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Seeing Parts, Reasoning about Worlds: Visual Inference under Partial Observation
Wei Wang, Wenqiao Zhang, Yutong Lin, Jun Xiao, Yueting Zhuang
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Abstract
Abstract:World modeling under partial observation requires reasoning about the complete worlds that remain compatible with limited visual evidence. Occluded objects and unseen regions can leave several world states possible; additional views can exclude alternatives and strengthen the conclusions supported by the observations. We introduce WorldScope to study this process through possible-world semantics, evidence-grounded data, and learned visual representations. WorldScope-1.2M provides 1.2 million English question-answer pairs spanning eight world properties, ten task interfaces, and three observation protocols. Its answers encode confirmed facts, supported bounds, and unresolved possibilities. Complementary supervision comprises 4,800 certified counterworld groups with equivalent base observations and different hidden object configurations and query answers. These groups provide physical witnesses of ambiguity and training-only labels for world compatibility and view-induced exclusions. We propose WorldFlow, which composes cross-view entity evidence and support-surface coverage into an image-subset evidence lattice. Counterworld compatibility and transition objectives train subset representations to reflect how new observations constrain possible worlds. A shared answer generator uses these representations to predict the strongest supported conclusion. WorldScope-Bench evaluates claim judgments and evidence-dependent conclusions as views are selected, combined, removed, or ordered. On its 5,000-question test set, WorldFlow reaches 64.34% exact accuracy, improving over the same backbone trained on QA alone by 24.88 percentage points. It retains 50.43% accuracy on the 3,000 questions from structure-disjoint scenes.
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- Source: https://arxiv.org/abs/2609.32456v1
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Seeing Parts, Reasoning about Worlds: Visual Inference under Partial Observation Wei Wang, Wenqiao Zhang ∗ , Yutong Lin, Jun Xiao, Yueting Zhuang College of Computer Science and Technology, Zhejiang University ∗ Corresponding author. World modeling under partial observation requires reasoning about the complete worlds that remain compatible with limited visual evidence. Occluded objects and unseen regions can leave several world states possible; additional views can exclude alternatives and strengthen the conclusions supported by the observations. We introduce WorldScope to study this process through possible-world semantics, evidence-grounded data, and learned visual representations. WorldScope-1.2M provides 1.2 million English question–answer pairs spanning eight world properties, ten task interfaces, and three obser- vation protocols. Its answers encode confirmed facts, supported bounds, and unresolved possibilities. Complementary supervision comprises 4,800 certified counterworld groups with equivalent base obser- vations and different hidden object configurations and query answers. These groups provide physical witnesses of ambiguity and training-only labels for world compatibility and view-induced exclusions. We propose WorldFlow, which composes cross-view entity evidence and support-surface coverage into an image-subset evidence lattice. Counterworld compatibility and transition objectives train subset representations to reflect how new observations constrain possible worlds. A shared answer generator uses these representations to predict the strongest supported conclusion. WorldScope-Bench evalu- ates claim judgments and evidence-dependent conclusions as views are selected, combined, removed, or ordered. On its 5,000-question test set, WorldFlow reaches 64.34% exact accuracy, improving over the same backbone trained on QA alone by 24.88 percentage points. It retains 50.43% accuracy on the 3,000 questions from structure-disjoint scenes. Keywords: world modeling, possible-world reasoning, partial observation, visual inference, visual identifiability 1 Introduction Visual observations reveal only part of a world; occluded regions and content outside the field of view remain unknown. Consider a tabletop image containing two visible objects of the same category while another object hides part of the surface. The evidence supports “at least two,” but it does not justify “exactly two,” because the hidden region may contain another instance. A partially observed scene can therefore admit multiple complete configurations consistent with the visible evidence. We call each such configuration a possible world. Different possible worlds may yield different exact answers while sharing weaker conclusions. If the total count could be two, three, or four, for example, the model can still conclude that it is at least two. Visual reasoning under partial observation thus requires extracting the strongest conclusion shared by the possible worlds compatible with the current evidence, including exact values, bounds, existence claims, and “cannot be determined” when the requested conclusion remains unresolved. Additional observations further restrict the compatible possible worlds. A complementary image can expose a previously hidden region, exclude some configurations, and tighten a weak conclusion into an exact answer. A repeated or irrelevant image may increase the visual input without excluding any new world. Single- image judgment and multi-image reasoning consequently share the same central problem: determine which possible worlds remain compatible with the current observations and summarize the strongest conclusion they jointly support. Figure 1 summarizes how WorldScope connects this evidence semantics to data construction, evaluation, and model design. Modern multimodal large language models (MLLMs) can process interleaved images and text and increasingly support cross-image comparison and spatial reasoning [1–3]. Assessing how strongly these observations 1 arXiv:2609.32456v1 [cs.CV] 26 Sep 2026 Seeing parts, reasoning about worlds Partial observation · certified counterworlds · evidence-grounded visual reasoning How much of the world can the current evidence prove? 1.2M train 5K test 3K unseen structures RGB + question1–5 views10 tasksBalanced testEvidence subsets Claim status single observation Are there fewer apples than oranges? Hidden surface remains. ? visible+hidden→bound cannot be determined Best counting view add each candidate Which view maximizes confirmed canned drinks? Answer: B BaseCandidate B Claim-resolving view close the blind spot Which view makes “at most one soap” determined? Answer: C BaseCandidate C Necessary vs. redundant views leave-one-out evidence Remove one image at a time: which evidence is indispensable? Necessary: CRedundant: A, B BaseA · redundantB · redundantC · necessary How many apples? exact answer unresolved POSSIBLE-WORLD SUPERVISION Same observation, different complete-world answers. Only conclusions shared across compatible worlds are licensed. 4,800 groups · training only same pixels W0 0 apples W1 1 apple W2 2 apples Overall accuracy (%) 0–100 SCALE Native Qwen15.06 Best external37.44 QA-only39.46 WorldFlow64.34 +24.88 points over QA-only Accuracy across 10 tasks (%) QA-onlyWorldFlow 100 50 0 Unseen:50.43vs. 35.60 QA-onlybest on all 10 tasks 5,000 balanced test questions Claim status Claim select. Strongest claim Resolve view Nec./ red. Shortest seq. Minimal set Best count First gain Max. answer WorldFlow results WorldScope WorldScope-1.2MWorldScope-BenchCertified Counterworlds Figure 1 Overview of WorldScope. Partial RGB observations may support a bound or leave a claim unresolved even when visible objects are recognized. WorldScope-1.2M and WorldScope-Bench cover eight world properties and ten task interfaces, while certified counterworlds expose equivalent observations with different hidden answers. The lower panels compare overall and task-wise accuracy: WorldFlow reaches 64.34%, a 24.88-point gain over the same-backbone QA-only baseline. support a conclusion about the whole world remains a separate requirement. Three limitations motivate our study. First, existingdataoverlookhiddenpossibilities. Visual question answering datasets typically ask about content directly visible in an image. Synthetic-scene datasets often use a property of the complete scene as the answer [4, 5]. Both generally provide one target answer without labeling which hidden configurations remain possible beyond the image or distinguishing exact answers, bounds, and unresolved conclusions. Supervision for reasoning about unobserved content therefore remains limited. Second, existing benchmarks do not test evidence boundaries. Visual entailment and multi-image benchmarks mainly compare predictions against preset answers under a fixed input, evaluating evidence judgment, cross- image comparison, or scene integration [6–8]. They do not systematically test whether a model keeps its conclusions within what the current evidence permits, or whether answers change correctly as observations are added, removed, and recombined. Evaluation under partial observation should test whether a model derives the strongest supported conclusion while retaining uncertainty about hidden content. Third, related research lacks a unified model of possible-world constraints. Multi-image models fuse continuous features, cross-view methods establish object correspondence, and occlusion or coverage methods characterize unseen regions. Reasoning