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

arXiv:2605.06223 · cs.AI, cs.RO · Submitted 2026-05-07 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries".

Jane: ProCompNav is a two-stage framework that first constructs a candidate pool and then identifies the target through comparative judgment, replacing independent matching strategy with a two-stage collect-then-compare pipeline:

Tom: First, who's behind it and why it matters.

Title and authors: Jane: When we look at the title "Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries," it really highlights the proactive nature of the system; it doesn't just wait for a perfect description.

Lu: I see that "proactive" part as crucial because existing methods often fail by stopping too early or asking questions about individual candidates instead of comparing them in the pool.

Tom: That’s what they’re saying, right? They point out that previous work sometimes gets stuck on a plausible candidate before exploring other options properly, which leads to premature decisions.

Meng: So, the core idea is to proactively ask only the information needed to distinguish the target from similar distractors instead of needing a detailed description initially.

Lalam: It’s about shifting the focus from describing one thing perfectly to asking questions that separate candidates within a set of possibilities.

Tom: Right, and what's fascinating is how they frame this by suggesting that instead of open-ended descriptions, the agent should be asking questions specifically designed to narrow down the candidate set at each step.

Jane: That makes sense; it turns the disambiguation from an open-ended target description into a process of pool-level discriminative questioning.

Lu: And they show in Table one how this Comparative Judgment strategy significantly outperforms independent matching and pooled independent matching in terms of success rate and response length, which is pretty compelling data.

Meng: The data shows that the comparative judgment approach achieves a success rate of twenty-three point seven compared to just seventeen point five for pooled independent matching on CoIN-Bench, which suggests a tangible improvement in reliability during these navigation tasks.

Lalam: It shows that by using this iterative pruning process, you can keep the response length much shorter while still hitting a higher success rate than other methods.

The paper's summary: Tom: So, summarizing the main idea of "ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries," it’s that they introduce this method to reduce user burden by actively asking only the information necessary to distinguish the target.

Jane: It means instead of a long description upfront, you let the agent explore, gather candidates, and then use comparative judgment to figure out which one is correct through targeted questions.

Lu: The paper details how they build a candidate pool C where each candidate has both an image collage and a multi-view description, and then they use an MLLM to generate those descriptions based on the views collected.

Meng: I see them accumulating point clouds and RGB views for existing candidates; it sounds like a detailed exploration strategy to ensure you’re not missing any relevant instances in the environment.

Lalam: The description generation step is key because it aggregates attribute-value pairs across multiple views, capturing details that might be missed from just one single viewpoint.

Tom: That’s a big part of it; by aggregating those pairs, they create a richer set of attributes to work with for the subsequent comparison stage.

Jane: And then in the second stage, they introduce Recursive Comparative Judgment or RCJ, where they extract a discriminative attribute-value pair that splits the current pool and asks a binary question to eliminate inconsistent candidates.

Lu: The mechanism involves first finding a core set Gc with high intra-set similarity and then looking for an attribute that contrasts this core set against the remainder set Gr.

Meng: That process of selecting the discriminative attribute is important; they use NLI-based entailment scores to judge whether an attribute is present in Gc but absent in Gr.

Lalam: It’s smart because it allows the system to dynamically select the most useful attribute at each round based on what actually separates the current groups of candidates.

The paper's improvements: Tom: Now for the suggested improvements, ProCompNav suggests moving from independent matching strategies to this two-stage collect-then-compare pipeline as a major step forward in ambiguity resolution.

Jane: They emphasize that the main improvement is reframing disambiguation from asking open-ended questions about a single target to pool-level discriminative questioning.

Lu: They also highlight the creation of the Discriminative Attribute discovery process, which involves extracting candidate attributes and scoring them across all instances using NLI scores.

Meng: The refinement step for the remainder set Gr is another key idea; they re-evaluate candidates in that remainder set using the score on a proposed discriminative attribute to see if they should move into the core set Gc.

Lalam: A really interesting improvement is how it adapts for non-interactive settings, like TextNav, by pre-extracting an attribute set from the goal text instead of relying solely on user interaction.

Tom: And they also propose adapting to those non-interactive scenarios by using a text-only LLM verifier when the candidate pool is finally reduced to just one instance.

Jane: The whole structure aims to minimize user burden by ensuring that each question asked is specifically chosen to narrow the candidate set, rather than just querying attributes of individual candidates haphazardly.

Lu: They also suggest incorporating loop detection heuristics and line-of-sight rotation triggers during the initial exploration phase to make the candidate pool construction more efficient in unknown environments.

Conclusion: Tom: So, wrapping up on "ProCompNav: Proactive Instance Navigation with Comparative Judgment for Ambiguous User Queries," it seems the main takeaway is using a structured two-stage approach to manage ambiguity efficiently through comparative judgment.

