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arxiv: 2606.31719 · v1 · pith:AZO3QVVAnew · submitted 2026-06-30 · 💻 cs.CL · cs.AI

Seeing Is Not Sharing: Some Vision-Language Models Overestimate Common Ground in Asymmetric Dialogue

Nan Li , Albert Gatt , Massimo Poesio This is my paper

Pith reviewed 2026-07-01 05:16 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords vision-language modelscommon grounddialogue groundingasymmetric dialogueMapTaskinterpretation matchingalignment predictionmultimodal bias
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The pith

Vision-language models treat map content as evidence of mutual understanding even without dialogue establishing it.

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

The paper tests whether vision-language models can separate potential shared perception from actual common ground built through interaction in collaborative dialogue. It does so by running an interpretation-matching task on over 13,000 annotated expressions from HCRC MapTask dialogues, systematically varying whether models see map images, textual map descriptions, or neither alongside dialogue context. Models given relevant map content improve at some predictions but over-predict alignment on cases where participants have not actually grounded the reference, while non-informative images eliminate the over-prediction. The pattern indicates reliance on static map features rather than tracking how references are established or repaired across turns. A reader would care because many real-world uses of these models involve asymmetric information and require accurate modeling of what each participant knows the other knows.

Core claim

In the interpretation-matching task, authentic map images or textual descriptions of the same content cause models to over-predict that dialogue participants share an interpretation, even when the dialogue history shows no such grounding has occurred. This bias degrades accuracy specifically on non-aligned references. Non-informative images remove the over-prediction entirely, showing the effect is driven by task-relevant map content rather than the visual modality. Calibration and reference-chain analyses indicate models draw on static referential cues visible on the maps instead of following the incremental establishment of common ground through dialogue turns. The pattern appears most str

What carries the argument

The controlled interpretation-matching task on HCRC MapTask reference expressions that varies dialogue context and map-information access to measure over-prediction of alignment.

If this is right

  • Providing informative map content, whether visual or textual, increases over-prediction of alignment while lowering accuracy on non-aligned cases.
  • The bias is triggered by task-relevant map content rather than the presence of any image.
  • Models rely on static referential cues instead of tracking incremental grounding across dialogue history.
  • The overestimation pattern occurs across multiple vision-language model families.

Where Pith is reading between the lines

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

  • Models exhibiting this bias would likely misjudge mutual knowledge in real asymmetric settings where one participant lacks visual access entirely.
  • The static-cue reliance suggests current architectures may need explicit mechanisms for logging what has versus has not been verbally confirmed.
  • The result raises the possibility that similar conflations occur in other multimodal tasks involving potential versus realized shared knowledge.
  • Testing the same manipulation on additional dialogue corpora with different visual domains could show whether the bias is specific to spatial map tasks.

Load-bearing premise

The controlled manipulations of dialogue context and map-information access in the interpretation-matching task accurately isolate the contribution of visual or textual map content to alignment predictions without confounding effects from annotation quality or task formulation.

What would settle it

A follow-up run in which models receive both the map and explicit dialogue turns stating that a reference has not been grounded; if over-prediction of alignment persists at the same rate, the claim that models treat map content as established common ground would be supported.

Figures

Figures reproduced from arXiv: 2606.31719 by Albert Gatt, Massimo Poesio, Nan Li.

Figure 1
Figure 1. Figure 1: The interpretation matching task, illustrated on a misalignment example. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: (a) Accuracy and (b) yes-rate by reference [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Prompt template. Template variables (${...}) are filled per instance based on the text-access and map￾access conditions. For example, under the startT text-access window and both-maps access level, ${text_access} is filled with “You can read the dialogue from the beginning of the conversation through the end of the transaction that contains the target reference expression (i.e., including the subsequent li… view at source ↗
Figure 4
Figure 4. Figure 4: Example filling of ${map_access} under the text-landmark-names condition for dialogue q1ec1. Map landmark information: - Giver’s map landmarks: start, caravan park, old mill, abandoned cottage, fenced meadow, fenced meadow, west lake, trig point, monument, nuclear test site, east lake, farmed land, finish - Follower’s map landmarks: start, caravan park, picket fence, mill wheel, forest, abandoned cottage, … view at source ↗
Figure 5
Figure 5. Figure 5: Example filling of ${map_access} under the text-discrepancy-detail condition for dialogue q1ec1. 15 [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Giver’s map for map pair 0 (map0g), used for the spatial-description comparison in Table [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Macro F1 and yes-rate across map-access conditions for Qwen3-VL models (8B, 2B, 4B) at startT. [PITH_FULL_IMAGE:figures/full_fig_p017_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Macro F1 and yes-rate across map-access conditions for Gemma-3 models (4B, 12B) at startT. [PITH_FULL_IMAGE:figures/full_fig_p017_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Macro F1 by grounding status (aligned, pending, misunderstood) across all models and map-access [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
read the original abstract

