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SCOPE: Score-Isolated Agentic Optimization for Video World Models
Yuhua Jiang, Jiaming Wang, Qingbin Liu, Feifei Gao
Intelligence
Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 91%
Last extracted: 8/18/2026, 5:17:22 AM
Summary
The paper introduces SCOPE (Score-Isolated Control Optimization with Provenance-Bound Evidence), a framework for auditable inference-time adaptation of frozen video world models. SCOPE addresses the 'inference-control evaluation gap' by treating external controls (prompts, samplers, verifiers) as a typed state that evolves only through bounded, development-evidence-supported updates. Crucially, the final policy is frozen before held-out evaluation to prevent feedback loops. On the Physics-IQ benchmark, SCOPE improves performance by +14.24 over the frozen base, though gains do not uniformly transfer across different model backbones.
Entities (8)
Relation Signals (6)
SCOPE → improves → Physics-IQ
confidence 95% · On Physics-IQ benchmark, Scope improves over the exact frozen base by +14.24
SCOPE → outperforms → Frozen Base
confidence 95% · Scope improves over exact Base on the matched Physics-IQ protocol
SCOPE → uses → Score-Isolation
confidence 95% · SCOPE... provides both score isolation—held-out outcomes cannot drive adaptation
SCOPE → evaluatedon → Wan2.2
confidence 90% · We evaluate Scope with two frozen video backbones, Wan2.2 and CogVideoX
SCOPE → evaluatedon → CogVideoX
confidence 90% · We evaluate Scope with two frozen video backbones, Wan2.2 and CogVideoX
SCOPE → comparedwith → Qwen-Image-Agent
confidence 85% · margin over the strongest matched Qwen-Image-Agent-style baseline remains unresolved
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Abstract
Abstract:Video world models are increasingly used as simulators for planning and embodied decision making, yet improving them at inference time introduces a subtle evaluation problem: prompts, samplers, verifiers, and selectors may evolve together, making it difficult to attribute gains or prevent held-out feedback from shaping the final policy. We introduce \scope (\emph{\scopefullname}), a framework for auditable inference-time adaptation of frozen video world models. \scope represents external controls as a typed state, updates this state only through bounded changes supported by development evidence, and freezes the resulting policy before held-out evaluation. On Physics-IQ benchmark, \scope improves over the exact frozen base by $+14.24$ (95\% CI $[+8.10,+21.23]$). Controlled ablations further identify gains from scene specification, sampling, and learned selection, while the margin over the strongest matched agentic baseline remains unresolved. Cross-backbone and prospective evaluations reveal a complementary result: useful inference-time updates exist, but their benefits do not transfer uniformly across models and settings. Together, these findings suggest that reliable inference-time adaptation requires not only better proposals, but also a principled mechanism for deciding which updates should become part of the deployed system. Code is available at this https URL.
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- Source: https://arxiv.org/abs/2608.15043v1
- Canonical: https://arxiv.org/abs/2608.15043v1
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SCOPE: Score-Isolated Agentic Optimization for Video World Models Yuhua Jiang Affiliation: Tsinghua University Jiaming Wang Affiliation: National University of Singapore Qingbin Liu Affiliation: Tencent Feifei Gao Affiliation: Tsinghua University Abstract Video world models are increasingly used as simulators for planning and embodied decision making, yet improving them at inference time introduces a subtle evaluation problem: prompts, samplers, verifiers, and selectors may evolve together, making it difficult to attribute gains or prevent held-out feedback from shaping the final policy. We introduce Scope (Score-Isolated Control Optimization with Provenance-Bound Evidence), a framework for auditable inference-time adaptation of frozen video world models. Scope represents external controls as a typed state, updates this state only through bounded changes supported by development evidence, and freezes the resulting policy before held-out evaluation. On Physics-IQ benchmark, Scope improves over the exact frozen base by +14.24+14.24 (95% CI [+8.10,+21.23][+8.10,+21.23]). Controlled ablations further identify gains from scene specification, sampling, and learned selection, while the margin over the strongest matched agentic baseline remains unresolved. Cross-backbone and prospective evaluations reveal a complementary result: useful inference-time updates exist, but their benefits do not transfer uniformly across models and settings. Together, these findings suggest that reliable inference-time adaptation requires not only better proposals, but also a principled mechanism for deciding which updates should become part of the deployed system. Code is available at https://github.com/YuhuaJiang2002/SCOPE. 