Jane: Essentially, they’ve shown that by proactively building a pool and then iteratively pruning it with targeted yes or no questions, you can achieve a higher success rate while significantly reducing the user's effort.

Lu: The implications for language-driven instance navigation are substantial because it suggests that robust systems can handle ambiguity not just through massive descriptions, but through structured comparison between plausible candidates.

Meng: From a practical impact view, this means we can deploy agents that require far less manual guidance from users when dealing with complex visual search tasks in real-world applications.

Lalam: I see the biggest cultural improvement being in how we design these systems; it pushes us to build models that prioritize structured comparison over just raw description generation when faced with uncertainty.

Tom: It’s a solid approach, and while they show how to handle ambiguity better, they also note a limitation: the method relies on having enough candidates in the initial pool to perform meaningful comparisons effectively.

Jane: That's an important caveat; if the initial exploration doesn't yield a sufficiently large set of plausible instances, the comparative judgment stage might not work as well as expected.

Lu: And they also mention that while it works for interactive settings, adapting it fully to non-interactive scenarios still requires careful pre-extraction of attributes from the text goal to be truly effective.

Meng: The computational cost aspect is also something we need to watch; they do suggest replacing repeated open-ended MLLM scoring with lightweight NLI verification during the comparison stage to keep things running smoothly.

Lalam: Overall, ProCompNav gives us a clearer blueprint for how AI should tackle uncertainty by moving toward structured, interactive refinement rather than just hoping the initial prompt is perfect.

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

GSAI, POSTECH 2CSE, POSTECH 3Oracle

cs.AI, cs.RO

Submitted: 2026-05-07

Updated: 2026-09-29

Code: https://github.com/tree-jhk/procompnav

Project page: https://tree-jhk.github.io/procompnav/Abstract

Importance score: 92/100

The gist: ProCompNav is a two-stage framework that first constructs a candidate pool and then identifies the target through comparative judgment, replacing independent matching strategy with a two-stage

Key concepts

Proactive Instance Navigation
This approach means the system actively asks only the information needed to distinguish the target instance from others, rather than waiting for a perfect initial description. It shifts focus from describing one thing perfectly to asking questions that separate candidates within a set.
Comparative Judgment
This is the second stage of ProCompNav where the system uses targeted questions to narrow down a pool of candidates. It involves finding an attribute that contrasts a core set with the remainder and using entailment scores to eliminate inconsistent candidates iteratively.
Candidate Pool Construction
The framework first builds a candidate pool by collecting images and multi-view descriptions for potential instances. These descriptions are generated using a Multi-Modal Language Model (MLLM) based on the views collected, aggregating attribute-value pairs across multiple viewpoints to create rich data.

Terminology

Summary

ProCompNav is a two-stage framework that first constructs a candidate pool and then identifies the target through comparative judgment, replacing independent matching strategy with a two-stage collect-then-compare pipeline: candidate pool construction and candidate pool pruning through user interaction. 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.

The framework operates in two main stages:

Pool Construction Stage (Sec 4.2):

This stage explores the unknown environment to construct a candidate pool of category-c instances, denoted as C = [ci] for i=1 of N distinct candidates on which the Recursive Comparison Stage operates. Each candidate ci = (Ii, di) is represented by a multi-view collage image Ii and a multi-view description di. For each candidate i, the agent maintains an accumulated 3D point cloud Pi of the instance and a set of RGB views Vi captured from different viewpoints during exploration. For each new detection of category-c, if it sufficiently overlaps with the accumulated point cloud Pi of an existing candidate i, its RGB view is added to Vi. Otherwise, a new candidate is initialized with its point cloud and view set seeded from this detection. From Vi, the agent clusters the views in an embedding space into K clusters and selects a representative per cluster, arranging them into the multi-view collage Ii, and prompts an MLLM to produce the multi-view description di. The description summarizes the candidate’s attribute-value pairs—attributes (e.g., color, nearby objects) and their values (e.g., blue, next to a TV)—aggregated across the K views, capturing pairs that may be missed from any single viewpoint. Once C ≥ Nmin, the Pool Construction Stage terminates and the agent transitions to the Recursive Comparison Stage.

