CVSearch: Empowering Multimodal LLMs with Cognitive Visual Search for High-Resolution Image Perception

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What is this paper about?

This paper is about helping AI systems that understand both pictures and text (called multimodal LLMs, or MLLMs) handle very large, detailed images better and faster. The authors introduce a simple add-on, called CVSearch, that tells the AI where to look in a big image so it doesn’t miss tiny important details or waste time on empty areas.

What questions did the researchers ask?

• How can we let an AI find small or hard-to-see details in huge, high-resolution images without slowing it down too much?
• Can we combine two common ways of searching images—asking a helper tool to point out likely spots, and scanning the whole image—so we get both speed and reliability?
• Can we avoid breaking objects apart when we cut an image into smaller pieces to look closer?

How does their method work?

Think about how you look for a friend in a huge stadium:

1. you first glance around to see if they’re obvious,
2. you might ask a staff member for help,
3. if that doesn’t work, you scan the seats in a smart order, focusing on crowded spots first.

CVSearch copies this human strategy with an “Assess-then-Search” workflow. Here’s the idea in everyday terms:

Step 1: Quick check (Assess)

The AI first asks itself, “Can I answer the question with what I already see?” If yes, it answers right away. If not, it moves to the next step.

Step 2: Ask a visual expert

The system asks a separate, specialized vision tool (think of it like a helpful security guard) to point out where the important objects might be in the image. This is fast when it works. If the helper can’t find the target (for example, the object is tiny or partly hidden), CVSearch doesn’t give up—it switches to a smarter scan.

Step 3: Smart scanning with meaning-aware patches (not rigid grids)

Instead of cutting the image into equal squares (which can slice a single object into pieces), CVSearch groups parts of the image by meaning. This is called Semantic Guided Adaptive Patching (SGAP).

• Analogy: rather than cutting a photo into a checkerboard, it cuts along natural boundaries—like sky, road, building, person—so each piece makes sense on its own.
• It also measures “visual complexity,” which is like asking, “Does this area look busy or empty?” Busy areas likely have useful details, so the system focuses there first and skips big blank spaces.

Step 4: Bottom-up search (start with the details, then zoom out)

Many systems search top-down (big areas first, then smaller ones), which can go wrong if the first guess is off. CVSearch starts from the smallest, most detailed pieces and works upward. If it finds strong clues at a small scale, it uses that evidence to guide the next steps. If not, it combines information and moves up a level to try again. This reduces mistakes and helps the AI recover if it initially looks in the wrong place.

Training-free add-on

Importantly, CVSearch is “training-free.” You don’t have to retrain the AI model. You attach CVSearch on top, and it tells the model where to look and when to switch strategies.

What did they find?

• Better accuracy on tough, high-resolution tests: CVSearch helped several popular open-source models answer more questions correctly on multiple benchmarks that use big images (like 4K and 8K). It often reached or beat the best reported results at the time.
• Works across different models and sizes: Whether the base model was small (2–3 billion parameters) or large (32 billion), CVSearch consistently improved performance. Smaller models gained the most, but even strong large models improved.
• Faster than full-image scanning: Exhaustive “scan everything” methods are slow. CVSearch was about three times faster than some scanning baselines on a key benchmark, while also being more accurate. It’s not as fast as doing nothing extra, but the speed–accuracy trade-off is much better.
• Great at tiny-object tasks: On datasets where the questions depend on noticing very small details, CVSearch gave big boosts, especially in open-ended questions where guessing is harder.

Why these results matter:

• The “meaning-aware” patches keep objects intact, making it easier for the AI to reason about what it sees.
• The bottom-up strategy helps the AI find small details first, so it doesn’t get misled early on.
• The “Assess-then-Search” plan avoids wasting time: easy questions get answered quickly, and only tricky ones trigger deeper searching.

Why does this matter?