under partial observation requires integrating these components to represent 2 repeated objects across images, regions that may still contain hidden objects, and the contraction of possible worlds as images are combined. We address these gaps with WorldScope, comprising two complementary sources of training supervision, the structure-disjoint WorldScope-Bench, and the WorldFlow model. Together, they support the study of which conclusions a model can reliably draw from partial observations. On the data side, WorldScope-1.2M provides 1.2 million English question–answer pairs over 20 HSSD struc- tures and 240 rendered scene instances, spanning eight world properties, ten task interfaces, and three obser- vation protocols [9]. Its questions and answers are derived jointly from the complete scene, visible objects, and uncovered regions, so they capture both confirmed facts and the bounds or uncertainty caused by unseen space. The 805,955 distinct reasoning instances use stable identities to prevent duplicate cross-view counting and dense, object-size-aware surface probes to determine whether an unseen region can contain the queried object. A complementary corpus contains 4,800 certified counterworld groups and 14,400 complete worlds. Worlds in a group share the same base observation but differ in hidden object configuration, directly labeling which worlds remain compatible and which are excluded by a new image. These worlds provide only visual compatibility supervision; all natural-language questions remain drawn from WorldScope-1.2M. On the evaluation side, WorldScope-Bench tests whether a model can derive the strongest conclusion justified by a partial observation. The model must use visible facts while preserving possibilities left by unseen regions, avoid overclaiming when evidence is insufficient, and update its answer when images are added, removed, or combined. Ten task interfaces cover single-image judgment, multi-image composition, and active disambiguation. The test set contains 5,000 balanced questions, including 3,000 from three HSSD structures entirely unseen during training. On the model side, WorldFlow follows the formation of evidence from individual images to image combinations. The visual features of each image are first compressed into 64 per-image evidence vectors. For every non- empty image subset, a shared entity fusion module uses 16 slots to merge repeated physical objects across images, while a coverage branch estimates a 4× 4 map of the support-surface regions observed by the subset. A subset encoder then compresses the entity slots, coverage estimate, and member-image evidence into four subset evidence vectors. The language model receives these vectors together with the original images and question to generate one answer. Entity and coverage supervision distinguish confirmed content from regions that may still hide an object, while certified counterworld supervision teaches the subset vectors how the range of possible worlds contracts as observations are added. Our main contributions are: • We introduce the WorldScope data suite, in which WorldScope-1.2M provides 1.2 million natural-language question–answer pairs and 4,800 certified counterworld groups provide training-only world-compatibility supervision without creating a second language-question distribution. • We build WorldScope-Bench, which tests whether models derive the strongest conclusion supported only by a partial observation and update that conclusion correctly as visual evidence is acquired, removed, and composed. • We propose WorldFlow, which forms a hierarchical evidence representation through per-image compres- sion, cross-view entity fusion, coverage prediction, and image-subset encoding, and uses certified counter- worlds to learn how observations contract the range of possible worlds. 2 Related Work 2.1 Multi-Image Reasoning Early general-purpose vision–language models connected frozen or pretrained visual encoders to large lan- guage models through resampling, query, or projection modules [10–13]. Qwen-VL, Qwen2-VL, and Qwen3- VL extended this interface to localization, dynamic visual resolution, videos, and long interleaved multimodal contexts [3, 14, 15]. LLaVA-NeXT-Interleave and MANTIS showed that dedicated interleaved instruction data can substantially improve comparison, reference, and reasoning across multiple images [1, 2]. These 3 systems establish a general multi-image interface and evaluate whether a model can combine content across images. Their supervision is usually attached to the final answer after all images have been encoded into one context. A correct answer therefore need not reveal which image supplied new evidence, whether a smaller subset was already sufficient, or whether the answer claims more than the visible evidence supports. Spatially focused MLLMs add metric, region, depth, or three-dimensional supervision. SpatialVLM auto- matically generates large-scale spatial QA from detected objects and estimated depth, whereas SpatialRGPT grounds spatial questions in designated regions and metric depth features [16, 17]. M-Spatial studies open-category metric relations and the contribution of depth and multi-view input [18]. Video-3D LLM and MV-ScanQA integrate multiple scene views using position-aware video features, coverage-driven sampling, or 2D–3D–language pretraining [8, 19]. Their central target is recovering spatial relations or three-dimensional scene information from accumulated observations. Evaluating evidence sufficiency further requires tracking the informative, repeated, or irrelevant contribution of each view, together with the answers still permitted by blind regions. 2.2 Cross-View Identity Stable object identity is a prerequisite for combining views. Classical matching methods establish local correspondences through graph attention, optimal transport, or detector-free dense matching [20, 21]. Re- identification and multi-camera tracking learn identity features across viewpoint and camera changes [22–24]. MessyTable isolates instance association in cluttered tabletop scenes, where repeated categories and occlusion make appearance-only matching unreliable [25]. Recent MLLM-oriented work reaches beyond geometric keypoints: MMVM evaluates visual correspondence in general multimodal models and trains object-level instance tokens, while ObjectRelator transfers object conditions and masks between egocentric and exocentric views [26, 27]. These methods can establish that two observations refer to the same physical entity, which is essential for avoiding duplicate counts. Correspondence alone does not determine whether an additional entity can remain outside the matched regions or behind an occluder. Object-centric representation learning provides a complementary mechanism for organizing repeated evidence. MONet, IODINE, and Slot Attention decompose a scene into a set of object-like components through iterative inference or competitive attention [28–30]. Perceiver uses learned latent queries to compress large inputs, while DETR and Mask2Former formulate detection and segmentation as query-based set prediction [31– 33]. These models motivate compact entity sets. Their components are normally formed within one image or optimized for detection, segmentation, or scene decomposition, leaving the evidential relation among arbitrary image subsets unspecified. Cross-view identity identifies repeated visual evidence. Reasoning about a claim additionally requires repre- senting unobserved regions and the hidden configurations they may still contain. 2.3 Incomplete Evidence Natural language inference formalizes whether a premise entails, contradicts, or remains neutral toward a hypothesis [34, 35]. Visual entailment transfers this three-way relation to image–text pairs, and NLVR2 tests whether paired photographs satisfy compositional language descriptions [6, 7]. Diagnostic datasets such as CLEVR and GQA further expose compositional and relational reasoning failures through executable scene questions or structured scene graphs [5, 36]. These benchmarks evaluate a fixed image–text input or derive answers from a specified scene, without certifying which alternative complete scenes remain consistent with the visible evidence. They consequently leave open how to evaluate the strongest consequence shared by all complete worlds compatible with a partial observation, including exact answers, bounds, and unresolved results. Occlusion research also distinguishes visible evidence from the unobserved extent of an object. Amodal instance segmentation predicts complete object shapes from visible fragments, with KINS providing large- scale annotations for occluded driving scenes [37, 38]. For partial-observation reasoning, a further question is whether an unobserved support region can contain an additional instance and thereby change an existence or count claim. 4 An unresolved proposition also differs from unknown-category recognition and model abstention. Open-set recognition and open-world detection identify inputs or object categories outside a registered label space [39, 40]. Selective classifiers reject predictions to control expected risk at a chosen coverage level [41, 42]. Both address limitations of the predictor relative to its label space or confidence estimate. Their rejection behavior may change with recognition or calibration quality even when the available evidence is unchanged. A different problem arises when the categories and proposition are known but observationally equivalent complete worlds assign different answers. Existing abstention settings do not separate this evidence-induced indeterminacy from model uncertainty, nor do they require weaker conclusions, such as a valid lower bound, when an exact answer is unavailable. VQA research has repeatedly shown that models can exploit question and answer regularities instead of images [4, 43]. Causal counterfactual VQA removes a language-only direct effect from model scores, while broader analyses describe such behavior as shortcut learning [44, 45]. Such counterfactuals diagnose language effects in a predictor. Certifying observational ambiguity requires physical examples of complete scenes with identical observations and different hidden contents and answers. Direct supervision of these underdetermined conclusions also needs to control scene, position, and language shortcuts. 