{"id":"cec4e35c-d50b-4349-976f-2ad20583d6f3","arxiv_id":"2607.26769","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":6.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":3,"one_line_summary":"Multimodal models usually pick relevant visual actions, but faithful rendering is the bottleneck, and corrupted visual feedback drops accuracy over 10 points in 3D tasks.","lead":"See2Think tests whether multimodal models actually use the sketches and intermediate images they generate while reasoning. It finds that models often plan useful visual steps, but rendering quality is the bottleneck, and corrupting visual feedback still changes answers.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.5","headline":"No significant objection beyond the reader's already-identified intervention-quality caveat; the central claim holds under the paper's own controls.","rationale":"The paper's contribution is diagnostic infrastructure plus a multi-model empirical pattern, not a single fragile theorem. Matched settings already address the usual confound (visual traces as decoration). Human validation of process scores is reasonably strong; WrongRender quality is imperfect but directionally consistent after filtering. The reader's CONDITIONAL + medium correctness_risk correctly targets release of data/logs and tighter intervention-quality reporting rather than rejecting the design. No stricter verdict shift is warranted from a second pass.","tokens_in":24754,"tokens_out":473,"duration_ms":10731,"concrete_test":"On the public release, recompute Table 7 / Fig. 8 Acc_VAoT − Acc_WrongRender for 3D only on the human Strict-Pass WrongRender subset (and, if logs allow, on automatic proxies for corruption validity); if the 3D drop falls below ~5 pp or loses monotonicity in Feedback Uptake, the dependence headline needs softening; if it stays ≥10 pp, the claim is reinforced.","verdict_should_be":"UNCHANGED","load_bearing_attack":"No additional load-bearing concern. The strongest claim is an empirical map (setting-dependent outcomes; high action relevance; lower render faithfulness; utility ≠ dependence; WrongRender drops especially in 3D), not a universal causal law. The paper already separates CoT / NoRender / VAoT / WrongRender, reports process scores with human audit (92.9–96.9% at least partially reasonable), and shows the VAoT–WrongRender gap remains positive on quality-filtered subsets (strict 8.82 pp, relaxed 3.19 pp; App. F.2). The reader's weakest assumption—imperfect modify_key quality (56.7% Strict Pass) and judge/renderer proxies—is the real residual risk and is already priced into CONDITIONAL. I do not find an independent internal inconsistency (e.g., caption-filter circularity or action-space under-specification) that would overturn the claim if intervention quality is reported carefully.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.5","summary":"See2Think asks whether multimodal models genuinely use intermediate visual states rather than merely producing visual traces. The authors introduce See2ThinkBench (1,200 caption-filtered, mostly free-form problems across 12 categories in 2D structured, 3D scene, and real-world settings) and Visual Action-of-Thought (VAoT), which logs thoughts, structured actions, rendered states, and later reasoning under CoT, VAoT-NoRender, VAoT, and task-relevant VAoT-WrongRender. Across GPT-5.5, GPT-o3, Gemini 3.5 Flash, and Qwen3-VL-32B-Instruct they report that no single setting dominates; action relevance is near saturation while render faithfulness is the main bottleneck; high feedback uptake need not raise accuracy; and corrupted feedback still induces behavioral dependence, with large drops especially in 3D scenes. Process scores are human-audited, and WrongRender quality is audited with filtered re-analysis in the appendix.","tokens_in":25017,"tokens_out":1409,"duration_ms":33222,"significance":"The paper addresses a timely gap: existing visual-thinking benchmarks largely score final answers or aggregate process quality without jointly testing action relevance, render faithfulness, and behavioral dependence under matched interventions. The four-way protocol, caption-only shortcut filtering, full 1.2K paired outcome tables, 4,800-trajectory process analysis, outcome-stratified gaps, render benefit/harm transitions, and human audits (process judgments ≥92.9% reasonable-or-partial; WrongRender quality audit with positive filtered drops) are concrete methodological contributions. If the dependence results hold under clearer main-text quality controls, the work gives the community a reusable diagnostic that separates visual-state utility from behavioral dependence—useful for tool-using and “thinking with images” systems beyond any single accuracy leaderboard.","major_comments":[{"comment":"§4.3–4.4 and Takeaway 3/6 lean on VAoT–WrongRender accuracy drops (e.g., >10 pp and up to 15.5 pp in 3D at Feedback Uptake=1; Fig. 8, Table 7) as evidence of behavioral dependence. Appendix F.2 reports only 56.7% Strict Pass (68/120) and 78.3% Acceptable on the human WrongRender audit, and the paired drop shrinks from 8.82 pp (strict) to 3.19 pp (relaxed). The direction is preserved, but magnitude and environment-specific claims are sensitive to intervention quality. The main text should report Strict/Acceptable rates and quality-filtered drops alongside Fig. 8, and soften absolute “over 10 percentage points” language where it is not restricted to quality-passed cases.","section":"§4.3–4.4, Fig. 8, Appendix F.2"},{"comment":"Process diagnosis (Table 3, Fig. 6) rests on a single external judge (GPT-5.4) selecting one key step and scoring Action Relevance / Render Faithfulness / Feedback Uptake on {0, 0.5, 1}. Human audit (Table 