• Practical AI for real images: Many real photos (maps, medical images, satellite pictures, drone footage) are huge and packed with tiny details. CVSearch makes today’s models more reliable on these without costly retraining.
• Smarter resource use: The system focuses computing power where it matters most, making high-resolution understanding more efficient and accessible.
• A step toward human-like vision: The method borrows from how people search visually—quick global checks, helpful hints, and detailed follow-ups—showing that cognitive strategies can guide better AI design.

A quick note on limitations and future work

• It’s still slower than answering with no search at all, because it sometimes needs multiple steps. Future work could speed this up by evaluating multiple regions in parallel.
• The current helper tool is one specific “visual expert.” Testing other experts or combining several could make the system even more robust.

In short, CVSearch is a smart, plug-in search strategy that helps AI look at big images like a careful human would: check the big picture, get help if needed, then scan in a meaningful, detail-first way. This leads to better answers, faster searches, and fewer missed tiny details.

Below is a concise list of knowledge gaps, limitations, and open questions left unresolved by the paper. Each point highlights a concrete avenue for future work.

• Calibration and reliability of confidence signals: The framework hinges on MLLM-derived confidences (c_q, c_o) and thresholds (τ_q, τ_curr, τ̂_q) without calibration analysis; how robust are these signals across prompts, temperatures, models, and domains, and can calibration (e.g., temperature scaling) improve switching decisions?
• Prompt sensitivity and parsing robustness: The extraction of target objects from queries (LLM-based with SpaCy fallback) is brittle for abstract concepts, attributes, synonyms, or multilingual queries; how should the system robustly parse and ground complex, relational, or attribute-centric instructions?
• Expert dependence and generality: Only SAM 3 is used as the visual expert; how does CVSearch behave with other experts (e.g., GroundingDINO, OWL-ViT, GLIP, DETR variants) and ensembles, and what design choices are needed to harmonize differing feature spaces and proposal formats?
• Proposal validation heuristic: The coverage check (“count of segmented instances equals target count”) can fail for grouping, occlusion, or ambiguous object categories; what more reliable validation metrics (e.g., text-conditioned grounding scores) can replace this brittle criterion?
• Reuse of expert features: SGAP assumes expert features H_e retain useful semantics even when proposals fail; what is the empirical quality of these features across failures, which layers to use, and how does feature choice affect clustering and search outcomes?
• Semantic fragmentation measurement: The paper claims reduced fragmentation with SGAP but provides no direct metric; can future work define and report quantitative measures (e.g., boundary adherence, object completeness per patch) to validate semantic integrity?
• SGAP under top-down search: Ablations show “SGAP + top-down” underperforms “rigid grid + top-down” on some benchmarks; under what scene conditions does SGAP help or hurt, and what modifications (e.g., overlap-aware merging) mitigate its failure modes?
• Clustering hyperparameters and model selection: The search for k* uses a silhouette/overlap cost with fixed k_min/k_max and SLIC superpixels; what is the sensitivity to these parameters, superpixel granularity N, and clustering algorithms, and can k be selected in a data-driven or learned manner?
• Visual complexity prior validity: The complexity score c_v (1 − mean cosine similarity) may conflate texture with semantic richness; how well does c_v predict “useful” regions across domains, and could learned saliency/uncertainty maps outperform this heuristic?