2.4 Partial Observability Partially observable decision processes summarize unobserved state with a belief state and update it after each action and observation [46]. Learned world models compress observations into latent dynamics for future prediction and control [47, 48]. Both frameworks address sequential interaction: the former assumes action, transition, observation, and reward models, while the latter emphasizes future prediction or control. They do not directly address the evidential question of which conclusions already hold across all complete worlds compatible with a given set of visual observations. Active perception selects observations that reduce uncertainty or improve a task-specific estimate [49]. Learned active localization similarly frames visual information acquisition as a sequential policy, although within a single image [50]. These methods learn where to sense by executing actions in an image or environ- ment. They leave open whether a model can assess the evidential effect of already supplied observations or candidate images before an action is executed. 3 The WorldScope Dataset and Benchmark 3.1 Problem Formulation Let W denote a complete tabletop world and let S be a set of RGB observations of that world. Each observation exposes only part of the support surface and a subset of its objects. We study which conclusions S supports. A proposition q is determined true when every complete world compatible with S satisfies q, determined false when none does, and non-identifiable when both truth values remain possible. This semantics also admits conclusions more informative than a three-way label. An observation may establish a count lower bound, rule out categories, or confirm part of a regional distribution even when an exact answer remains unknown. Formally, let Ω(S) be the set of complete worlds consistent with all observations in S. The evidential status of q is status(q, S) = true,∀W ∈ Ω(S), q(W ) = 1, false,∀W ∈ Ω(S), q(W ) = 0, non-identifiable, otherwise. (1) Adding an observation can only contract this set: Ω(S ∪v) ⊆ Ω(S). A useful view therefore changes the conclusions shared by the compatible worlds; an irrelevant or repeated view can leave them unchanged. This relation provides one semantics for single images, unordered image sets, view removal, and sequential observation. We instantiate this problem along three observation protocols. Single view presents one partial observation, multi-view provides a set of observations to be composed, and active disambiguation presents a base ob- 5 Table 1 Base-query taxonomy and data composition. Base queryTargetTraining set Benchmark Presence and sets254.7K1,197 Existence / absenceConfirmed presence / exclusion22.6K178 Category compositionCategory-set membership30.9K214 Category inventoryConfirmed / possible categories201.2K805 Counting863.9K3,402 Exact countUnique count312.8K1,399 Count thresholds / boundsTightest supported count interval204.2K871 Scoped countCount within a specified region222.8K662 Count comparisonComparison between object counts124.0K470 Regional reasoning81.4K401 Regional distributionConfirmed / possible regions81.4K401 Total1.2M5,000 Table 2 Task-interface taxonomy and data composition. TaskOperationTraining set Benchmark Claim reasoning360K1,500 Claim statusEvaluate a claim from supplied views120K500 Claim selectionCompare candidate claims120K500 Strongest claimSelect the strongest supported claim120K500 Evidence operations480K2,000 Claim-resolving viewAdd a view to resolve a claim120K500 View necessity / redundancy Remove each view and assess its role120K500 Shortest disambiguating sequence Find the shortest resolving sequence120K500 Minimal sufficient view setCompare subsets for sufficient evidence120K500 Count acquisition240K1,000 Best counting viewAdd the view that maximizes confirmed count120K500 First count-gain viewFind the first view that increases the count120K500 Maximal answer120K500 Maximal identifiable answerReturn the strongest identifiable answer120K500 Total1.2M5,000 servation together with candidate views or view orders. Across the three protocols, the underlying world property is drawn from eight families: existence and absence, exact count, count thresholds and bounds, scoped count, count comparison, category composition, regional distribution, and category inventory. Propo- sitions combine existence, count, comparison, set, and regional predicates with Boolean connectives. Table 1 summarizes these eight base query families. They specify what property is queried. The ten interfaces in Table 2 specify how the available evidence is used or changed. The two axes are composed whenever their semantics are compatible: exact count, for example, can underlie a claim judgment, a resolving-view question, a minimum sufficient set, or a direct request for the tightest count interval. Thus the eight base queries and ten task interfaces are distinct, composable levels of the data design. 3.2 Scene and Observation Construction We construct the main visual pool from 20 HSSD structural scenes spanning ten indoor and semi-outdoor scene families [9]. For each structure, we generate sparse, medium, and dense tabletop layouts containing 3, 6, and 9 target objects, respectively, with four layout instances per density. This produces 240 scene 6 instances. Target objects are sampled from 38 everyday tabletop categories and 153 object assets derived from OmniObject3D [51]. The support surfaces and surrounding rooms remain native to HSSD, with 19 support assets represented in the final pool. We add one to three larger contextual objects drawn from 18 categories and 29 assets, including speakers, plants, monitors, and storage containers. These objects create occluded regions within the image. Category, asset, placement region, scene family, density, and occluder configuration are varied jointly so that hidden space is not tied to a single visual pattern. The construction follows a render-once, reason-many pipeline that separates physical scene generation from question production. A support surface is selected in each HSSD room and populated with category- appropriate objects at collision-free poses. Layout generation checks support contact, boundary containment, inter-object penetration, plausible scale, and visibility from at least part of the camera set. Occluders are then positioned to create locally hidden objects and contiguous hidden surface regions. Camera placement is conditioned on the support geometry and surrounding room, preserving variation in distance, elevation, perspective, and background while keeping the target surface legible. The resulting physical world is rendered once. Offline evidence extraction then measures per-view object visibility, cross-view identity, and category- conditioned hidden space. Executing query programs on different image subsets yields structured answers, after which task interfaces and language realizations are applied. This order keeps rendering, evidence, reasoning, and language as separately controlled stages. Each scene is rendered from nine stable camera candidates using Habitat-Sim [52]. RGB observations have resolution 1024× 768 and are downsampled from 2× supersampled renders. Each question uses the subset of camera views required by its observation protocol. For regional questions, a camera-matched reference image of the empty support surface marks front-left, front-right, back-left, and back-right. It defines the coordinate convention but contributes no object or count evidence. Rendering also produces aligned depth, semantic labels, and instance identities. These privileged modalities are used offline to certify visibility, physical coverage, and cross-view correspondence. They are never part of a benchmark question or model input. This separation provides exact evidence labels from the simulator while preserving the intended inference setting, in which a model receives only RGB images, their public roles, and the question. It also allows the same observation bank to support single-view, multi-view, set, removal, and sequential tasks without changing the underlying scene or its object identities. The held-out pool uses five additional HSSD structures from five scene families absent from the main pool. Each structure contains two layout instances at each density. Two structures form an unseen-structure validation pool and three form the final test set. The partition is chosen to minimize divergence in task and answer distributions without using model predictions. Structural scene identity is the split unit, preventing the same room geometry from appearing in training and structure-disjoint evaluation. 3.3 Evidence-Grounded Answer Generation Confirmed entities. Every physical object retains one identity across cameras. An object is confirmed only when its visible evidence exceeds the countability criterion; small fragments do not become positive count evidence. For category c, region r, and observation set S, let V c (S, r) be the union of confirmed object identities in r over all views in S. The confirmed lower bound is L c (S, r) =|V c (S, r)|.