1 Introduction Video world models provide a flexible interface for predicting future visual states and are increasingly used for simulation, planning, and embodied decision making. Yet visually plausible videos are not necessarily physically consistent, and retraining a large backbone for every new task is expensive. This motivates inference-time adaptation: improving a frozen model through external controls such as prompts, sampling strategies, verifiers, retrieval, and candidate selection. The challenge is that these controls are typically coupled. An agent may rewrite a prompt, alter the sampler, call a tool, rerank candidates, and reuse previous experience within the same workflow. If several choices change at once, a higher final score does not identify which intervention was responsible. More importantly, if held-out outcomes are reused to choose later controls, the system can adapt to the evaluation itself. We refer to this as the inference-control evaluation gap: the gap between observing an improvement and establishing that it comes from a well-defined inference-time intervention. We address this problem with Scope, short for Score-Isolated Control Optimization with Provenance-Bound Evidence. Scope treats the external inference harness as an explicit state Ωr _r containing typed text, sampler, verifier, and reward controls. At each development round, an agent proposes a bounded update. The update is accepted only using a predefined development objective; otherwise the previous state is retained. The final route, including an exact Frozen Base fallback, is fixed before held-out evaluation. This design separates three objects that are often conflated in agentic systems: the proposed update, the deployed control state, and the score used to evaluate it. We evaluate Scope from controlled components to the full inference-time system. SceneLang conditioning, a sampler modification, and supervised best-of-N selection each improve their matched controls. On the 40-scene Physics-IQ common-base protocol, the complete procedure reaches a P-IQ score of 34.9434.94 and improves over exact Base by +14.24+14.24 (CI [+8.10,+21.23][+8.10,+21.23]), although the margin over the strongest matched Qwen-Image-Agent-style baseline remains unresolved. Cross-backbone experiments on Wan2.2 and CogVideoX, together with P-AI, OpenS2V-Eval, and prospective evaluations, show a complementary result: improvements found during development do not reliably transfer across all backbones, metrics, or unseen tasks. This distinction between finding useful updates and reliably selecting them is central to our analysis. Contributions. Our contributions are threefold: • We formulate the inference-control evaluation gap for agentic adaptation of frozen video world models and separate proposed updates, deployed controls, and held-out evaluation. • We introduce Scope, a typed inference-control framework with bounded updates, an exact Base fallback, and a score-isolated update rule that prevents held-out scores from changing the deployed state. • We provide matched experiments across controls, backbones, and benchmarks. Scope improves over exact Base on the matched Physics-IQ protocol, while ablations and prospective evaluations identify where component gains transfer and where selection remains unreliable. 2 Related Work Physics-aware video generation. Training-time methods inject physical supervision, imagined trajectories, latent motion priors, or geometry rewards into model parameters (Zhang et al. 2026a; Wang et al. 2026; Jiang et al. 2026; Li et al. 2026a). Retrieval-based methods instead augment generation with external physical knowledge (Cheng et al. 2026), while PhyGround and WorldReasonBench stress-test physical reasoning and future-state prediction (Lin et al. 2026a; Wu et al. 2026). Our setting instead keeps the video backbone fixed and studies reversible inference-time controls under matched evaluation. Inference-time alignment and scaling. WMReward, VIGOR, and VHS rank or steer generated samples with latent-world, geometric, or hidden-state critics (Yuan et al. 2026; Yin et al. 2026; Bucciarelli et al. 2026); iterative refinement and repeated sampling spend additional test-time compute through different operators (Kang et al. 2026; Brown et al. 2024; Ma et al. 2025). These methods primarily optimize a particular search or reward mechanism. Scope instead places prompts, samplers, verifiers, and rewards in a common typed control space and evaluates each change against a matched protocol-local Base. Agentic generation and tool orchestration. A closer comparison comes from agentic systems that improve generation through feedback, planning, and reusable tools. Qwen-Image-Agent closes context gaps through tool-augmented feedback, NEWTON plans physically grounded videos, and LingBot-World structures interactive world generation (Zhang et al. 2026c; Feng et al. 2026; Gao et al. 2026). GenEvolve and COMFYCLAW further organize tool calls and reusable workflow experience for visual generation (Chen et al. 2026b; Li et al. 