• Priority weighting: The node-priority weights (α, β, γ) are fixed; can they be tuned per-task/domain or learned (e.g., via reinforcement learning) to maximize accuracy/efficiency trade-offs?
• Stopping schedule design: The decay of τ_curr is unspecified and unvalidated; what decay schedules (or Bayesian stopping rules) optimize recall vs. precision, and how do they affect false-positives/negatives?
• Depth selection and scalability: Depth D is rule-based (2 for single-object, 3 for multi-object) and not validated for ultra-HR (>8K) or dense scenes; can depth be selected adaptively per sample, and how does performance scale at 12K/16K with controlled compute budgets?
• Iteration bounds and termination: The iterative loop (scan → hand back top-ranked node to expert) has no reported maximum iterations or safeguards; how to bound runtime while preserving recall, and what is the optimal iteration policy?
• Evidence aggregation: The paper does not specify how multi-patch evidence is fused for final answers (beyond per-patch confidence checks); what aggregation mechanisms (memory, attention over patch summaries, voting) yield the best downstream reasoning?
• Multi-target reasoning: Decoupling multi-target queries into independent Q_d may miss relational constraints; how to jointly reason about relationships (spatial, logical) across patches and targets without losing global context?
• Domain generalization: Evaluations focus on a limited set of HR benchmarks (aerial UAV included) but exclude domains like medical histopathology, documents, satellite mega-scenes, or nighttime/low-light; how robust is CVSearch across diverse visual statistics and degradations?
• Multilingual and long-form queries: Robustness to non-English, code-mixed, or very long queries is not evaluated; what adaptations are needed for cross-lingual grounding and instruction-following?
• Efficiency profiling and system design: Throughput is reported, but memory usage, token counts, and energy/latency breakdowns (expert vs. SGAP vs. MLLM passes) are absent; which system-level optimizations (batching across nodes, mixed precision, FlashAttention, model quantization) deliver the best gains?
• Superpixel and clustering compute costs: The scalability and GPU/CPU overheads of SLIC and agglomerative clustering at 8K–16K are not analyzed; can approximate or learned segmentation be used to reduce preprocessing latency?
• Failure mode taxonomy: The paper mentions typical failure cases in the appendix but lacks a structured taxonomy; what are the dominant errors (mis-parsing, expert misses, poor clustering, threshold miscalibration), and how can they be automatically detected and remedied online?
• Comparative breadth: Comparisons omit several strong HR pipelines (recent AnyRes variants, learned routing/search policies, region-level summarizers); how does CVSearch fare against these baselines under matched compute budgets?
• Training-free vs. learned policies: The switching, patching, and traversal policies are hand-crafted; can lightweight training (imitation/RL) learn better switching, complexity priors, and stopping criteria while preserving generality and minimal supervision?
• Robustness to adversarial or noisy inputs: Sensitivity to noise, compression artifacts, motion blur, or adversarial patterns in HR images is untested; how resilient is the Assess-then-Search pipeline under such perturbations?
• Negative or non-localized queries: Many queries lack discrete localizable objects (e.g., “Is the pattern striped?” or global counting); how should the expert and scanning stages handle attribute-only or global-context tasks without forcing object extraction?
• Resource-aware early exits: Beyond c_q-based decisions, can resource-constrained settings (mobile, real-time) impose explicit time/compute budgets with graceful early-exit strategies that retain acceptable accuracy?
• Privacy and security considerations: The reuse of expert features and iterative cropping may expose sensitive regions; what safeguards (on-device processing, cropping policies) are needed for privacy-critical applications?