(2) Taking a union over identities is essential: the same object visible in three images contributes once, whereas an object first exposed by a new image increases the bound by one. Countability is computed from each object’s instance projection in each view. This separates three cases that would otherwise be conflated: an object absent from the world, an object present but fully hidden, and an object whose visible fragment is too weak to support reliable counting. Once an object becomes countable in any selected view, its stable identity enters V c (S, r) and remains confirmed for every superset of S. Object-size-aware hidden space. Absence and exact counts require more than enumerating visible objects. We densely sample the support surface and determine which locations are exposed by the selected observations 7 using aligned depth and semantic renderings. A remaining blind region is considered relevant to category c only if an object with that category’s physical dimensions can be placed there without leaving the support surface or intersecting known objects. The placement test uses a stable hidden neighborhood for small objects and permits a partially visible feasible pose for large objects, reflecting that a small isolated gap should not license an arbitrary hidden object while a large occluded volume can still conceal most of a large object. Visibility is checked jointly across the selected observations. Consequently, complementary views remove hidden placements monotonically, while repeated views do not create spurious count evidence. Purely out- of-frame blind regions are retained when physically necessary but are downweighted in question construction in favor of occluder-caused blind regions. Surface sampling adapts to the physical dimensions of the selected support. Fine placement candidates are evaluated over the actual support shape and several object orientations. The test combines support boundaries, the footprints of visible objects, and joint visibility across all observations. It checks whether a physically valid placement remains consistent with all supplied views. Coverage is category-dependent: a narrow blind strip may remain open for a pen while being closed for a laptop. The same observation set can consequently establish an exact count for one category and only a lower bound for another. Let H c (S, r) indicate whether any feasible placement for another instance of c remains. The observation- supported count interval is I c (S, r) = [L c (S, r), U c (S, r)], U c (S, r) = ( L c (S, r), H c (S, r) = 0, ∞,H c (S, r) = 1. (3) The infinity symbol denotes an open upper bound under the benchmark evidence semantics; the genera- tor never substitutes the privileged complete-scene count when unseen space remains. Existence, equality, threshold, comparison, set, and regional propositions are evaluated over these intervals. A proposition is determined true if all compatible values satisfy it, determined false if no compatible value does, and non- identifiable otherwise. Maximal-identifiable-answer examples report the maximal information licensed by the same evidence, including count intervals, confirmed and excluded categories, and regions where an object is confirmed or still possible. Observation operations. Gold answers for view operations are obtained by executing the same evidence calculation on the relevant image subsets. A resolving view changes a claim from non-identifiable to either true or false. A view is necessary when deleting it from the full set prevents the current judgment, and redundant otherwise. Minimal sets and shortest sequences are found by comparing all admissible subsets or cumulative prefixes. Count-view tasks compare the union of confirmed identities before and after each addition. Thus all ten task interfaces share the same evidence semantics. These computations form an evidence lattice over image subsets. Nodes store the strongest conclusion supported by one subset; edges correspond to adding or removing one observation. Set questions compare nodes at the same level, while sequence questions follow paths through the lattice and locate the first evidential change. Because every node is recomputed from the same entity and hidden-space semantics, the answer for a set is invariant to the order in which its images are presented. The lattice also supplies hard negative examples: some views reveal additional pixels but no relevant entity or hidden region, so their correct evidential effect is explicitly null. 3.4 WorldScope-1.2M From the executable scene evidence, we construct 805,955 distinct reasoning instances and realize them as 1.2 million English question–answer pairs over 4,281 unique RGB observations. Each of the ten tasks contributes exactly 120,000 questions. The eight base query families and ten task interfaces play different roles in this count: a base query selects the property to infer, whereas a task interface determines how an observation set is inspected, expanded, reduced, or ordered. The natural-language question is generated only after its structured reasoning instance and answer have been fixed. 8 Outer ring: WorldScope-1.2M | Inner ring: WorldScope-Bench Task interfaces Maximal answer Set 120.0K | 10.0% Bench 500 | 10.0% Count acquisition Set 240.0K | 20.0% Bench 1,000 | 20.0% Claim reasoning Set 360.0K | 30.0% Bench 1,500 | 30.0% Evidence operations Set 480.0K | 40.0% Bench 2,000 | 40.0% Base-query families Regional distribution Set 81.4K | 6.8% Bench 401 | 8.0% Count comparison Set 124.0K | 10.3% Bench 470 | 9.4% Category inventory Set 201.2K | 16.8% Bench 805 | 16.1% Thresholds / bounds Set 204.2K | 17.0% Bench 871 | 17.4% Category composition Set 30.9K | 2.6% Bench 214 | 4.3% Existence / absence Set 22.6K | 1.9% Bench 178 | 3.6% Exact count Set 312.8K | 26.1% Bench 1,399 | 28.0% Scoped count Set 222.8K | 18.6% Bench 662 | 13.2% (a) Training and benchmark composition. Shared base observation 3 compatible worlds Same RGB; queried region hidden. W0: 0 bottlesW1: 1 bottleW2: 2 bottles 050100 Share of observation states (%) Base +1 reveal +2 reveals +3 reveals +4 reveals 3 worlds 1 world 92.5% Corpus-wide contraction 1 world2 worlds3 worlds Query: how many bottles are on the selected support surface? (b) Certified counterworlds and evidence-induced contraction. Figure 2 Data composition and certified counterworld supervision. (a) The two nested donut charts compare four task- interface groups (left) and eight base-query families (right). Outer rings denote WorldScope-1.2M; inner rings denote WorldScope-Bench; callouts report counts and proportions. (b) A shared base observation admits three certified completions with zero, one, or two bottles; a reveal view exposes their differences. The chart summarizes compatible- set contraction over 446,400 image-subset states from 4,800 groups. These completions certify ambiguity but do not exhaust all physically possible worlds. Figure 2a compares the training and benchmark composition along both axes of the data design. Candidate generation begins from the full executable lattice of each scene. We enumerate valid propositions from the eight base query families, evaluate them on single views and admissible multi-view subsets, and derive task records only when the requested contrast is well-defined. Resolving-view examples, for instance, require an unresolved base observation and at least one candidate that reaches a definite truth value; shortest- sequence examples require a resolving prefix whose length can be compared across candidate orders. This eligibility filtering prevents questions whose answer is determined by a template convention rather than by visual evidence. The structured answer is computed before the same reasoning instance is verbalized as a question. 9 The final sample is selected from a substantially larger candidate pool. Sampling is stratified over task, base query, truth status or answer-set shape, scene structure, density, category, number of supplied views, and the location of the informative view. Per-scene caps prevent a small number of productive layouts from dominating the corpus, and rare-category reweighting retains the long tail of the object inventory. Option labels are assigned after the evidential answer is fixed, and image display order is independently permuted. For selection and sequence tasks, positive-set cardinality and answer position are balanced separately. These steps weaken question-only, camera-only, fixed-position, and generation-order correlations without changing the underlying evidence semantics. The resulting density distribution contains 318,593 sparse, 393,559 medium, and 487,848 dense questions. 