2026b). Tool-augmented systems can change retrieval, segmentation, geometry, and proposal quality simultaneously, making attribution difficult. Scope therefore contributes a score-isolated update rule rather than a new search, segmentation, or foundation model. The selected route or Base fallback is fixed before held-out scoring, which enables matched ablations of individual controls. Persistent skills and self-evolving harnesses. SkillOpt, SkillCAT, and SkillDAG update or select reusable agent skills (Yang et al. 2026; Chen et al. 2026a; Bai et al. 2026); Library Drift and Harness Updating Is Not Harness Benefit expose failure modes in evolving skill libraries (Zhang et al. 2026b; Lin et al. 2026b). Scope similarly maintains a persistent typed control state, but restricts updates to development evidence while keeping validation and confirmation read-only. This separates the mechanics of updating an inference harness from evidence that the update improves held-out performance. Adaptive evaluation and safe updates. Reusable-holdout methods study validity under repeated adaptive queries, while high-confidence policy evaluation asks whether a proposed policy is safe to deploy from a conservative lower bound (Dwork et al. 2015; Thomas et al. 2015). These ideas motivate the score-isolation principle in Scope. The method prevents direct adaptation to held-out scores, while repeated development on the same ledger can still overfit; prospective evaluation on disjoint tasks therefore remains necessary. proposal context (replaceable infrastructure) prompt first frame VLM feedback or Director card tool calls image_search / sam_segment frozen ledger references, masks, event notes proposal set sA_s Base fallback typed control state Ωr _r: text ⋅· noise verifier ⋅· reward bounded edits add / delete / replace retirable cards provenance + guards conflicts, risk, Base fallback score-blind policy qΩrq_ _r, captions, reward, task evidence freeze before score a⋆(s)a (s) or Base 12 Frozen Wan2.2 WM pθ(y∣x0,c)p_θ(y x_0,c) weights fixed generate mp4 from frozen choice development acceptance rule objective improves and all guards pass accept Ωr+1 _r+1 before held-out evaluation reject edit preserve Ωr _r 3otherwise3 score-isolated state update: an accepted edit becomes the next typed state 4 held-out scores are read-only: validation / confirmation cannot change ΩR _R or the frozen route a⋆(s)a (s). 4Scope CORE A: typed, score-blind control policyScope CORE B: score-isolated state update Figure 1: Overview of Scope. Proposal mechanisms in the gray band are replaceable. Scope maintains (1) a typed state over text, noise, verifier, and reward controls; (2) a score-blind deployment choice with an exact Base fallback; (3) a development-time state update that accepts or rejects bounded edits; and (4) a score-isolation constraint that keeps held-out outcomes read-only. Only the external inference state changes; model weights remain frozen. 3 Method 3.1 Problem Formulation and Overview Let pθ(x1:T∣x0,c)p_θ(x_1:T x_0,c) denote a pretrained video world model, where x0x_0 is the initial observation, c is the conditioning signal, and x1:Tx_1:T is the generated trajectory. Throughout adaptation, the model parameters θ remain frozen. Rather than modifying the world model itself, we adapt an external inference-time control state Ω∈, , (1) where C denotes the space of admissible inference controls. A control state may contain textual directives, sampling configurations, verification or abstention rules, and reward-based selectors. Starting from an initial state Ω0 _0, an agent proposes a sequence of bounded edits δ0,…,δR−1 _0,…, _R-1. State evolution is governed by a development-only update operator, Ωr+1=r(Ωr,δr;Dr),r=0,…,R−1, _r+1=U_r( _r, _r;D_r), r=0,…,R-1, (2) where DrD_r is the sealed development ledger available at round r. Each edit modifies a declared component of the current control state, while rU_r determines whether the edit is committed. Crucially, rU_r has access only to admissible development evidence; held-out evaluation outcomes are never valid inputs to the update process. After R development rounds, the resulting state ΩR _R is frozen. For an evaluation instance s, the frozen state induces the deployment policy πΩR(s)=F(ΩR,xs,s), _ _R(s)=F( _R,x_s,A_s), (3) where xsx_s denotes the available input and sA_s is the corresponding candidate set. The complete routing rule, including the exact Frozen Base fallback, is fixed before the held-out evaluation score is revealed. This formulation separates two questions that are often conflated in inference-time adaptation: which intervention improves development behavior, and which intervention is admissible for deployment. We therefore formulate adaptation as constrained control-state optimization around a frozen world model, rather than unconstrained search over prompts, samplers, or candidate outputs. 