{"id":"9ad3b32b-617a-4a3c-b157-dfd11171eeb1","arxiv_id":"2605.31387","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":4.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"Multi-turn multi-agent dialogue improves VLM spatial reasoning in collaborative reconstruction only marginally, with text descriptions outperforming visual inputs.","lead":"The paper tests vision-language models on a collaborative task where agents use dialogue to reconstruct a structure from visual and text inputs. It finds that multi-turn multi-agent dialogue yields only small gains in spatial reasoning performance.","discovery_kind":"new_application","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"Reader correctly identified the external-validity gap as the primary uncertainty and set UNVERDICTED due to abstract-only access. With the claim now visible, no additional internal inconsistency or unsupported assumption appears in the reported results. The proposed test would still be useful for tightening the 'improves but barely' qualifier but does not alter the current verdict.","tokens_in":1665,"tokens_out":262,"duration_ms":12569,"concrete_test":"Re-run the reconstruction task with an added baseline condition that uses only single-turn non-collaborative VLM prompting on the same inputs; if the multi-turn multi-agent setting shows no statistically significant lift (p>0.05) beyond the single-turn case, the headline claim of 'improves ... but only barely' would require qualification.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract and extracted claim are internally consistent and modest in scope. The central finding (spatial reasoning remains difficult; text > image; decomposed images help) follows directly from the described experimental setup without evident internal contradiction or unsupported leap. The reader's weakest_assumption correctly flags external validity to robotics, but that is a scope limitation rather than a flaw in the argument as presented.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript introduces a collaborative structure-building task in which multiple VLM agents engage in multi-turn dialogue to reconstruct a target structure from visual and/or textual inputs. It evaluates both open-weight and closed VLMs across interaction settings, input modalities, and image representations (detailed text vs. whole vs. decomposed images), concluding that spatial reasoning over visual representations remains difficult, that detailed text yields higher reconstruction success, and that decomposed images provide modest gains.","tokens_in":1707,"tokens_out":449,"duration_ms":18807,"significance":"If the quantitative results and evaluation protocol are made rigorous, the work would supply a concrete, reproducible benchmark for assessing grounded spatial reasoning and instruction generation in VLMs, directly relevant to collaborative robotics. The modest scope of the claims (improvement “but only barely”) and the emphasis on persistent limitations constitute a useful negative result for the field.","major_comments":[{"comment":"Abstract and §4 (Results): the central claims rest on directional statements (“higher reconstruction success”, “improve performance”, “only barely”) with no reported success rates, confidence intervals, statistical tests, or effect sizes. Without these numbers it is impossible to judge whether the observed differences are reliable or practically meaningful.","section":"Abstract and §4"},{"comment":"§3 (Experimental Setup): the manuscript provides no description of the precise success metric used to score reconstruction, the prompting templates, the exact model versions and temperatures, or the number of trials per condition. These protocol details are load-bearing for any comparative claim across modalities and representations.","section":"§3"}],"minor_comments":[{"comment":"Title: the phrase “But Only Barely” should be justified by a specific quantitative comparison (e.g., absolute percentage-point gain) once the results are reported.","section":"Title"},{"comment":"Figure captions and tables: ensure that every reported condition lists the exact VLM, modality, and representation variant so readers can replicate the comparisons.","section":"Figures/Tables"}],"recommendation":"major_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the constructive feedback. The comments correctly identify gaps in quantitative reporting and protocol transparency that limit the interpretability of our results. We will revise the manuscript to address both points directly.","responses":[{"response":"We agree that the current presentation relies on directional language without supporting numerical evidence. In the revised version we will report exact per-condition success rates, 95% confidence intervals, effect sizes, and the results of appropriate statistical tests (e.g., McNemar or paired t-tests) to allow readers to assess both reliability and practical significance of the observed differences.","revision_made":"yes","referee_comment":"[Abstract and §4] Abstract and §4 (Results): the central claims rest on directional statements (“higher reconstruction success”, “improve performance”, “only barely”) with no reported success rates, confidence intervals, statistical tests, or effect sizes. Without these numbers it is impossible to judge whether the observed differences are reliable or practically meaningful."},{"response":"We acknowledge that these implementation details are currently underspecified. The revised manuscript will include: (1) the exact definition and computation of the reconstruction success metric, (2) the full prompting templates for all agent roles and modalities, (3) precise model identifiers and version numbers together with temperature and other sampling parameters, and (4) the number of independent trials run per experimental cell.","revision_made":"yes","referee_comment":"[§3] §3 (Experimental Setup): the manuscript provides no description of the precise success metric used to score reconstruction, the prompting templates, the exact model versions and temperatures, or the number of trials per condition. These protocol details are load-bearing for any comparative claim across modalities and representations."