Immediate Applications

Below are applications that can be deployed now by integrating CVSearch’s training-free Assess-then-Search workflow, Semantic Guided Adaptive Patching (SGAP), and Dynamic Bottom-Up Search into existing multimodal LLM (MLLM) stacks.

• Bold name: Cognitive Zoom-Assistant SDK for High-Res Visual Q&A
  • Sector: Software, Developer Tools
  • Use case: Add drop-in HR image perception to apps using Qwen/LLaVA/InternVL to answer fine-grained questions about large photos (e.g., “What is the serial number on the device label?”).
  • Workflow/product: Python SDK or REST microservice that wraps an MLLM with CVSearch routing: global glance → expert proposals → semantic-aware scan with bottom-up prioritization. Exposes per-step logs and heatmaps.
  • Dependencies/assumptions: Requires a visual expert (e.g., SAM family) for proposals and features; GPU memory sufficient for patching; tuning thresholds (Tq, Ty) per domain; licensing of base MLLM and expert.
• Bold name: Retail Planogram and Shelf Compliance Auditor
  • Sector: Retail, Supply Chain
  • Use case: Verify product placement, detect missing SKUs, read tiny price tags/logos in HR aisle images without scanning empty shelf space.
  • Workflow/product: Batch process store images nightly; CVSearch prunes low-complexity backgrounds, scans dense regions, and surfaces evidence patches per violation with explanations.
  • Dependencies/assumptions: High-res images; catalog of target objects; reasonable lighting; expert model capable of segmenting product-like regions.
• Bold name: PCB and Surface Defect Inspector Copilot
  • Sector: Manufacturing, Quality Assurance
  • Use case: Assist inspectors by spotting micro-cracks, solder bridges, or scratches in HR captures; answer “Is there a bridge on pad X?” with evidence crops.
  • Workflow/product: Human-in-the-loop tool that triages boards; CVSearch triggers scan only on complex zones, reducing compute while preserving micro-defect sensitivity.
  • Dependencies/assumptions: Domain calibration for thresholds; ground-truth sampling for acceptance testing; integration with AOI camera formats.
• Bold name: UAV/Remote Sensing Tiny-Target Finder
  • Sector: Energy, Agriculture, Public Safety
  • Use case: Identify small objects in aerial HR images (e.g., insulator faults, wildlife counts, missing solar-cell strings, stranded persons) with prioritized scanning of high-entropy regions.
  • Workflow/product: Post-flight analysis service for HR frames; supports queries like “How many damaged panels?” with patch-level evidence.
  • Dependencies/assumptions: Adequate resolution and coverage; scene-dependent clustering works on domain features; potential replacement of SAM with a remote-sensing expert for better proposals.
• Bold name: OSINT and Newsroom SatScan Assistant
  • Sector: Media, Intelligence, Policy Analysis
  • Use case: Verify claims on satellite or telephoto imagery (e.g., “Are there new vehicles near the facility entrance?”) with minimal compute by pruning background.
  • Workflow/product: Analyst workstation plugin that logs Assess-then-Search steps for auditability; exports evidence patches and confidence.
  • Dependencies/assumptions: High-res inputs; expert generalizes to aerial scenes or is swapped for a satellite-trained expert; careful threshold tuning to avoid false positives.
• Bold name: Cultural Heritage and Museum Gigapixel Guide
  • Sector: Education, Arts
  • Use case: Interactive Q&A on paintings or artifacts (e.g., “What color is the figure’s brooch?”) preserving object semantics while zooming.
  • Workflow/product: Web viewer backed by CVSearch; users ask questions while the system reveals supporting patches and narrative answers.
  • Dependencies/assumptions: Gigapixel tiling strategy compatible with patching; content rights and privacy for high-res archives.
• Bold name: Document Microprint and Form Assistant
  • Sector: Enterprise Software, Finance, Legal
  • Use case: Read microtext, stamps, signatures, and seals in HR scans; answer “Does the contract include clause X?” with locational evidence.
  • Workflow/product: CVSearch-driven doc-QA that uses semantic patching to avoid fragmenting lines/figures; integrates with e-discovery pipelines.
  • Dependencies/assumptions: OCR integration for extracted text; expert able to propose text/figure regions; PII handling policies.
• Bold name: Accessibility Detail Finder for Low-Vision Users
  • Sector: Accessibility, Consumer Apps
  • Use case: On-device assistant to describe small details in photos or printed materials (e.g., expiry dates, fine print), focusing compute on informative zones.
  • Workflow/product: Mobile app employing CVSearch to minimize latency and energy by skipping blank regions; returns concise descriptions and highlighted crops.
  • Dependencies/assumptions: Edge-capable MLLM or server fallback; careful UX around confidence thresholds; privacy-safe processing.
• Bold name: Security and Safety Keyframe Analyzer
  • Sector: Security, Transportation
  • Use case: Analyze HR keyframes from CCTV for small safety hazards or forbidden items with scene-aware scanning to reduce wasteful compute on static backgrounds.
  • Workflow/product: Keyframe selection + CVSearch pipeline; escalates only evidence-backed alerts with explicit patch-level rationale.
  • Dependencies/assumptions: Frame selection policy; calibration to avoid bias; expert proposals robust in low light or occlusion.
• Bold name: Research and Annotation Accelerator
  • Sector: Academia, Data Ops
  • Use case: Semi-automatic labeling of tiny objects; propose semantically coherent patches to annotators to avoid object fragmentation and speed up curation.
  • Workflow/product: Labeling UI that presents CVSearch-ranked patches and existence confidences; exports patch trees and logs for reproducibility.
  • Dependencies/assumptions: Domain-specific prompts for parsing targets; annotator final control; dataset governance policy.