3.2 SCOPE: Structured Inference-Time Control Scope instantiates the above formulation with an explicit and persistent control state. At round r, we decompose Ωr=(Ωrtext,Ωrsample,Ωrverify,Ωrselect), _r= ( _r^text, _r^sample, _r^verify, _r^select ), (4) where the four components respectively encode textual directives, sampling controls, verification and abstention rules, and reward-based selection mechanisms. The decomposition makes interventions explicit and independently editable. More importantly, it separates four conceptually distinct operations: proposal, commitment, deployment, and evaluation. At each round, a proposer suggests a bounded modification to one control axis; the update rule determines whether that modification enters the persistent state; the final frozen state determines deployment; and held-out evaluation measures the resulting policy without modifying it. Formally, an axis-specific proposer generates δr=Pr(Ωr,Dr), _r=P_r( _r,D_r), (5) where PrP_r may itself use VLM feedback, Director modules, retrieval, external tools, or predefined proposal families. Scope does not constrain the internal form of PrP_r; instead, it specifies the interface through which a proposal is allowed to modify persistent inference-time behavior. Some components of Ωr _r may themselves be learned during development. For example, the reward selector can be trained on disjoint Physics-IQ scenes. Once development terminates, however, its parameters and inputs are treated as part of the frozen control state. At deployment it consumes only label-free features and cannot be updated using held-out outcomes. Figure 1 summarizes this separation between proposal generation, persistent control-state adaptation, frozen deployment, and held-out evaluation. 3.3 Development-Only Control-State Updates We restrict each proposal δr _r to a bounded elementary add, delete, or replace operation on one declared control axis. This restriction makes individual state changes attributable and prevents a single proposal from silently modifying multiple components. Let Jdev(Ω,Dr)J_dev( ;D_r) denote the predeclared development objective evaluated on ledger DrD_r. We define the development contrast of proposal δr _r as Δrdev(Dr)=Jdev(Ur(Ωr,δr),Dr)−Jdev(Ωr,Dr), _r^dev(D_r)=J_dev (U_r( _r, _r);D_r )-J_dev ( _r;D_r ), (6) where Ur(Ωr,δr)U_r( _r, _r) denotes the state obtained by applying the proposed edit before acceptance testing. In addition to improving the development objective, a proposal must satisfy a predefined collection of admissibility constraints. Let Gr,k(Ur(Ωr,δr);Dr)≤0,k=1,…,K,G_r,k (U_r( _r, _r);D_r )≤ 0, k=1,…,K, (7) represent the individual guards, including motion, sharpness, identity preservation, and static-video checks. We summarize their conjunction by Gr(Dr)=∏k=1K[Gr,k(Ur(Ωr,δr);Dr)≤0].G_r(D_r)= _k=1^K 1 [G_r,k (U_r( _r, _r);D_r )≤ 0 ]. (8) The persistent state evolves through the commit-or-retain rule Ωr+1=Ur(Ωr,δr),Δrdev(Dr)>0∧Gr(Dr)=1,Ωr,otherwise. _r+1= casesU_r( _r, _r),& _r^dev(D_r)>0\ \ G_r(D_r)=1,\\[2.84526pt] _r,&otherwise. cases (9) Equation 9 gives every persistent intervention an explicit acceptance criterion: an edit is committed only when it improves its predeclared development contrast and satisfies every guard. Otherwise, the incumbent is retained exactly. Importantly, this rule does not assume that development improvement transfers monotonically to unseen tasks, environments, or model backbones. Its purpose is narrower: to specify which evidence is permitted to modify the deployed system and to prevent held-out evaluation feedback from becoming an implicit optimization signal. 3.4 Frozen Deployment and Score Isolation After the final development round, adaptation terminates and the state ΩR _R is frozen. For each evaluation instance s, the inference procedure constructs an admissible candidate set s=as(1),…,as(M),asBase,A_s= \a_s^(1),…,a_s^(M),a_s^Base \, (10) where asBasea_s^Base is the exact output of the Frozen Base system and is always retained as a fallback. The deployed route is then fixed as as⋆=πΩR(s)=F(ΩR,xs,s),a_s = _ _R(s)=F( _R,x_s,A_s), (11) before the official evaluation score ysy_s is revealed. Equivalently, the complete deployment mapping ΦΩR:(s,s)⟼as⋆ _ _R:(s,A_s) a_s (12) is precommitted before held-out evaluation. Optional MAGE-lite memory may supply capability, task, experience, or environment records to F. Such records are treated as part of the frozen inference state: they may be constructed or updated during development, but cannot be modified using held-out outcomes. This ordering yields a simple score-isolation property. Proposition 1 (Score-isolation invariant). Fix the proposal randomness, sealed development ledgers, candidate construction procedure, world model, evaluation inputs, and fallback rule. Replacing the complete validation, confirmation, held-out, or official-score ledger by arbitrary alternative values leaves both the final control state ΩR _R and every frozen deployment route as⋆a_s unchanged prior to score release. Proof. By Equation 5, proposal δr _r is determined only by the incumbent Ωr _r, sealed development ledger DrD_r, and fixed proposal randomness. Equation 9 likewise depends only on development quantities associated with the current