3.5 Certified Counterworld Supervision We complement the interval-based language targets with visual examples of observational ambiguity. We construct 4,800 training-only counterworld groups over all 20 main structures and all 240 scene instances. Candidate groups start from a common visible scene and a support region hidden from a chosen base camera. We instantiate three physically valid completions by changing the category-consistent hidden objects and their collision-free poses while preserving everything observable from the base camera. Each completion is then rendered from the shared base camera and five reveal cameras. The base observations are accepted only when the three completions agree in RGB, depth, and semantic renderings, which rules out imperceptible color differences as well as geometric or categorical leakage. At least one reveal view must make some completion distinguishable, and the three complete worlds must yield at least two answers to the associated query. Among the accepted groups, 3,491 contain two distinct completion answers and 1,309 contain three. Thus every shared base image has a concrete pair of physically rendered worlds that agree on the observation but disagree on the answer. We next compare each of the six observations rendered from every completion against each of the three complete-world hypotheses, producing 259,200 view–world compatibility judgments. These atomic judgments are composed over every admissible reveal subset: a completion remains compatible only when every supplied observation is consistent with it. For each subset we record the compatible completions, and for every edge formed by adding one view we record which completions are eliminated. This produces 446,400 image-subset compatibility states and 1,080,000 one-view transitions from 14,400 complete worlds. Uninformative edges preserve the compatible set; revealing edges contract it. Figure 2b connects this supervision at the instance and corpus levels. In the illustrated group, the shared observation hides the queried region, while a reveal view separates three physically valid completions with different bottle counts. Across all groups, the base observation retains all three certified worlds, whereas one reveal view leaves a single compatible world for 92.5% of the resulting observation states. The three completions are concrete counterexamples rather than an exhaustive enumeration of all possi- ble worlds. Disagreement between two compatible completions certifies non-identifiability, while agreement among the sampled completions alone is not treated as proof that all possible worlds agree. The 4,800 groups are selected with source-frequency weighting over structure, scene, density, category, camera, placement re- gion, and object asset. They cover all 38 target categories and all 153 target assets and broadly follow the visual distribution of WorldScope-1.2M. The counterworld corpus contributes no synthetic propositions or question–answer pairs. All language supervision continues to come from WorldScope-1.2M, so the auxiliary world supervision does not introduce a second question distribution. 3.6 WorldScope-Bench WorldScope-Bench is generated with the same evidence executor, eight base query families, ten task interfaces, and three observation protocols as WorldScope-1.2M, but from five additional HSSD structures belonging to scene families absent from the training pool. Candidate construction first enumerates all valid propositions and observation operations in these scenes, then keeps only items whose answer is visually grounded and whose requested contrast exists. This matters for set and sequence tasks: an image set must have a genuine sufficient subset, and a proposed order must contain a measurable first resolving step. Questions that can only be answered by a template convention are excluded. 10 The five structures are partitioned at the structure level. We evaluate all ten choices of two development and three test structures and choose the split with the smallest combined Jensen–Shannon divergence in task and answer distributions. No model prediction is used in this choice. Development and test sets also contain questions sampled from the 20 main structures, allowing performance under familiar geometry to be separated from structural transfer. For every task, development contains 200 seen-structure and 200 unseen-structure questions, giving 4,000 questions in total. Test contains 200 seen-structure and 300 unseen- structure questions per task, giving 5,000 questions in total. The 3,000 unseen test questions come from 18 scene instances in the three structure-disjoint test structures. Balanced task quotas make both the task average and the overall question accuracy directly interpretable. Official scoring uses structured answers; optional free-form explanations do not affect the score. We compute exact accuracy separately for every task and macro-average the ten values. Since all tasks have equal quotas, this macro average equals overall question accuracy. Single-choice and status tasks use exact match. Multi- answer proposition and view tasks also report set F1, while exact credit requires the complete set with neither missing nor spurious choices. Necessity/redundancy requires both partitions to be correct. A shortest- sequence prediction is accepted when it matches any gold sequence attaining the minimum resolving-prefix length. For maximal identifiable answers, the lower bound, upper bound, exactness flag, category sets, and region sets are scored as components, with exact credit granted only when every applicable component is correct. The metric therefore tests whether a model returns the strongest claim licensed by the supplied observations and tracks how that claim changes under view addition, removal, grouping, and order. 3.7 Quality Control and Human Review Quality control is applied at the scene, evidence, and question levels. Automatic checks reject invalid support placement, object penetration, implausible scale, corrupted rendering, inconsistent cross-view identity, and any evidence transition that violates monotonicity. We manually review every rendered scene for physical plausibility, recognizable assets, meaningful occlusion, and usable camera framing, removing or repairing scenes that fail these criteria. For WorldScope-1.2M, we draw a stratified random sample of 12,000 training questions across tasks, base queries, answer forms, densities, and scene families. Each sample is checked for image–question correspon- dence, the sufficiency of visible evidence, language clarity, and the structured gold answer. Every candidate question for WorldScope-Bench is then reviewed individually. We remove items with physically implausible hidden placements, ambiguous visual evidence, confusing wording, or an incorrect answer contract. After this full benchmark review, 97.2% of the candidates are retained in the released evaluation pool. 4 Methodology 4.1 Overview We propose WorldFlow, an evidence adapter for visual reasoning under partial observation. WorldFlow represents every image subset from two complementary sides. Observed evidence summarizes the entities that the selected images jointly confirm. Latent evidence summarizes the physical support regions and complete-world configurations that those images have not yet ruled out. The first side establishes visible facts and count lower bounds; the second prevents these facts from being mistaken for a complete description of the world. For a sample with n≤ 5 images, Qwen’s vision encoder runs once on each RGB image. WorldFlow compresses the resulting patch features into 64 observation tokens and enumerates the 2 n − 1 non-empty image subsets. For every subset, 16 cross-view entity slots form its observed evidence, while a 4× 4 support-surface coverage map forms its latent evidence. A shared encoder combines both sides with the selected image features and produces four subset tokens. All subset representations together form an evidence lattice: each node corresponds to one image set, and adjacent nodes differ by one observation. The subset tokens are projected into Qwen’s language space and inserted alongside the original image tokens and question. This information flow can be summarized as observe, compose, constrain, and conclude: encode 11 OBSERVATION ENCODINGTWO-SIDED EVIDENCEEVIDENCE LATTICEANSWER GENERATION Training only View 1 View 2 View 3 Qwen Vision Encoder Shared across views Patch tokens View Resampler 64 tokens / view Image-label embeddings Observed Evidence Cross-view Entity Fusion Across views 16 entity slots Confirmed entities Latent Evidence Coverage Prediction 1 2 3 4 x 4 support grid Covered Unresolved C_S = 1 - product (1 - C_i) For every non-empty image subset S Entity slots 16 x 1024 Coverage + position 16 cells Selected-view summaries 1 vector / view Shared Subset Encoder 4 state tokens / subset H_S 4 x 1024 Image-subset relationships 123 1,21,32,3 1,2,3 3-view illustration; up to 31 subsets for n <= 5 Projection to language space 1024 to 4096 Input sequence to LM Image tokens (native) Subset tokens (all subsets) Public image labels View 1 View 2 View 3 Using View 1 only, how many bottles are on the table? Qwen3.5-9B Language Model + LoRA Autoregressive answer Undetermined. Native visual path A. Evidence Grounding Instance targetsSupport coverageSubset targets V1 V2 V3 Union target Covered Unresolved 1 1,2 1,2,3 Shared auxiliary heads Counts / bounds Claim status Evidence losses B. Counterworld Supervision Same base observation; different hidden worlds W1 0 bottles W2 1 bottle W3 2 bottles Complete-world RGB views Reveal-view thumbnails shown above W3 is the observed completion in this example. EMA Teacher World embeddings W1 W2 W3 No gradient to teacher Compatibility Head Certified compatibility targets W1W2W3 Base 1 Base + view 1,2 Compatibility + transition losses Student subset states + EMA world embeddings Detached entity, coverage and view inputs Forward data flowTraining supervisionTeacher / embeddingSubset inclusion Figure 3 WorldFlow architecture. Each RGB view is compressed by the shared Qwen vision encoder and resampler. Observed entity slots and latent support coverage are composed for every non-empty image subset, whose four state tokens form an evidence lattice before projection into Qwen’s language space. The original image tokens remain in the multimodal sequence. The EMA teacher, complete-world embeddings, and compatibility head are used only for training; the coverage grids, token arrays, and counterworld thumbnails are schematic architectural illustrations rather than checkpoint outputs. each observation, compose its two-sided evidence, represent how an image set constrains the world, and generate the strongest conclusion licensed by those constraints. These terms describe successive operations within a single forward computation. The same representation serves every task interface; the model receives no task identifier and uses no task-specific answer head. 4.2 Observation Encoding Let X i ∈R P i ×4096 denote the Qwen patch features of image i, and let M i mark valid patches. A learned projection maps X i into a 1024-dimensional evidence space. A resampler with 64 learned queries then reads the projected patches through multi-head attention: E i = LN (Q + MHA(Q, X i W x , X i W x ; M i )) ,E i ∈R 64×1024 .