4, §4.5) is reassuring at the reasonable-or-partial level (92.9–96.9%) but strict “Reasonable” rates are lower for Render Faithfulness (70.2%) and Action Relevance (74.8%), with large annotator disagreement on the strict boundary (Appendix F.1). Because Takeaway 4 identifies faithful execution—not action selection—as the bottleneck, the paper should either (i) report inter-annotator agreement / score-level confusion on the audited subset for Render Faithfulness, or (ii) show that the correct–incorrect Render gap in 3D (0.097 in Fig. 6b) remains under human-rescored or double-judged subsets. Without that, the stage-localization claim is only partially stress-tested.","section":"§4.4–4.5, Table 3–4, Fig. 6, Appendix F.1"}],"minor_comments":[{"comment":"Robot Manipulation accuracy is near floor (0–4% in Table 2) across all settings. A short note in §4.2 on whether this is grounding granularity, action-format mismatch, or benchmark construction would prevent over-reading “no visual benefit” on that category.","section":"§4.2, Table 2"},{"comment":"Caption-only filtering uses a strong MLLM captioner and a ≥3/5 solvability rule (Appendix A.2). State in the main §2.2 how many candidates were removed and whether any sensitivity check (e.g., 2/5 vs 4/5) was run, so readers can gauge residual text-shortcut risk.","section":"§2.2, Appendix A.2"},{"comment":"Table 1’s “Action–Render–Use Diagnosis” checkmark is fair, but a one-sentence clarification that MIRA/ViC/TWI/TwiFF were not re-run under VAoT would avoid implying head-to-head process scores on identical items.","section":"Table 1, §2.1"},{"comment":"Normalize naming of the judge model (GPT-5.4 in §4.1 vs GPT-5.5 as an evaluated model) and fix minor typos in the abstract/intro spacing (“imagesduring”, “itremainsunclear”).","section":"Abstract, §4.1"},{"comment":"Figure 5 and group aggregates would be easier to read with error bars or per-model spreads, given the strong model-dependence emphasized in Takeaway 1.","section":"Figure 5"}],"recommendation":"minor_revision","confidential_remarks":"Fit for a solid empirical methods/benchmark venue in multimodal reasoning. I do not see a load-bearing internal inconsistency; the residual risk is intervention- and judge-proxy quality, which the authors already partially quantify in appendices. Requiring main-text surfacing of F.2 and a bit more stress on the Render Faithfulness audit is enough—no need for a full redesign or new model suite for acceptance. Scope is evaluation/diagnosis rather than a new training method; that is appropriate if the journal accepts benchmark-and-protocol papers."},"author_rebuttal":null,"desk_editor":{"model":"grok-4.5","letter":"The thing worth knowing is that this paper actually tests whether models use intermediate visual states, instead of treating sketches and tool traces as self-justifying. They build a 1.2K caption-filtered bench across 12 categories and run four matched settings—CoT, action-only, closed-loop VAoT, and task-relevant WrongRender—so you can see planning, rendering, uptake, utility, and dependence as separate pieces.\n\nWhat is new is the package, not any single slogan. Caption-only solvability filtering, self-generated closed-loop actions with an external renderer, stage scores (action relevance / render faithfulness / feedback uptake), and corrupted-feedback interventions together give a cleaner map than MIRA, ViC-Bench, or the process-reward benches. The empirics are consistent: no setting wins everywhere; action relevance sits near ceiling; render faithfulness is the clear bottleneck; high uptake does not equal accuracy gain; WrongRender still moves answers, with large drops in 3D. Full 1.2K tables, 4,800 trajectories, benefit/harm splits, and human audits (process judgments mostly reasonable-or-partial; filtered WrongRender still shows a positive accuracy drop) make the central claim hold under their own controls.\n\nSoft spots are real but proportionate. WrongRender is only 56.7% strict-pass on audit, and drop size shrinks under looser filters—so dependence magnitudes are noisier than the qualitative story. Process scores come from another model on one key step; the renderer and action space are external and constrained. Four models only. None of that overturns the design; it caps how hard you should lean on the exact percentage points.\n\nMath is not the load-bearing part—this is controlled empirical work. Data construction and citation pattern look honest relative to the prior visual-thinking literature. For anyone building tool-using MLLMs, process rewards, or “thinking with images” claims, this is useful. I would send it to referees; it deserves serious review, not a desk reject. Engage with it, cite the protocol and the utility-vs-dependence split, and keep the intervention-quality caveat in view.","headline":"Solid evaluation package that cleanly separates visual-state utility from behavioral dependence; residual risk is intervention quality, not the design.","tokens_in":25681,"tokens_out":535,"would_cite":true,"duration_ms":14704,"reading_group":"yes","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"grok-4.5","headline":"Multimodal models often pick useful visual actions, but faithful rendering is the bottleneck—and corrupted visual feedback still changes their answers.","keywords":["multimodal reasoning","visual chain-of-thought","intermediate visual states","action relevance","render faithfulness","feedback uptake","corrupted feedback","benchmark evaluation"],"falsifier":"Re-run the paired VAoT versus WrongRender comparison on a large human-verified set where every corruption clearly alters task-critical content while staying visually plausible; if accuracy no longer drops systematically—especially