Long-Term Applications

These applications require further research, scaling, domain adaptation, validation, or systems engineering (e.g., video, real-time constraints, regulatory approval).

• Bold name: Clinical-Grade Digital Pathology Triage and Decision Support
  • Sector: Healthcare
  • Use case: Triage whole-slide images and assist pathologists in locating rare micro-lesions; ask targeted questions about suspicious regions with evidence trails.
  • Tools/workflow: Replace general visual expert with pathology-tuned proposal models; validate bottom-up search on WSIs; integrate with PACS/LIS.
  • Assumptions/dependencies: Rigorous clinical validation, bias audits, regulatory clearance, domain-specific experts; data governance and privacy.
• Bold name: Real-Time Perception for Autonomous Systems
  • Sector: Robotics, Automotive
  • Use case: Active fine-grained perception under compute budgets (e.g., reading tiny signs, detecting small obstacles) by routing attention to high-complexity regions.
  • Tools/workflow: Video extension of CVSearch with temporal coherence; batched parallel node evaluation; on-chip acceleration; hard real-time scheduling.
  • Assumptions/dependencies: Video-aware search policies; deterministic latency; hardware support; robust expert under motion blur and adverse weather.
• Bold name: Smart-City Wide-Area Monitoring with Privacy-Preserving Triage
  • Sector: Public Sector, Policy, Security
  • Use case: City-scale high-res feeds analyzed with semantic pruning to minimize processing of irrelevant or privacy-sensitive regions; produce auditable evidence snippets.
  • Tools/workflow: Policy-configured thresholds; on-prem processing; redaction of non-target areas; audit logs from Assess-then-Search path.
  • Assumptions/dependencies: Legal frameworks for surveillance; differential privacy or on-device processing; transparent governance.
• Bold name: Energy-Efficient HR AI Standards and Procurement Guidelines
  • Sector: Policy, Sustainability
  • Use case: Inform green-AI procurement by recommending cognitive search architectures that reduce redundant compute on HR imagery.
  • Tools/workflow: Benchmark suites adding throughput-per-accuracy metrics and evidence accountability; reference profiles for different sectors.
  • Assumptions/dependencies: Consensus metrics; independent evaluations; vendor adoption.
• Bold name: Scientific Imaging Copilots (Astronomy, Microscopy)
  • Sector: Science, R&D
  • Use case: Discover faint or tiny features in massive images (e.g., galaxies, nanoparticles) with dynamic bottom-up search and domain-aware experts.
  • Tools/workflow: Swap SAM-like experts with domain detectors; integrate with lab notebooks; export provenance of search decisions for reproducibility.
  • Assumptions/dependencies: Domain-specific pre-processors; extremely high resolutions (16K+); deeper trees and robust pruning strategies.
• Bold name: Gigapixel Creative QA for Digital Content and CAD/PCB Design
  • Sector: Media, EDA/Design
  • Use case: Automated checks for tiny artifacts, misalignments, or label inconsistencies in gigapixel artworks and complex layouts.
  • Tools/workflow: CVSearch-based linting pipeline producing patch-level issues; integrates with CI for design assets.
  • Assumptions/dependencies: Domain rule sets; expert proposals trained on design primitives; organizational workflow integration.
• Bold name: On-Device Drone and Edge Agents with Cognitive Search
  • Sector: Drones, Edge Computing
  • Use case: Near real-time triage while bandwidth-constrained by selectively scanning informative tiles and transmitting only evidence patches.
  • Tools/workflow: Quantized MLLMs; batched node inference; streaming partial results; adaptive thresholds tied to energy budget.
  • Assumptions/dependencies: Hardware acceleration; robust operation offline; safety cases for autonomy.
• Bold name: Explainable Compliance Auditing for Industrial Sites
  • Sector: Compliance, Insurance
  • Use case: Verify signage, safety gear, and micro-labels across sprawling facilities with searchable explanations and patch-level proofs.
  • Tools/workflow: Evidence pool and confidence logs stored for audits; policy-aware prompts; integration with incident management.
  • Assumptions/dependencies: Clear compliance taxonomies; controlled camera positioning; periodic recalibration.
• Bold name: OS-Level High-Res Photo Assistant
  • Sector: Consumer Platforms
  • Use case: Natively query 100MP+ photos in galleries for tiny details (e.g., “What’s the trail marker number?”) with automatic zoom and annotation overlays.
  • Tools/workflow: System API exposing CVSearch to mobile apps; on-device caching of semantic clusters; privacy-preserving local execution.
  • Assumptions/dependencies: Efficient mobile MLLMs; model update channels; user consent and data controls.