Recursive Comparison Stage (Sec 4.3):

This stage proposes Recursive Comparative Judgment (RCJ), which iteratively prunes the candidate pool C through binary questions on attribute-value pairs that contrast candidates. At each interaction round, ProCompNav extracts a Discriminative Attribute (DA) a∗t that partitions the current pool Ut into a coherent core set Gc and a remainder set Gr. This is achieved by first selecting a core set Gc ⊆ Ut with high intra-set similarity, defined by maximizing the mean pairwise similarity:

Discriminative Attribute (DA) Discovery (Sec 4.3.2):

To identify the target T∗, ProCompNav aims to discover a DA a∗t that contrasts the core set Gc against the remainder set Gr and enables effective pruning with a yes/no question. This requires instance-level evidence of whether an attribute is present in Gc but absent in Gr, which is precisely what an entailment classifier is trained to judge. The procedure involves three steps: (i) extracting a candidate set of attributes A from captions in Gc using an LLM; (ii) scoring each attribute a ∈ A on every instance i ∈ Gc ∪ Gr using the NLI-based entailment score s(di, a); and (iii) selecting the DA a∗t that maximizes the contrast between Gc and Gr:

Property-guided Group Refinement (Sec 4.3.3):

Although a∗t is selected to maximize contrast between Gc and Gr, it may also be possessed by candidates in Gr. Therefore, the remainder set Gr is refined by re-evaluating each candidate i ∈ Gr via the NLI-based entailment score s(di, a∗t), moving i to Gc if s(di, a∗t) ≥ τ.

Interactive Pruning and Re-exploration (Sec 4.3.4):

The agent then asks the user a binary question of whether the target T∗ possesses the selected DA a∗t. Based on the user’s response, ProCompNav updates the active candidate pool as:

Adaptation to TextNav (Sec 4.3.4):

For non-interactive settings like TextNav, ProCompNav adapts by: (1) using a goal-derived attribute set A preextracted from the text goal, and at each round selecting the discriminative attribute that best supports the current core set Gc; and (2) when Gr = ∅, invoking a text-only LLM-based verifier to compare the sole remaining candidate instance against the fixed text goal.

Improvements for AI systems

Here are specific improvements that can be made to AI systems by implementing the concepts from the ProCompNav paper, along with what these improved systems can achieve:

  1. Improve disambiguation robustness against visually similar distractors by replacing independent matching with a two-stage Collect-then-Compare pipeline (Candidate Pool Construction followed by Recursive Comparative Judgment).

  2. Implement a mechanism where the agent proactively builds a pool of plausible candidates based on initial ambiguous queries, rather than immediately asking open-ended questions about the target.

  3. Utilize Recursive Comparative Judgment (RCJ) to iteratively prune candidate sets using binary yes/no questions derived from contrastive attribute-value pairs, ensuring each question is chosen specifically to narrow the pool rather than querying attributes of individual candidates.

  4. Develop a similarity-based core set selection algorithm (using greedy peeling or KMeans on embedding spaces) within the RCJ stage to efficiently partition the candidate pool into semantically coherent groups (Core Set vs. Remainder Set).

  5. Employ Natural Language Inference (NLI) models as verifiers to score attribute entailment between a candidate's description and a proposed attribute, allowing the system to dynamically select the most discriminative attribute that maximizes contrast between the current core set and remainder set.

  6. Minimize user burden by restricting interactive queries to binary yes/no questions, which are designed to eliminate multiple inconsistent candidates at once, rather than requiring long descriptive answers.

  7. Adapt the framework for non-interactive settings (like TextNav) by pre-extracting a fixed attribute set from the target description and using this set for property-guided group refinement, enabling accurate target identification without any user interaction.

  8. Enhance exploration efficiency in unknown environments by incorporating loop detection heuristics (exponential moving average of position) and line-of-sight rotation triggers to prevent stagnation and ensure adequate coverage of potential candidate instances.

  9. Optimize computational cost by replacing repeated open-ended MLLM scoring with lightweight NLI verification during the comparative judgment stage, significantly reducing inference time per episode while maintaining or improving Success Rate (SR).

  10. Balance exploration efficiency (Path Length) and success rate by dynamically setting a minimum candidate pool size threshold (e.g., Nmin=5) to trigger the comparison stage, ensuring that only sufficiently representative pools are subjected to costly comparative judgment rounds.

These improved AI systems can achieve the following:

  1. Resolve highly ambiguous user queries in 3D environments with high accuracy, even when distractors share many visual or descriptive attributes with the target instance (improving Success Rate).

  2. Achieve state-of-the-art performance in both interactive and non-interactive tasks by efficiently managing the information gathering process, leading to higher overall success rates compared to previous independent matching baselines.

  3. Significantly reduce the required user interaction time and cognitive load by asking only targeted, binary questions that rapidly prune the search space, resulting in much shorter user responses (reducing Response Length).

  4. Operate efficiently in unknown environments by maintaining a balanced exploration strategy that avoids getting stuck in local loops while ensuring comprehensive coverage of relevant candidate instances.

  5. Provide highly scalable and cost-effective instance navigation by minimizing the reliance on expensive, repeated full-model inference during the critical decision-making phase, leading to faster execution times for deployment.

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