in 3D scenes—behavioral dependence is not established.","tokens_in":25623,"feed_emoji":"👁️","tokens_out":952,"duration_ms":21710,"temperature":0.7,"pith_summary":"This paper asks whether multimodal models that sketch, annotate, crop, or otherwise produce intermediate images during reasoning actually depend on those visual states, or merely decorate a mostly textual chain of thought. It builds a 1,200-problem benchmark of visually dependent tasks across 2D diagrams, 3D scenes, and real-world settings, then runs a closed-loop protocol that records planned visual actions, externally rendered states, and later reasoning under matched conditions—including deliberately corrupted but task-relevant feedback. Across several strong models, no single strategy (text-only, plan-without-render, full closed loop) wins everywhere. Models usually choose relevant operations, yet realizing those operations faithfully is the clearest weak link, and high uptake of returned pixels does not reliably raise accuracy. Still, when the returned state is corrupted in a task-relevant way, accuracy falls—by more than ten points in 3D scenes—showing behavioral dependence even when clean visual feedback was not a free accuracy boost.","feed_headline":"Corrupted sketches cut multimodal accuracy by over 10 points","feed_subtitle":"Models pick relevant visual moves, but rendering fails often—and bad feedback still steers answers.","key_machinery":"Visual Action-of-Thought (VAoT): an intervenable closed loop that interleaves textual thoughts, structured visual actions, externally rendered image states, and subsequent reasoning, compared across CoT, action planning without rendering, standard VAoT, and task-relevant WrongRender feedback, with process scores for action relevance, render faithfulness, and feedback uptake.","core_discovery":"Genuine intermediate visual-state use is model- and environment-dependent rather than a universal gain: models typically select task-relevant visual operations, faithful rendering is the main bottleneck after planning, high feedback uptake need not improve final accuracy, and task-relevant corrupted feedback still induces measurable behavioral dependence, with accuracy drops over 10 percentage points in 3D scene reasoning under controlled interventions.","pith_inferences":["Training objectives that reward tool calls or intermediate images without a faithfulness or dependence check may inflate visual-trace metrics while leaving the render bottleneck untouched.","If 3D relational tasks show the strongest corruption sensitivity, intermediate-state methods may matter most where structure is not already explicit in the input diagram.","Product systems that show users model-drawn highlights should treat those overlays as potentially causal inputs, not just explanations, because answers move when the overlay is wrong.","A natural next stress test is learned or model-internal renderers under the same WrongRender pairing, to see whether the bottleneck is the external editor or the model’s use of any returned pixels."],"forward_implications":["Final-answer gains alone cannot certify that a model is thinking with images; action relevance, render faithfulness, and feedback uptake must be measured separately.","Improving intermediate visual reasoning should prioritize faithful execution of planned edits over teaching models to request more visual operations.","Utility and dependence diverge: a visual workspace can steer decisions under corruption even when clean rendering yields little net accuracy benefit.","Benchmarking and tool design should filter text-only shortcuts and use matched corrupted-feedback interventions, not only oracle sketches or end-task scores.","The best visual-reasoning regime will remain model- and environment-specific rather than one fixed closed-loop recipe."],"fun_headline_variants":["Corrupted visual feedback cuts accuracy over 10 points","Rendering fails more than planning in multimodal reasoning","Visual-state use depends on model and environment","Models pick relevant visuals but botch faithful rendering","Task-relevant sketch corruption drops 3D accuracy >10 pts"],"cache_read_input_tokens":16512,"weakest_assumption_plain":"The claim rests on treating an external constrained renderer plus automatic process scores and task-relevant corrupted edits as faithful enough stand-ins for whether a model truly generated, saw, and used an intermediate visual state.","fun_headline_variants_meta":{"raw":{"variants":["Corrupted visual feedback cuts accuracy over 10 points","Rendering fails more than planning in multimodal reasoning","Visual-state use depends on model and environment","Models pick relevant visuals but botch faithful rendering","Task-relevant sketch corruption drops 3D accuracy >10 pts"]},"model":"grok-4.5","effort":"low","cost_usd":0.00552,"raw_usage":{"total_tokens":1471,"prompt_tokens":772,"num_sources_used":0,"completion_tokens":58,"cost_in_usd_ticks":55204000,"prompt_tokens_details":{"text_tokens":772,"audio_tokens":0,"image_tokens":0,"cached_tokens":128},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":641,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":772,"tokens_out":58,"duration_ms":10967,"temperature":1.0,"reasoning_tokens":641,"cache_read_input_tokens":128,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-07-30T21:36:16.714309+00:00","model_set":{"reader":"grok-4.5"},"falsifier":"Re-run the paired VAoT versus WrongRender comparison on a large human-verified set where every corruption clearly alters task-critical content while staying visually plausible; if accuracy no longer drops systematically—especially in 3D scenes—behavioral dependence is not established.","supporting_citations":[],"review_version":1}