In collaborative dialogue, shared perception does not guarantee shared interpretation. Mutual understanding must be established through interaction. We investigate whether vision-language models (VLMs) can distinguish what could be shared from what has been shared between dialogue participants through grounding. We formulate this as an interpretation-matching task on 13,077 annotated reference expressions from HCRC MapTask dialogues, and evaluate VLMs under systematically controlled manipulations of dialogue context and map-information access. Our results show that providing authentic map images improves overall performance but shifts models toward over-predicting alignment. Textual descriptions of the same map content reproduce this bias, while non-informative images suppress alignment predictions entirely, indicating that the bias is driven by task-relevant map content, not the visual channel. This improvement comes at the cost of degraded accuracy on non-aligned cases. Calibration analysis and reference-chain tracking further suggest that models rely on static referential cues on the maps rather than tracking how grounding unfolds through dialogue history. We observe these patterns most clearly in Qwen3-VL-8B-Instruct and, to varying degrees, in four additional models from two architecture families. In models that exhibit the bias, map content, whether presented visually or textually, is treated as evidence of mutual understanding, conflating potential with established common ground.

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

1 major / 2 minor

Summary. The paper claims that some vision-language models overestimate common ground in asymmetric dialogue by treating map content (whether visual or textual) as evidence of mutual understanding rather than tracking how grounding unfolds through dialogue history. This is demonstrated via an interpretation-matching task on 13,077 annotated reference expressions from the HCRC MapTask corpus, with systematic manipulations of dialogue context and map-information access across multiple VLMs; results show improved overall performance but degraded accuracy on non-aligned cases when map content is provided, with the bias replicated textually and suppressed by non-informative images.

Significance. If the result holds, the work identifies a concrete limitation in VLMs' handling of dynamic common ground, with implications for collaborative dialogue systems. Credit is due for the use of an established public dataset, controlled experimental manipulations, evaluation across five models from two architecture families, and additional analyses such as calibration and reference-chain tracking that go beyond aggregate accuracy.

major comments (1)
  1. [Methods (interpretation-matching task)] Methods (interpretation-matching task description): The central claim that map content drives the bias (rather than task artifacts) rests on the assumption that the 13,077-reference task cleanly isolates map-access effects. The manuscript provides no quantitative checks on inter-annotator agreement for alignment labels or prompt ablations, so it remains possible that annotation reliability or prompt formulation confounds contribute to the reported over-prediction on non-aligned cases and the textual-map replication.
minor comments (2)
  1. [Abstract] Abstract: The abstract refers to 'four additional models from two architecture families' without naming them; listing the specific models (e.g., in a footnote or parenthetical) would improve immediate clarity for readers.
  2. [Results] Results section: The description of 'reference-chain tracking' is mentioned as supporting evidence but lacks a concrete example or figure illustrating how static referential cues are distinguished from dialogue-history tracking; adding one would strengthen the interpretation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their careful reading and constructive feedback on our manuscript. We address the single major comment below and will incorporate revisions to strengthen the methodological transparency of the interpretation-matching task.

read point-by-point responses

  1. Referee: The central claim that map content drives the bias (rather than task artifacts) rests on the assumption that the 13,077-reference task cleanly isolates map-access effects. The manuscript provides no quantitative checks on inter-annotator agreement for alignment labels or prompt ablations, so it remains possible that annotation reliability or prompt formulation confounds contribute to the reported over-prediction on non-aligned cases and the textual-map replication.

    Authors: We agree that the absence of reported inter-annotator agreement (IAA) metrics for the alignment labels and the lack of explicit prompt ablations represent a methodological gap that could affect interpretation of the results. The alignment labels were produced by extending the original HCRC MapTask annotations through a multi-stage verification process involving multiple annotators; we will add Cohen's kappa (or equivalent) statistics computed on a held-out subset in the revised manuscript. For prompt formulation, we performed limited sensitivity checks during development but did not report them systematically. We will include an appendix with prompt-ablation results (varying instruction phrasing and few-shot examples) to demonstrate that the core bias pattern is robust. These changes directly address the concern that annotation reliability or prompt artifacts may confound the map-content effect. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical evaluation on public dataset

full rationale

The paper reports an empirical study that formulates an interpretation-matching task on the existing public HCRC MapTask annotations (13,077 references) and evaluates off-the-shelf VLMs under controlled input manipulations. No equations, parameter fitting, derivations, or self-citation chains are used to generate the central claims; performance differences are measured directly against the fixed annotations. The analysis is therefore self-contained against external benchmarks and does not reduce any result to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper depends on the validity of the HCRC MapTask corpus and its reference-expression annotations as a faithful representation of asymmetric dialogue grounding; no free parameters, new entities, or ad-hoc axioms beyond standard domain assumptions about dialogue are introduced.

axioms (1)
  • domain assumption The HCRC MapTask dialogues and reference-expression annotations faithfully capture asymmetric dialogue and the process of establishing common ground.
    The entire evaluation is built on this dataset and its annotations.

pith-pipeline@v0.9.1-grok · 5759 in / 1296 out tokens · 43478 ms · 2026-07-01T05:16:25.890351+00:00 · methodology

discussion (0)

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Reference graph

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