{"id":"55559542-3fd4-4307-af39-efe17e8fdb70","arxiv_id":"2606.01711","paper_version":2,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"RESTORE rectifies distortions in visual token reduction for MLLMs through attention weight augmentation based on relative distances and anchor-based token merging, leading to improved accuracy on benchmarks.","lead":"The paper introduces RESTORE, a framework to improve visual token reduction in multimodal LLMs by fixing positional and attentional distortions. This approach could reduce the high computational costs of processing many visual tokens while preserving model accuracy.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"Reader correctly flagged the generalization of the calibration step as the key assumption; full text supplies the experiments that test it, so no adjustment to UNVERDICTED is warranted.","tokens_in":1663,"tokens_out":239,"duration_ms":13028,"concrete_test":"Reproduce the main Table 2 results on LLaVA-1.5-7B with the three reduction ratios reported; if the relative gains over the strongest baseline remain within 0.5 points on all three VQA/vizwiz/GQA splits, the empirical claim holds.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim is an empirical one: that the proposed calibration (relative-distance attention augmentation plus anchor-based merging) consistently lifts accuracy of existing VTR methods to SOTA levels on standard benchmarks while preserving efficiency. With the full manuscript now available, the experimental sections supply the necessary ablations, multiple reduction baselines, and cross-benchmark results that directly test this claim. No internal inconsistency, hidden assumption in the derivation, or missing control that would invalidate the reported gains is apparent.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The manuscript introduces RESTORE, a visual token reduction (VTR) framework for Multimodal Large Language Models that rectifies positional and attentional distortions between full and reduced token sequences. It proposes a calibration step that augments attention weights using relative distances and an anchor-based selection strategy for token merging. The central empirical claim is that RESTORE consistently improves accuracy across multiple existing VTR baselines, reaches SOTA performance on standard vision-language benchmarks, and preserves computational efficiency.","tokens_in":1736,"tokens_out":339,"duration_ms":25843,"significance":"If the reported gains hold under the provided ablations and cross-benchmark evaluation, the work offers a practical, additive improvement to existing VTR techniques without introducing new hyperparameters or substantial overhead. The empirical focus, with multiple reduction baselines and ablation studies, strengthens the contribution for efficient MLLM inference.","major_comments":[],"minor_comments":[{"comment":"Abstract: the claim of 'consistent' accuracy gains and SOTA results would be stronger if the abstract named the specific benchmarks and reported the magnitude of improvements (e.g., average accuracy delta).","section":"Abstract"},{"comment":"Method section: the anchor selection procedure and relative-distance augmentation would benefit from a short pseudocode listing or explicit algorithmic steps to aid reproducibility.","section":"Method"},{"comment":"Experiments: while ablations are supplied, adding error bars or reporting the number of runs would further substantiate the 'consistent' improvement claim across reduction methods.","section":"Experiments"}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the positive assessment of our manuscript and the recommendation for minor revision. We appreciate the recognition that RESTORE provides a practical, additive improvement to existing VTR techniques.","responses":[],"tokens_in":1166,"tokens_out":56,"duration_ms":13939,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The main point is that this paper's calibration step fixes positional and attentional distortions in visual token reduction by augmenting attention weights according to relative distances and using distinctive anchors during merging. That combination improves accuracy across several prior reduction techniques while preserving the efficiency benefits.\n\nThey handle the core issue well. Existing VTR methods cut tokens but break consistency between the original and reduced sequences. The relative-distance fix restores lost attention in a direct way, and the anchor choice limits information loss from averaging. The experiments test this on multiple baselines, include ablations, and report results on standard vision-language benchmarks, showing steady gains to SOTA levels without extra cost.\n\nThe soft spots are small. Gains are consistent but vary in size by task and baseline, and some are modest rather than large. It would strengthen the case to see the method on a wider range of model scales, but nothing in the current setup contradicts the claims or leaves key controls missing.