(4) The public image label is encoded with Qwen’s input embedding, projected to the same space, and added to every token in E i . It preserves the association between images and roles such as base view or candidate view, but contains no task number or teacher annotation. The same resampler is used for every image and task. 4.3 Observed Evidence For subset S, the observed-evidence module reads only the 64-token sequences of its member images. Sixteen learned slot queries jointly attend to their concatenation, followed by a residual feed-forward block: 12 Z S = LN (MHA(Q e , E S , E S ) + Q e ) ,Z S ∈R 16×1024 .(5) Each slot denotes one stable entity candidate across the selected views. A slot is paired with every member- view summary to predict its instance mask on the Qwen patch grid, countability logit, and normalized box in that view; the slot itself also predicts an occupancy logit. The mask query combines the shared slot with a view summary and scores it against projected raw patch features using normalized dot products. One entity can therefore keep a shared subset representation while changing location and appearance across cameras. Each slot also produces an open-vocabulary category embedding. Slot indices are not treated as permanent object IDs. For each subset, Hungarian matching assigns slots to stable-object targets jointly over all member views. Its cost combines mask Dice and binary cross-entropy, countability, box L1, and category-embedding distance. A stable object is matched to one slot, so its repeated appearance does not increase the confirmed count. For nested subsets, a consistency loss brings the matched representations of already-countable objects closer. An object first revealed by a new image is free to occupy a previously empty slot. The slots and their countability predictions consequently describe the distinct entities confirmed by the current observations and support a count lower bound. They cannot, by themselves, exclude an additional object in an unseen region. 4.4 Latent Evidence The latent-evidence branch summarizes the part of the support surface that remains unresolved. It mean- pools the 64 tokens of each image and predicts 16 sigmoid values, one for each cell of a 4× 4 grid. Coverage for subset S is composed by a probabilistic union: C S [x] = 1− Y i∈S (1− C i [x]),x∈1, . . . , 16.(6) A second head maps C S to a coverage-complete logit. The grid is not an image-space visibility map. It estimates how much of the physical support surface has been ruled out as unobserved placement space. Its dense, category-neutral target is generated offline from the support surface, and cells with no legal support locations are excluded by a validity mask. The category- and size-dependent placement tests used by the data executor provide the physical basis for this supervision, while depth, semantic masks, legal sites, and stable IDs never enter the model input. Coverage gives a local description of unexcluded surface space. During training, certified counterworlds add a global description of what remains possible: they show which distinct complete configurations are still compatible with the current RGB observations. This global signal shapes the subset representation above the local coverage branch. Counterworld representations are used only by the learning objective described in Section 4.7; they are not available during inference. 4.5 Evidence Lattice WorldFlow combines the observed and latent sides separately for every non-empty image subset. The subset encoder concatenates 16 entity slots, 16 projected coverage cells with learned grid-position embeddings, and one projected image summary for every selected view. Four learned queries, conditioned on the mean summary of the subset members, attend to this source: T S = [Z S ; G S ; R S ], A S = MHA(Q s + g(S), T S , T S ), H S = LN(A S + Q s + g(S)), H S ∈R 4×1024 .(7) Here G S denotes the positional coverage representation, R S the selected image summaries, and g(S) the projected member summary. The four tokens in H S summarize the evidence available from S. They are not four possible-world slots and do not enumerate complete scenes. Their mean feeds shared auxiliary heads 13 for confirmed object count (17 classes, 0–16), count lower bound (17 classes), finite upper bound (binary), and claim status (determined true, determined false, or non-identifiable). For these auxiliary predictions, a mean-pooled embedding of the natural-language question is projected into the same space and added to the pooled state. All lattice nodes use the same encoders and differ only in their selected images. A single-view or ordinary multi-view question reads the corresponding node; view addition and removal compare nodes connected by one observation; set questions compare several nodes; and sequence questions follow cumulative subsets through the lattice. Thus the ten task interfaces operate on one shared representation. 4.6 Answer Generation The wrapper builds Qwen’s ordinary multimodal chat sequence by interleaving each public image label with its RGB image and appending the original question. For n images, it additionally places (2 n − 1) × 4 reserved state placeholders between the images and question. Each H S is projected from 1024 to Qwen’s 4096-dimensional language space and written into its corresponding positions. Qwen’s native image features are inserted in parallel through the standard visual path, after which the multimodal position indices are recomputed. The language model therefore receives both the subset tokens and the original image features. All tasks use Qwen’s shared autoregressive output layer and retain their native answer strings. At inference, deterministic generation receives only the RGB images, public image labels, and question. The complete- world teacher, compatibility head, answer, scene metadata, and privileged sensor arrays are absent. 4.7 Learning Training follows a three-stage curriculum before the final model is used for evaluation. The first stage runs 10,000 scene-supervision updates. It trains the WorldFlow evidence modules with entity, coverage, and image- subset targets while the Qwen backbone remains frozen. This stage grounds the representation in observable entities, countable visibility, and uncovered support regions. The second stage runs 4,000 world-compatibility updates from the first-stage checkpoint. The entity and coverage branches are kept fixed; the subset-state encoder and the training-only compatibility head learn from certified complete worlds and from the worlds eliminated by an additional view. The teacher encoder is updated as an exponential moving average during this stage. This separates the learning of global observation compatibility from the grounding of local visual evidence. The third stage runs 4,000 joint updates. Each eight-update cycle contains six QA updates, one scene update, and one compatibility update. QA updates train Qwen with the answer-token causal loss and LoRA parameters, while the scene and compatibility updates retain the auxiliary evidence objectives. The later joint-training configuration may unfreeze the last four Qwen vision layers; this parameter change takes place within the joint stage. Across all stages, scene records provide entity, coverage, and subset targets; compatibility records provide certified complete worlds and one-view transitions; QA records provide the answer string. Visual batches always retain all single-image subsets and the full set, while intermediate subsets can be rotated to bound the cost of computing up to 31 nodes. Evidence grounding. Matched entity slots use mask binary cross-entropy and Dice losses, box L1 plus generalized-IoU loss, countability and occupancy binary cross-entropy, and open-vocabulary category align- ment. Their weights are 1.0, 1.0, 0.25, 0.5, 0.5, and 0.1, respectively; nested-subset slot consistency has weight 0.1. Coverage-grid and coverage-complete losses each have weight 0.5, with the complete loss multi- plied by three for incomplete targets. Confirmed-count and lower-bound cross-entropy have weights 0.5 and 0.25, finite-upper-bound binary cross-entropy has weight 0.25, and claim-status cross-entropy has weight 0.5. Together these targets connect the two-sided subset representation to confirmed entities, count bounds, and remaining support space. Possibility learning. For each certified group, a frozen teacher encodes the RGB views of every complete world. The teacher is an exponential-moving-average copy of the student WorldFlow core with momen- tum 0.995. Completions with more than five views are encoded in chunks and combined according to their 14 represented view counts. A training-only compatibility head compares a student subset state with each com- pletion using projected cosine similarity and learned pair functions over both representations, their absolute difference, and their elementwise product. The head predicts an independent compatibility logit for each world. The compatibility and in-group ranking losses each have weight 0.2. A transition loss with weight 0.2 requires a world eliminated by a new view to cross the incompatibility boundary; a preservation loss with weight 0.05 keeps scores stable for uninformative views. Entity and coverage inputs are detached within this branch, so counterworld supervision shapes the subset encoder without overwriting grounded perception. The teacher and compatibility head are discarded after training. Answer learning. The Qwen chat template appends the gold answer, while all labels before the assistant response are masked. The causal language loss is therefore computed only on answer tokens and has weight 1.0. Per-example losses are scaled by source-distribution weights, and full-model QA batches add claim-status supervision with coefficient 0.5. Qwen’s language backbone is adapted with LoRA, while the WorldFlow modules are trained directly. Scene and counterworld annotations supervise the auxiliary objectives. In both training and inference, the student receives RGB images, image labels, and the question. 