{"id":"80191aef-f4c1-47c5-82e9-f4d2afb4259c","arxiv_id":"2605.23655","paper_version":1,"verdict":"UNVERDICTED","confidence":"LOW","novelty_score":5.0,"correctness_risk":"unknown","formal_verification":"none","parameter_count":0,"one_line_summary":"CVSearch proposes an Assess-then-Search workflow combining expert-assisted search with Semantic Guided Adaptive Patching and Dynamic Bottom-Up Search to improve efficiency and accuracy on high-resolution image tasks for MLLMs.","lead":"CVSearch is a training-free adaptive framework that switches between expert-assisted visual search and a new semantic-aware scanning method for multimodal LLMs processing high-resolution images. A smart generalist might read it to understand practical ways to improve AI perception of detailed images without heavy retraining or excessive compute.","discovery_kind":"new_method","skeptic_critique":{"model":"grok-4.3","headline":"No significant objection identified","rationale":"Reader correctly flagged the workflow reliability as the key assumption; after inspecting the full description, that assumption is testable via the released code and does not appear internally inconsistent or unsupported by the experimental framing. No stronger load-bearing concern surfaces.","tokens_in":1694,"tokens_out":234,"duration_ms":21025,"concrete_test":"Reproduce the main HR benchmark results from the released GitHub code on the same splits; if accuracy or efficiency numbers deviate by more than the reported variance, the workflow's decision logic or implementation details require further inspection.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim rests on empirical results from the Assess-then-Search workflow plus the two proposed search primitives. The abstract and method description supply a clear, falsifiable pipeline (global insufficiency check → expert search → failure-triggered semantic scan with adaptive patching and bottom-up exploration). No internal contradiction, hidden assumption about bounded quantities, or unstated dependence on external models is visible in the provided text. The training-free nature and released code further reduce the usual correctness risks for such frameworks.","agreement_with_reader":"agree"},"referee_report":{"model":"grok-4.3","summary":"The paper introduces CVSearch, a training-free adaptive framework for high-resolution image perception in multimodal LLMs. It uses an Assess-then-Search workflow that first applies expert-assisted search when global information is insufficient and triggers semantic-aware scanning (with Semantic Guided Adaptive Patching and Dynamic Bottom-Up Search driven by a Visual Complexity prior) only upon failure. The central claim is that this resolves the coverage-efficiency trade-off and achieves state-of-the-art accuracy with substantially improved search efficiency on HR benchmarks; code is released.","tokens_in":1749,"tokens_out":368,"duration_ms":18661,"significance":"If the empirical results hold, the work addresses a practical bottleneck for MLLMs on high-resolution inputs by adaptively combining search primitives without training. The training-free design and released code are explicit strengths that support reproducibility and allow direct falsification of the pipeline. This could meaningfully improve vision-language performance on tasks requiring fine local detail.","major_comments":[],"minor_comments":[{"comment":"The description of how the initial global insufficiency check is implemented (e.g., which MLLM outputs or thresholds are used) should be expanded with pseudocode or a concrete example to make the Assess-then-Search decision reproducible.","section":"Method"},{"comment":"Table or figure reporting the efficiency metrics (e.g., number of patches or tokens processed) should include standard deviations across runs or datasets to substantiate the 'substantially improving search efficiency' claim.","section":"Experiments"},{"comment":"The paper should clarify whether the expert-assisted search component relies on any external models or APIs whose failure modes could affect the overall pipeline.","section":"Method"}],"recommendation":"minor_revision","confidential_remarks":null},"author_rebuttal":{"model":"grok-4.3","summary":"We thank the referee for the positive assessment of our work, the recognition of its practical significance for MLLMs on high-resolution inputs, and the recommendation for minor revision. The training-free design and code release are indeed intended to facilitate reproducibility and direct evaluation.","responses":[],"tokens_in":1216,"tokens_out":70,"duration_ms":10351,"standing_objections":[]},"desk_editor":{"model":"grok-4.3","letter":"The core takeaway is that this paper gives a clean procedural fix for the coverage-efficiency trade-off in visual search for multimodal models. It starts with expert-assisted search when global context falls short, then switches to semantic scanning only on failure, using Semantic Guided Adaptive Patching to keep regions coherent and Dynamic Bottom-Up Search guided by a visual complexity prior for local detail. The training-free design and released code stand out as practical pluses that let others reproduce the pipeline quickly. The description of how rigid grids cause fragmentation and how the new mechanisms avoid that is straightforward and easy to follow. The workflow itself is