}],"tokens_in":1287,"tokens_out":383,"duration_ms":16401,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that adding multi-turn multi-agent dialogue improves VLM results on this collaborative structure-building task, but the gains are small, and text descriptions of the target still outperform visual inputs. The paper sets up a new task where agents reconstruct a structure through dialogue, testing open and closed VLMs under different modalities and image breakdowns. They compare single-turn versus multi-turn, single-agent versus multi-agent, and full versus decomposed images. This produces a clear pattern: spatial reasoning stays hard, detailed text lifts success rates across conditions, and splitting images into parts helps a little.\n\nThe evaluation is systematic enough to isolate those effects, and the negative result on visual grounding matches what other VLM studies have found. Running the same setup across model types adds some breadth. No new method is proposed, which keeps the scope honest.\n\nThe soft spots are the narrow task definition and the reliance on one reconstruction metric. It is not obvious how well this measures real robotic collaboration, and the abstract gives no numbers or error bars, so the size of the \"barely\" improvement is hard to judge without the full tables. If the paper includes proper statistical checks and clear success criteria, that would help.\n\nThis is for people who need concrete benchmarks on where current VLMs fall short in grounded multi-agent settings. It is worth sending to peer review because the experimental design is straightforward and the result is reproducible in principle, even if the scope stays limited.","headline":"This evaluation shows multi-turn dialogue gives only marginal gains on a VLM spatial reconstruction task, with text beating images.","tokens_in":2196,"tokens_out":361,"would_cite":false,"duration_ms":16761,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.3","headline":"Multi-turn multi-agent dialogue only marginally improves VLM spatial reasoning in collaborative structure reconstruction.","keywords":["vision-language models","spatial reasoning","collaborative dialogue","structure reconstruction","multi-agent interaction","grounded language generation","VLM evaluation"],"falsifier":"An experiment in which the same VLMs achieve high reconstruction success rates using only visual inputs and no text descriptions would falsify the reported difficulty.","tokens_in":2561,"feed_emoji":"🤖","tokens_out":524,"duration_ms":13503,"temperature":0.7,"pith_summary":"The paper tests vision-language models in a collaborative task where agents must reconstruct a target structure through dialogue using visual or textual inputs. It establishes that spatial reasoning over visual representations stays difficult even with multi-turn interactions, while detailed text descriptions of the target produce higher reconstruction success across conditions. Decomposed image representations provide modest gains over single images. These outcomes point to ongoing limits in visual spatial grounding and the generation of grounded instructions for joint tasks.","feed_headline":"Dialogue barely helps VLMs on spatial reconstruction tasks","feed_subtitle":"Text descriptions outperform images and decomposed views help only modestly in multi-turn collaborative tests.","key_machinery":"The collaborative structure-building task framework in which multiple VLMs use dialogue to reconstruct a target from visual and textual inputs.","core_discovery":"In a framework where VLMs engage in multi-turn dialogue to reconstruct structures, detailed text representations of the target yield higher reconstruction success across modality conditions while decomposed image representations improve performance, yet spatial reasoning over visual representations remains difficult for the evaluated models.","pith_inferences":["Hybrid text-visual input strategies may be required for reliable robotic collaboration involving spatial layouts.","The task setup could be extended to measure whether performance scales with additional dialogue turns or agents.","Similar evaluation frameworks might reveal comparable limits when applied to other grounded language tasks such as navigation or assembly."],"forward_implications":["Detailed text representations of the target yield higher reconstruction success across all modality conditions.","Decomposed image representations improve performance relative to whole-image inputs.","Spatial reasoning over visual representations remains difficult even with multi-turn multi-agent dialogue.","Limits persist in visual spatial grounding and grounded instruction generation for collaborative VLM agents."],"fun_headline_variants":["VLMs gain little from dialogue in spatial reconstruction","Text beats images for VLM collaborative structure building","Multi-turn talks offer scant VLM spatial task improvement","Visual spatial reasoning stays tough for dialogue VLMs","Dialogue helps VLMs reconstruct structures only slightly"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The collaborative structure-building task and chosen metrics accurately measure the spatial reasoning and grounded instruction generation capabilities relevant to real robotic applications.","fun_headline_variants_meta":{"raw":{"variants":["VLMs gain little from dialogue in spatial reconstruction","Text beats images for VLM collaborative structure building","Multi-turn talks offer scant VLM spatial task improvement","Visual spatial reasoning stays tough for dialogue VLMs","Dialogue helps VLMs reconstruct structures only slightly"]},"model":"grok-4.3","cost_usd":0.004955,"raw_usage":{"total_tokens":2383,"prompt_tokens":587,"num_sources_used":0,"completion_tokens":69,"cost_in_usd_ticks":49549500,"prompt_tokens_details":{"text_tokens":587,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":1727,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":587,"tokens_out":69,"duration_ms":12482,"temperature":1.0,"reasoning_tokens":1727,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T22:28:56.132221+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"An experiment in which the same VLMs achieve high reconstruction success rates using only visual inputs and no text descriptions would falsify the reported difficulty.","supporting_citations":[],"review_version":1}