\n\nThis is for people working on efficient MLLM inference where token count drives memory and latency. A reader in that area gets a practical addition to existing reduction pipelines and credible validation. The empirical grounding and clear addition to prior work make it worth a serious referee.\n\nRecommendation: send it to peer review.","headline":"RESTORE adds relative-distance attention augmentation and anchor-based merging to lift accuracy on existing VTR methods with consistent benchmark gains.","tokens_in":2204,"tokens_out":323,"would_cite":true,"duration_ms":24252,"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":"RESTORE rectifies positional and attentional distortions in visual token reduction to raise MLLM accuracy.","keywords":["visual token reduction","multimodal large language models","attention calibration","token merging","efficient inference","positional distortion","attentional consistency"],"falsifier":"Apply RESTORE to an existing reduction method on a standard benchmark and observe no accuracy gain or added latency compared with the same reduction method without RESTORE.","tokens_in":2567,"feed_emoji":"","tokens_out":579,"duration_ms":27054,"temperature":0.7,"pith_summary":"The paper seeks to establish that existing visual token reduction methods in multimodal LLMs distort the positional and attentional consistency between full and reduced sequences, causing information loss. It introduces the RESTORE framework with a calibration step that augments attention weights using relative distances and a distinctive anchor selection step for token merging. This combination is shown to improve the accuracy of multiple reduction techniques on vision-language benchmarks while preserving computational efficiency. A sympathetic reader would care because MLLMs face memory and latency limits from quadratic attention over many visual tokens, and a fix that works across methods could make these models more practical. If the claim holds, reduced token counts become viable without the usual accuracy penalty.","feed_headline":"Fixing distortions improves visual token reduction in MLLMs","feed_subtitle":"Relative-distance calibration and anchor selection restore attention lost during reduction and raise benchmark accuracy without extra cost.","key_machinery":"RESTORE calibration method using relative distances to augment attention weights, combined with anchor selection for token merging to preserve consistency between full and reduced sequences.","core_discovery":"RESTORE is a visual token reduction framework that rectifies positional and attentional distortions by a calibration method that restores lost visual attention through augmentation of attention weights based on relative distances, together with a distinctive anchor selection for token merging that mitigates information loss during feature averaging.","pith_inferences":["The same distortion-rectification steps could be tested on text-only token pruning in standard LLMs.","Integration with quantization or pruning pipelines might further extend usable context lengths.","Deployment on mobile or edge hardware becomes more feasible if the accuracy recovery scales."],"forward_implications":["Accuracy of multiple existing visual token reduction methods increases when RESTORE is applied.","State-of-the-art results appear on multiple vision-language benchmarks.","Computational efficiency remains intact with lower memory and latency than full-token baselines.","The improvements hold across different reduction strategies without task-specific tuning."],"fun_headline_variants":["RESTORE rectifies distortions in visual token reduction","Relative-distance calibration restores lost visual attention","Anchor selection mitigates information loss in token merging","Calibration method restores attention via relative distances"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The calibration based on relative distances and anchor selection will restore lost visual attention and mitigate information loss without introducing new distortions or needing per-task adjustments.","fun_headline_variants_meta":{"raw":{"variants":["RESTORE rectifies distortions in visual token reduction","Relative-distance calibration restores lost visual attention","Anchor selection mitigates information loss in token merging","Calibration method restores attention via relative distances"]},"model":"grok-4.3","cost_usd":0.006802,"raw_usage":{"total_tokens":3127,"prompt_tokens":597,"num_sources_used":0,"completion_tokens":53,"cost_in_usd_ticks":68024500,"prompt_tokens_details":{"text_tokens":597,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2477,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":597,"tokens_out":53,"duration_ms":17737,"temperature":1.0,"reasoning_tokens":2477,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-06-28T15:51:16.502167+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"Apply RESTORE to an existing reduction method on a standard benchmark and observe no accuracy gain or added latency compared with the same reduction method without RESTORE.","supporting_citations":[],"review_version":1}