5 Experiments 5.1 Experimental Setup Model and optimization. All WorldFlow variants and local ablations use the Qwen3.5-9B backbone [53], the same RGB images and public image labels, and the same question and answer contracts. The full curriculum comprises 10,000 scene-supervised updates, 4,000 counterworld-compatibility updates, and 4,000 joint updates. New modules use a learning rate of 2× 10 −4 in the first two stages and 5× 10 −5 in the joint stage. Rank-64 LoRA with scaling 128 and dropout 0.05 adapts the language model at 10 −5 . AdamW uses weight decay 0.1, 3% warmup followed by cosine decay, gradient clipping at 1.0, BF16, and gradient checkpointing. Effective batch sizes are 126 in the two warmup stages and 128 in joint training. The reported model keeps the Qwen vision backbone frozen and directly optimizes the WorldFlow modules. Splits and metrics. The development set has 4,000 questions: each task contributes 200 questions from seen structures and 200 from two unseen structures. It is used for checkpoint selection. The test set has 5,000 questions: each task contributes 200 seen-structure and 300 unseen-structure questions. The 3,000 unseen questions come from three HSSD structures excluded from training and model selection. We report exact accuracy for every task and its average over all ten tasks. Since every task contributes 500 test questions, macro accuracy and overall question accuracy are identical. Multi-answer tasks require the complete predicted set, and maximal identifiable answers require all applicable fields to be correct. Comparisons and ablations. The main comparison includes native Qwen3.5-9B, the same backbone trained only on WorldScope-1.2M QA, and general-purpose multimodal models. Models not trained on WorldScope use a permissive parser that accepts semantically equivalent answer formats. Architecture ablations add entity evidence, coverage evidence, the evidence lattice, and counterworld supervision cumulatively, then remove the entity or coverage branch from the full model. Curriculum ablations keep the full architecture and remove Stage 1, both warmups, auxiliary replay in Stage 3, or Stage 2. Every table reports absolute accuracy. 5.2 Main Comparison Table 3 reports the complete 5,000-question test. Full WorldFlow reaches 64.34%, compared with 39.46% for the same backbone trained on QA alone and 15.06% for native Qwen3.5-9B. The 24.88-point same-backbone gain is consistent across all ten tasks. WorldFlow also reaches 50.43% on the 3,000 structure-disjoint questions, compared with 35.60% for QA-only and 13.63% for the native backbone. 15 Status Select. Strongest Resolve Nec./red. Sequence Min. set Best First gain Max. 0 25 50 75 100 Exact accuracy (%) 64.3 39.5 ClaimsEvidence Counting Max. WorldFlow QA-only Native Best external 050100 Accuracy (%) Overall Status Selection Strongest Resolving Nec./red. Sequence Min. set Best count First gain Max. answer QA only w/o Entity + Entity w/o Coverage + Coverage Full No world sup. 050100 Accuracy (%) No Stage 1 No warmups QA-only Stage 3 No Stage 2 Full (a) Main comparison(b) Architecture(c) Curriculum Figure 4 Task-wise exact accuracy on the complete 5,000-question test set. (a) WorldFlow versus the same-backbone QA-only and native models and the strongest external result on each task. (b) Architecture variants and (c) curriculum variants. WorldFlow improves over QA-only on all ten tasks; the ablations localize the gains to evidence composition and its supervision. Figure 4(a) gives the task-wise comparison against QA-only, native Qwen3.5-9B, and the strongest external result obtained on each task. The largest same-backbone gains occur on shortest sequence (+38.60), resolving view (+31.40), minimal sufficient set (+30.40), and view necessity and redundancy (+25.60). These tasks compare image subsets or evidence transitions and therefore benefit most from the explicit subset representation. Native Qwen3.5-9B obtains 0% on necessity/redundancy because, under the 1,024-token generation budget, none of its responses could be parsed into both required sets. The task is unusually sensitive to answer formatting even with permissive parsing, so this value should not be read as an isolated measure of visual ability. 5.3 Architecture Ablations Table 4 separates the cumulative construction from the two leave-one-out tests. The +Entity and +Coverage variants remain close to the QA-only baseline on unseen structures (35.57% and 35.70%, compared with 35.60%), with only small finite-sample fluctuations because their outputs are not connected to answer gener- ation without the evidence lattice. The first substantial architectural gain appears when the lattice makes image subsets directly comparable, raising unseen accuracy to 45.97%; counterworld supervision then raises it further to 50.43%. Removing entity evidence from the full model costs 7.03 points; removing coverage costs 8.00. Figure 4(b) shows the corresponding task-wise results on the combined test set. The task pattern matches the intended division of labor. Removing entity evidence causes the largest unseen drop on first count gain (14.00 points), where duplicate instances must be merged across an observation sequence. Removing coverage has little effect on the two count-view tasks, but reduces strongest claim, resolving view, necessity/redundancy, and maximal identifiable answer by 10.67–12.00 points. The evidence lattice is most visible on resolving view, shortest sequence, and minimal sufficient set, each of which must compare several image subsets. Counterworld supervision adds a further 8.66 points on resolving view and 9.00 on minimal sufficient set beyond the lattice-only model. 16 Table 3 Main comparison on the balanced 5,000-question test set. Values are exact accuracy (%); models are sorted by overall accuracy. The best result in each column is shown in bold red. Task headings identify claim reasoning, evidence operations, count acquisition, and maximal answers. Model Claim status Claim select. Strongest claim Resolve view Necess./ red. Shortest seq. Minimal set Best count First gain Max. answerOverall WorldFlow (Qwen3.5-9B)62.0055.4073.6060.8053.6078.2043.2076.0085.0055.6064.34 Qwen3.5-9B a 41.4037.2053.2029.4028.0039.6012.8060.0065.4027.6039.46 Gemini 3.1 Pro40.4030.0054.8032.803.6038.4020.8055.2074.0024.4037.44 Gemini 3 Pro45.60 29.20 39.60 32.60 3.40 44.00 32.20 36.20 78.40 30.60 37.18 GPT-5.6-Luna33.00 25.60 44.20 23.20 7.00 47.00 18.20 69.40 72.40 30.20 37.02 GPT-5.6-Terra42.80 26.40 47.20 25.60 5.60 38.00 19.20 50.20 81.00 31.80 36.78 GPT-5.6-Sol33.20 23.80 28.00 35.40 4.80 49.20 14.80 50.00 68.00 28.40 33.56 Claude Opus 4.8 35.60 7.60 23.40 11.80 2.40 55.60 7.80 30.80 51.00 29.80 25.58 Claude Opus 530.80 9.80 30.80 8.802.60 55.20 8.00 30.60 40.60 26.20 24.34 Grok 4.545.20 12.20 54.20 12.00 3.80 23.20 2.60 11.60 41.00 33.60 23.94 Qwen3.7-Max45.40 22.80 18.60 23.80 6.00 34.80 12.60 12.00 44.80 11.00 23.18 Qwen3.8-Max46.40 17.60 24.00 14.20 6.40 38.20 4.20 12.40 53.00 12.00 22.84 Claude Opus 4.7 30.60 16.60 31.40 8.402.20 41.40 7.40 25.80 36.20 27.60 22.76 Kimi-K2.636.80 12.60 43.60 9.204.20 29.40 3.00 31.20 24.80 31.00 22.58 Qwen3.7-Plus45.80 22.40 19.40 14.00 5.40 35.00 15.20 11.20 39.60 10.00 21.80 Qwen3.6-Plus44.60 23.60 29.00 10.40 5.00 39.60 3.60 10.80 40.80 9.00 21.64 Qwen3-VL-8B31.20 8.40 31.00 26.00 5.20 15.40 3.20 10.40 50.00 34.00 21.48 Grok 4.629.60 7.80 21.60 10.00 4.40 39.20 2.80 16.60 46.20 29.40 20.76 Qwen3.5-9B b 40.208.2023.809.400.007.006.209.8020.0026.0015.06 a QA-only fine-tuning on WorldScope-1.2M. b Native backbone without WorldScope training. Gemini Pro rows use Preview checkpoints; Qwen3-VL-8B denotes the Instruct checkpoint. Task names follow Table 2; Necess./red. denotes view necessity and redundancy. 