falsifiable and avoids obvious circularity or hidden parameters. That said, the SOTA accuracy and efficiency claims rest entirely on experiments whose details, baselines, ablations, and error bars are not visible in the abstract, so the size of the actual gain is still unclear. The components draw from existing ideas in adaptive search and patching, so the novelty sits in the scheduling logic rather than a fundamental shift. Minor risk is that the failure trigger for scanning could add overhead or miss cases, but nothing in the description suggests a load-bearing flaw. This paper is for researchers building efficient perception stacks for MLLMs on high-resolution data who need concrete implementation ideas rather than theoretical breakthroughs. It deserves peer review because the method is described precisely enough for referees to test the empirical claims and the code lowers the barrier to verification.","headline":"CVSearch offers a training-free Assess-then-Search workflow with adaptive patching and bottom-up exploration to cut compute on high-res MLLM inputs, but the advance is mostly in the specific integration rather than new primitives.","tokens_in":2261,"tokens_out":368,"would_cite":false,"duration_ms":12051,"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":"CVSearch enables multimodal LLMs to perceive high-resolution images by adaptively switching between expert-assisted search and semantic scanning.","keywords":["high-resolution image perception","multimodal large language models","visual search","adaptive patching","cognitive visual search","Assess-then-Search","semantic guided patching"],"falsifier":"A benchmark where expert-assisted search proposals miss critical objects but the subsequent semantic scanning also fails to recover them at higher cost than a full grid scan would have required.","tokens_in":2593,"feed_emoji":"🔍","tokens_out":415,"duration_ms":17446,"temperature":0.7,"pith_summary":"High-resolution images challenge multimodal large language models because existing visual search methods either miss key details or waste computation on redundant scans. CVSearch introduces a training-free Assess-then-Search workflow that first tries efficient expert-assisted search and only falls back to a new semantic-aware scanning method if that fails. The scanning uses Semantic Guided Adaptive Patching to keep objects whole and Dynamic Bottom-Up Search guided by visual complexity to focus effort where needed. If successful, this approach delivers higher accuracy on high-resolution benchmarks while cutting search time compared to prior methods.","feed_headline":"Adaptive search fixes blind spots in high-res image perception for LLMs","feed_subtitle":"CVSearch assesses global information first and switches to semantic scanning only when needed, delivering top accuracy with less computation","key_machinery":"The Assess-then-Search workflow that combines expert-assisted search with semantic-aware scanning triggered on failure, using Semantic Guided Adaptive Patching to avoid object fragmentation and Dynamic Bottom-Up Search driven by a Visual Complexity prior.","core_discovery":"CVSearch is a training-free adaptive framework that dynamically schedules search strategies via an Assess-then-Search workflow: it invokes expert-assisted search when global information is insufficient, and triggers semantic-aware scanning with Semantic Guided Adaptive Patching and Dynamic Bottom-Up Search only upon failure, achieving state-of-the-art accuracy and improved efficiency on HR benchmarks.","pith_inferences":[],"forward_implications":[],"fun_headline_variants":["CVSearch assesses then searches for high-res LLM image perception","Adaptive assess-then-search for efficiency in HR image LLMs","Semantic guided adaptive patching avoids object fragmentation","Dynamic bottom-up search targets local details in HR images"],"cache_read_input_tokens":2112,"weakest_assumption_plain":"The Assess-then-Search workflow correctly identifies when global information is insufficient and that failure of expert-assisted search reliably triggers the semantic scanning without introducing new blind spots or excessive overhead.","fun_headline_variants_meta":{"raw":{"variants":["CVSearch assesses then searches for high-res LLM image perception","Adaptive assess-then-search for efficiency in HR image LLMs","Semantic guided adaptive patching avoids object fragmentation","Dynamic bottom-up search targets local details in HR images"]},"model":"grok-4.3","cost_usd":0.006915,"raw_usage":{"total_tokens":3196,"prompt_tokens":645,"num_sources_used":0,"completion_tokens":60,"cost_in_usd_ticks":69149500,"prompt_tokens_details":{"text_tokens":645,"audio_tokens":0,"image_tokens":0,"cached_tokens":256},"completion_tokens_details":{"audio_tokens":0,"reasoning_tokens":2491,"accepted_prediction_tokens":0,"rejected_prediction_tokens":0}},"tokens_in":645,"tokens_out":60,"duration_ms":15127,"temperature":1.0,"reasoning_tokens":2491,"cache_read_input_tokens":256,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-05-25T04:45:09.494205+00:00","model_set":{"reader":"grok-4.3"},"falsifier":"A benchmark where expert-assisted search proposals miss critical objects but the subsequent semantic scanning also fails to recover them at higher cost than a full grid scan would have required.","supporting_citations":[],"review_version":1}