Notes on Cross-Cutting Feasibility Assumptions

• Model and expert availability: CVSearch relies on an external visual expert for region proposals and features; if SAM 3 is unavailable, comparable segmenters/detectors must be substituted and validated per domain.
• Compute and latency: While faster than rigid scan methods, iterative search is slower than single-pass MLLMs; batching, parallel node evaluation, and attention optimizations are recommended for production.
• Threshold tuning and robustness: Information sufficiency (Tq), pruning (Ty), and stopping criteria require domain-specific calibration to balance recall on tiny objects with efficiency.
• Data governance and privacy: Even with pruning, HR data may include PII or sensitive content; consider on-device processing, redaction, and audit logging of search decisions.
• Evaluation and accountability: For regulated or safety-critical settings, retain Assess-then-Search logs, evidence patches, and confidence scores to support audits and error analysis.

• Adaptive image tree: A hierarchical representation of an image formed by recursively partitioning it into adaptive patches for guided search. "we model the HR image I as an adaptive tree T"
• Agglomerative Clustering: A bottom-up hierarchical clustering algorithm that iteratively merges clusters based on similarity. "Agglomerative Clustering (Müllner, 2011)"
• AnyRes: A mechanism that decomposes high-resolution images into flexible grids and a global view to preserve details in MLLMs. "AnyRes mechanisms (Li et al., 2024a;b) decompose high-resolution (HR) images into flexible grids"
• Attentional templates: Internal target representations that guide selective attention during visual search. "guided by attentional templates (Wolfe, 2020)"
• Cognitive Assess-then-Search: A decision workflow that first assesses global information sufficiency before invoking targeted search strategies. "Assess-then-Search workflow"
• Cosine similarity: A vector similarity measure used here to quantify dispersion of features within a region. "cosim(.,.) denotes the cosine similarity"
• Cropping-based paradigms: Methods that split high-resolution images into local crops for processing, often at the cost of semantic coherence. "Cropping-based paradigms (Li et al., 2024a;b;c) partition images into local crops."
• Dynamic Bottom-Up Search: A search strategy that starts from fine-grained leaf regions and aggregates evidence upwards to parents. "we devise a Dynamic Bottom-Up Search strategy"
• Existence confidence: The model’s estimated confidence that specified target objects are present in a given image patch. "assess the existence confidence co of the target objects in O."
• Fine-grained Cross-instance Perception (FCP): Evaluation of detailed perception across multiple instances in an image. "FSP: Fine-grained Single-instance Perception; FCP: Finegrained Cross-instance Perception."
• Fine-grained Single-instance Perception (FSP): Evaluation of detailed perception for a single instance in an image. "FSP: Fine-grained Single-instance Perception; FCP: Finegrained Cross-instance Perception."
• Gist extraction: Rapid, parallel processing of a scene’s overall structure to inform subsequent focused attention. "non-selective global perception (for gist extraction)"
• Hierarchical backbones: Multi-level neural network architectures that process features at different scales for efficiency and detail. "hierarchical backbones (e.g., Con- vNext (Woo et al., 2023))"