5.4 Training Curriculum Ablations Table 5 tests how the same architecture changes with its curriculum. Removing Stage 1 lowers unseen accuracy by 11.36 points; starting without either warmup lowers it by 14.23. Keeping both warmups but using only QA updates in Stage 3 is much closer to the full model, with a 2.90-point deficit. Removing only Stage 2 gives a similar 2.73-point deficit, showing that both warmups contribute while joint answer learning supplies most of the final task adaptation. Figure 4(c) shows that the curriculum changes are concentrated on evidence-composition tasks rather than uniformly distributed. The two warmup stages affect different tasks. Without Stage 1, unseen resolving view, shortest sequence, and minimal sufficient set fall by 23.66, 20.33, and 14.00 points, respectively, because the entity and coverage modules enter compatibility learning without grounded scene evidence. Removing Stage 2 has smaller losses concentrated on resolving view, necessity/redundancy, shortest sequence, and minimal sufficient set. Its 0.33– 0.34-point reversals on the two count-view tasks correspond to one additional correct example and do not form a consistent advantage. Likewise, QA-only Stage 3 exceeds the full model only on seen claim status; it remains lower on unseen claim status and on both overall splits. 5.5 Generalization and Remaining Difficulty The unseen-structure split remains substantially harder than the seen split: full WorldFlow obtains 50.43% versus 85.20%. On unseen structures it reaches 76.67% on first count gain, 65.33% on shortest sequence, and 61.33% on best count view. Minimal sufficient set (32.33%), necessity/redundancy (33.33%), and maximal identifiable answer (34.67%) remain the hardest tasks. These outputs require an exact set or several mutually consistent fields, so a single missing component makes the complete prediction incorrect. The consistent drops 17 Table 4 Architecture ablations on seen and unseen structures (accuracy, %). (a) Seen structures(b) Unseen structures Task QA only + Entity + Coverage + Lattice No Entity No Coverage Full model QA only + Entity + Coverage + Lattice No Entity No Coverage Full model Claim status 52.50 52.00 53.50 66.00 71.50 69.0083.00 34.00 33.67 33.33 43.33 39.67 37.3348.00 Claim select. 48.00 48.50 47.50 59.00 68.00 65.0080.50 30.00 30.33 30.67 36.67 34.67 31.3338.67 Strongest 58.00 57.00 58.50 77.00 87.00 84.5096.00 50.00 49.33 50.33 56.67 54.00 47.3358.67 Resolve view 28.50 29.00 27.50 48.00 61.00 59.5069.00 30.00 30.33 30.67 46.67 47.33 43.3355.33 Necess./red. 35.00 34.50 36.00 59.50 75.00 70.50 84.00 23.33 23.67 23.00 30.67 26.00 22.3333.33 Shortest seq. 39.00 40.00 38.50 65.00 88.50 87.0097.50 40.00 39.67 40.67 58.33 58.67 57.3365.33 Minimal set 17.00 16.50 18.00 36.00 49.50 46.0059.50 10.00 10.33 9.67 23.33 27.33 24.6732.33 Best count 67.50 68.00 67.00 90.00 85.00 97.50 98.00 55.00 54.67 55.33 60.67 55.67 60.6761.33 First gain 71.00 71.50 71.50 94.00 87.50 97.0097.50 61.67 62.00 61.00 73.33 62.67 76.0076.67 Max. answer 36.00 35.50 36.50 55.00 68.00 64.00 87.00 22.00 21.67 22.33 30.00 28.00 24.0034.67 Overall45.25 45.25 45.45 64.95 74.10 74.0085.2035.60 35.57 35.70 45.97 43.40 42.4350.43 Task labels are shortened from Table 2; Necess./red. denotes view necessity and redundancy. QA-only is the answer-training baseline. The three + columns add entity fusion, coverage prediction, and the image-subset lattice cumulatively. Full model also uses counterworld supervision; No Entity and No Coverage remove the respective branch. Table 5 Curriculum ablations on seen and unseen structures (accuracy, %). (a) Seen structures(b) Unseen structures Task No Stage 1 No warmups QA-only Stage 3 No Stage 2 Full model No Stage 1 No warmups QA-only Stage 3 No Stage 2 Full model Claim status57.00 53.50 84.00 81.5083.0034.67 34.33 45.33 46.6748.00 Claim select.50.00 49.00 75.50 78.00 80.5030.33 30.67 35.33 37.3338.67 Strongest claim 66.00 59.50 91.50 94.0096.0053.33 49.67 54.00 56.0058.67 Resolve view33.00 29.50 64.50 64.0069.0031.67 30.67 51.00 50.0055.33 Necess./red.37.50 36.00 79.50 79.0084.0027.33 24.00 30.33 28.6733.33 Shortest seq.45.00 40.00 93.50 92.0097.5045.00 41.00 62.67 60.6765.33 Minimal set25.00 17.50 56.50 55.0059.5018.33 10.67 29.33 28.0032.33 Best count82.50 70.00 95.50 98.5098.0056.67 55.67 60.67 61.6761.33 First gain82.50 72.50 97.00 98.0097.5068.33 63.33 75.33 77.0076.67 Max. answer45.00 36.00 82.00 84.0087.0025.00 22.00 31.33 31.0034.67 Overall52.3546.35 81.95 82.4085.2039.0736.20 47.53 47.7050.43 Stage 1 grounds scene evidence; Stage 2 learns counterworld compatibility; Stage 3 jointly trains answers and evidence. No warmups omits both preliminary stages. 18 OBSERVATIONS / CERTIFIED PROBES COVERAGE CERTIFIED WORLDS / COMPATIBILITY Apples · Base+ View 1 62%92% W 0 · n= 0 15.56 → 2.16 W 1 · n= 1 15.00 → -1.46 W 2 · n= 1 15.50 → -1.50 W 0 , W 1 , W 2 ⟶W 0 Laptops · Base+ View 1 73%96% W 0 · n= 1 5.81 → 1.12 W 1 · n= 2 6.25 → -5.09 W 2 · n= 2 6.28 → -5.53 W 0 , W 1 , W 2 ⟶W 0 Power strips Base+ View 1+ View 2 26%93% W 0 · n= 0 10.25 → 2.66 W 1 · n= 1 11.62 → -0.93 W 2 · n= 1 11.56 → -1.12 W 0 , W 1 , W 2 ⟶W 0 Figure 5 Internal evidence updates for three certified counterworld groups. Left: the base observation and one or two added views. Colored dots show certified support-surface probes hidden from the base (red) and exposed by the added views (green and blue); these annotations are used only to visualize the offline supervision. Middle: the model’s 4× 4 support-surface coverage prediction before and after adding the views, with the mean value below each grid. Right: the three certified complete worlds and the compatibility logit before → after the added evidence. Solid green retains the compatible world and dashed red marks worlds the model excludes.n is the true count of the queried category in each complete world. after removing coverage, the evidence lattice, or Stage 2 show that evidence composition and judgments of sufficiency remain key sources of error. 5.6 Qualitative Evidence Updates Figure 5 traces the internal evidence update for three certified groups from the selected full-model checkpoint. The colored points are physical support-surface probes from the offline certificate: red points are hidden in the base observation, while green and blue points mark portions revealed by added views. They visualize the target geometry and never enter the student input. The adjacent 4× 4 arrays are the model’s predicted coverage in canonical support-surface coordinates; they are not a partition of image pixels. Mean predicted coverage rises from 62% to 92% for apples, from 73% to 96% for laptops, and from 26% to 93% for power strips as the certified blind region is exposed. For this diagnostic only, we retain the training-time compatibility head and query it against the certified complete-world representations. Neither the head nor these privileged representations contributes to answer generation. Before the reveal, all three certified worlds have positive compatibility logits in every row. After the reveal, the logit of the observed completion remains positive (2.16, 1.12, and 2.66), while both alternatives cross below zero. The three rows also include a two-view and a three-view observation set. Their shared behavior shows that the subset state can accumulate complementary views, expand physical-surface coverage, and contract the certified alternatives in the direction required by the training objective. These cases are explanatory examples; the complete test results and ablations provide the aggregate evidence. 19 6 Conclusion Scope and limitations. WorldScope deliberately isolates partial-observation reasoning in controlled tabletop scenes. Its 38 target categories, support-surface queries, rendered HSSD interiors, and finite camera families do not cover deformable objects, articulated interactions, dynamic events, outdoor scenes, or real sensor artifacts. The structure-disjoint test measures transfer to new layouts within this controlled domain; it does not establish transfer to arbitrary photographs or embodied environments. The certified counterworlds provide concrete witnesses of ambiguity, not an exhaustive representation of all physically possible completions. Each group contains three complete worlds, which is sufficient to show that a base observation can admit different answers and to supervise view-induced exclusions. Agreement among the three worlds is not treated as proof that no other completion exists. WorldFlow also compresses unresolved support space into a category-neutral 4× 4 map. This representation is efficient, but it can discard fine geometry and object-specific placement information that the denser data executor uses to construct gold evidence. Finally, the reported large-model variants are single training runs selected on a fixed development set. Exact- set scoring requires the predicted set to match the complete gold set, so a prediction with one missing view receives no credit. The substantial gap between seen and unseen structures, especially on minimal sets and maximal identifiable answers, indicates that generalization remains a major challenge. Conclusion and future work. We introduced WorldScope to study world modeling under partial observation by relating visual evidence to compatible possible worlds and the strongest conclusions they jointly support. WorldScope-1.2M supplies 1.2 million questions over shared evidence semantics, certified counterworlds super- vise how added observations exclude complete worlds, and WorldScope-Bench evaluates ten ways of judging and composing partial evidence. WorldFlow combines cross-view entity evidence and physical-surface cover- age into image-subset representations, using counterworld supervision to learn how additional observations constrain compatible worlds. Its 64.34% exact accuracy exceeds same-backbone QA-only training by 24.88 points, while the architecture and curriculum ablations localize the largest gains to image-subset comparison and evidence-boundary reasoning. Future work can extend the same formulation beyond controlled tabletops. One direction is to certify compat- ible worlds from real multi-view captures using reconstruction or additional sensors only during annotation. 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