactly the one the stress-test flags. Pool construction gets almost no scrutiny in the abstract, and if the initial retrieval step misses the target on any non-trivial fraction of cases, the later pruning steps cannot save it. No recall figures, no failure-case breakdown, and no description of the embedding or LLM retrieval method appear in what is visible. That makes it hard to know whether the benchmark wins come from the comparative judgment or from a retrieval component that happens to work on these test sets. The experimental section also seems light on statistical tests and error analysis, which leaves the robustness claim thin. This is the kind of paper that would interest people building language-guided agents who already have a retrieval pipeline and want a better questioning strategy on top. It is worth a serious referee because the problem is real, the reframing is clean, and the claimed improvements are concrete even if the retrieval link needs more evidence. I would send it out for review with a request for pool-recall metrics and ablation on the first stage.

Referee Report

2 major / 2 minor

Summary. The paper proposes ProCompNav, a two-stage framework for proactive instance navigation under ambiguous natural-language queries. Stage 1 constructs a candidate pool from the initial query; Stage 2 extracts attribute-value pairs to generate binary yes/no questions that split the current pool, pruning all inconsistent candidates in each round. This reframes disambiguation as pool-level discriminative questioning rather than open-ended target description. On CoIN-Bench the method reports higher Success Rate than both minimal-input interactive baselines and detailed-description non-interactive baselines while reducing Response Length; it also claims state-of-the-art Success Rate on TextNav.

Significance. If the reported gains prove robust, the comparative-judgment reframing could meaningfully reduce user burden in dialogue-based navigation systems. The approach is conceptually clean and directly targets the failure modes (premature commitment, non-discriminative questions) identified in prior work.

major comments (2)

[Method (candidate-pool construction stage)] The central claim that gains are attributable to comparative judgment presupposes that the candidate pool constructed in Stage 1 reliably contains the target. No retrieval algorithm, recall metric, or failure-case analysis (queries where the target is absent from the pool) is supplied; if pool construction misses the target on a non-negligible fraction of CoIN-Bench or TextNav instances, all subsequent pruning steps become irrelevant.
[Experiments] The abstract asserts benchmark improvements, yet the experimental section supplies neither baseline implementation details, statistical significance tests, nor error analysis stratified by query ambiguity or pool size. Without these, it is impossible to determine whether the reported Success-Rate gains are robust or artifacts of the test distribution.

minor comments (2)

[Method] Clarify the precise procedure for extracting attribute-value pairs and the criterion used to select the splitting pair at each round.
[Evaluation metrics] Define Response Length consistently across interactive and non-interactive baselines so that length reductions can be compared directly.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed comments. We address each major comment below and indicate the revisions we will make to strengthen the manuscript.

read point-by-point responses

Referee: [Method (candidate-pool construction stage)] The central claim that gains are attributable to comparative judgment presupposes that the candidate pool constructed in Stage 1 reliably contains the target. No retrieval algorithm, recall metric, or failure-case analysis (queries where the target is absent from the pool) is supplied; if pool construction misses the target on a non-negligible fraction of CoIN-Bench or TextNav instances, all subsequent pruning steps become irrelevant.

Authors: We agree that the reliability of the candidate pool is a prerequisite for the effectiveness of the comparative judgment stage. The manuscript describes pool construction via semantic similarity retrieval but does not provide the specific retrieval algorithm, recall metrics, or failure-case analysis. We will revise the method section to fully specify the retrieval procedure, report recall@K results on both CoIN-Bench and TextNav, and add a dedicated analysis of queries where the target is absent from the pool. These additions will allow readers to evaluate the contribution of Stage 1 independently. revision: yes
Referee: [Experiments] The abstract asserts benchmark improvements, yet the experimental section supplies neither baseline implementation details, statistical significance tests, nor error analysis stratified by query ambiguity or pool size. Without these, it is impossible to determine whether the reported Success-Rate gains are robust or artifacts of the test distribution.

Authors: We acknowledge that the current experimental section lacks the requested details. We will expand it to include full baseline implementation specifications (hyperparameters, model versions, and any code availability), report statistical significance tests (e.g., paired t-tests) on the success-rate differences, and add error analysis stratified by query ambiguity levels and candidate pool sizes. These changes will provide stronger evidence for the robustness of the reported gains. revision: yes

Circularity Check

0 steps flagged

No circularity: method is a self-contained algorithmic procedure evaluated on external benchmarks

full rationale

The paper describes ProCompNav as a two-stage framework that first builds a candidate pool from an ambiguous query and then performs pool-splitting via attribute-value extraction and binary comparative questions. No equations, fitted parameters, or derivations are present that would reduce the success-rate claims to the inputs by construction. The central procedure is presented as a novel questioning strategy whose performance is measured against independent benchmarks (CoIN-Bench and TextNav), with no self-citation load-bearing on uniqueness theorems, no renaming of known results, and no fitted-input-called-prediction pattern. The derivation chain therefore remains independent of its own outputs.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on the unstated ability to build an accurate candidate pool and to generate reliable attribute-value questions from it; these are domain assumptions rather than derived results.

axioms (2)

domain assumption An effective candidate pool can be constructed from the initial ambiguous natural-language query
This is the prerequisite for the first stage of the framework described in the abstract.
domain assumption Attribute-value pairs can be extracted and used to generate questions that split the current candidate pool
This underpins the second-stage comparative judgment mechanism.

pith-pipeline@v0.9.0 · 5570 in / 1236 out tokens · 34760 ms · 2026-05-11T02:17:45.883139+00:00 · methodology

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Reference graph

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