• HR Visual Encoder: A class of encoder architectures designed to handle high-resolution inputs in multimodal models. "HR Visual Encoder paradigms (Ge et al., 2024; Luo et al., 2025)"
• In-context capability: An LLM’s ability to perform tasks by conditioning on examples or prompts without additional training. "we leverage the in-context capability from the LLM base of the MLLM"
• Information sufficiency: A measure of whether the current visual context is adequate to answer the posed query. "as the information sufficiency:"
• Multimodal LLMs (MLLMs): Models that jointly process and reason over multiple modalities such as text and images. "Multimodal LLMs (MLLMs) capable of sophisticated reasoning."
• Nonselective pathway: The cognitive route that rapidly processes global scene information in parallel. "The nonselective pathway rapidly extracts global gist"
• Region adjacency graph: A graph structure encoding the connectivity of neighboring image regions or superpixels. "construct a region adjacency graph G"
• SAM 3: The third-generation Segment Anything Model used as a visual expert for concept-driven region proposals. "we adopt SAM 3 (Carion et al., 2025) as our visual expert."
• Scan-based Visual Search: An exhaustive region scanning approach that ensures coverage but may ignore semantics. "Scan-based Visual Search (e.g., RAP (Wang et al., 2025d), ZoomEye (Shen et al., 2025), DC2 (Wang et al., 2025c))"
• Scene-aware Scanning: A scanning method guided by scene semantics rather than rigid grids to focus on informative areas. "triggers the Scene-aware Scanning phase"
• Semantic fragmentation: The breaking of object semantics across patch boundaries due to rigid partitioning. "causing semantic fragmentation"
• Semantic Guided Adaptive Patching (SGAP): A clustering-based patching method that forms semantically coherent image regions. "we propose Semantic Guided Adaptive Patching (SGAP)."
• Semantic sawtooth effect: Repeated semantic inconsistencies introduced by rigid grid partitioning across adjacent patches. "the "semantic sawtooth" effect (Huang et al., 2024)"
• Silhouette score: A clustering quality metric reflecting how well-separated and compact clusters are. "the sil- houette score (Vardakas et al., 2024)"
• Simple Linear Iterative Clustering (SLIC): A superpixel segmentation algorithm that over-segments images into compact regions. "Simple Linear Iterative Clus- tering (SLIC) (Achanta et al., 2012)"
• Superpixels: Small, homogeneous pixel groups used as atomic units for further region clustering. "N atomic superpixels A = {a1, a2, . . . , aN}"
• Token explosion: A rapid increase in sequence length (tokens) caused by many patches, leading to high computation. "miti- gate token explosion via hierarchical backbones"
• Top-down tree search: A hierarchical traversal strategy that proceeds from coarse root nodes to fine-grained leaves. "perform top-down tree search"
• Tree pruning: The process of discarding low-value nodes in a search tree to improve efficiency. "we prune T by discarding nodes with cy < Ty."
• Visual Complexity: A prior or score quantifying how semantically rich or diverse a region’s features are. "we introduce a Visual Complexity score"
• Visual Expert Assisted Search: A search strategy that uses external vision experts to propose candidate regions efficiently. "Visual Expert Assisted Search (e.g., SEAL (Wu & Xie, 2024), DyFo (Li et al., 2025a), V2-SAM (Pan et al., 2025))"
• Visual tokens: Vector representations of visual features fed into the LLM for multimodal reasoning. "visual tokens Z7 = P(Hv)"

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