round. Therefore, by induction over r, the complete state trajectory Ω0,…,ΩR _0,…, _R is invariant to any modification of held-out scores. Equation 11 subsequently depends only on the frozen state ΩR _R, evaluation input xsx_s, candidate set sA_s, and the fixed fallback rule. Hence every route as⋆a_s inherits the same invariance. ∎ The proposition establishes a deliberately narrow guarantee: held-out scores cannot influence either control-state adaptation or deployment routing. It does not imply that the proposal mechanism is optimal, that development selection is unbiased, that development gains generalize to unseen settings, or that Scope necessarily outperforms a matched alternative. These are empirical questions addressed separately in Section 4. 3.5 Provenance-Bound Update Records Score isolation specifies which information may influence adaptation; provenance binding makes the resulting state evolution auditable. We bind each attempted transition to the exact incumbent state, proposal, and development evidence from which it was produced. Let h(⋅)h(·) denote the SHA-256 hash of a canonical serialization of its argument, let br∈0,1b_r∈\0,1\ denote the commit decision, and let ℛrR_r contain the frozen outcomes and reasons of all admissibility guards. Each attempted transition emits the record r=(h(Ωr),h(δr),h(Dr),br,ℛr,h(Ωr+1)).T_r= (h( _r),h( _r),h(D_r),b_r,R_r,h( _r+1) ). (13) A proposal is admissible only if its recorded parent agrees with the current incumbent, h(Ωrparent)=h(Ωr).h( _r^parent)=h( _r). (14) A committed proposal modifies exactly one declared control axis and advances the corresponding revision. A rejected or retired proposal instead restores the incumbent exactly, which implies br=0⟹Ωr+1=Ωr⟹h(Ωr+1)=h(Ωr).b_r=0 _r+1= _r h( _r+1)=h( _r). (15) Consequently, rT_r distinguishes an edit that was merely proposed from one that actually modified the persistent state, while binding every committed change to its parent state, development evidence, and gate decision. Evidence roles. Only evidence explicitly designated for development and sealed before evaluation is admissible to Equations 5– 9. Validation, confirmation, official-score, held-out, and test fields are excluded from the executable update interface. Each admissible transition records the development-ledger hash, guard outcomes, commit decision, and resulting state hash. This distinction has a direct operational consequence. Altering any held-out score leaves the admissible inputs to Equation 9 unchanged and therefore cannot alter the deployed state. Altering development evidence, in contrast, changes h(Dr)h(D_r) and defines a distinct provenance-bound transition that must be evaluated independently. The resulting procedure therefore provides both score isolation—held-out outcomes cannot drive adaptation—and transition traceability—every persistent state change can be replayed from its recorded parent, proposal, evidence, and decision. 4 Experiments 4.1 Experimental Setup We evaluate Scope with two frozen video backbones, Wan2.2 and CogVideoX, across Physics-IQ, P-AI (PAI-Bench-G), and OpenS2V-Eval. The main Physics-IQ comparison uses 40 scenes and reports scene-level composite scores. P-AI evaluates the matched methods on the robot split6 tasks with the official Qwen2.5-VL-72B judge and an eight-dimensional quality score. OpenS2V-Eval contains 180180 items per backbone and is used as a broader open-domain test. We additionally use fresh Physics-IQ and PhyGround tasks for prospective evaluation. All comparisons are performed within the same backbone and evaluation protocol. Unless noted otherwise, deltas are paired against the corresponding Frozen Base and uncertainty is estimated using a paired bootstrap over the protocol’s independent unit. The generator, candidate pool, deployment route, and analysis unit are fixed before held-out outcomes are read. Secondary and post-hoc analyses are reported descriptively rather than pooled with the primary comparison. Full protocol and statistical details are provided in Appendix Table 8. 4.2 Main Results on Physics-IQ Our primary experiment asks two questions under the same 40-scene protocol: whether Scope improves over the exact Frozen Base and whether it improves over the strongest matched alternative. Table 1 compares eleven methods under a matched evaluation protocol: all methods share the same 40 scenes, official scorer, candidate pool, and Base reference within each backbone. The retrospective LingBot Director→ baseline follows the same generation and evaluation protocol. Following a post-score materialization failure, its score evidence was recovered by deterministic replay of the original hash-bound videos, without additional selection or tuning. On Wan2.2, Scope achieves the highest point estimate at 34.9434.94, compared with 32.8732.87 for the strongest non-Scope method, Qwen-Image-Agent-style feedback. The resulting +2.07+2.07 difference remains unresolved (CI [−0.27,+6.14][-0.27,+6.14]). LingBot obtains 29.9829.98, corresponding to a +9.27+9.27 improvement over Base (CI [+2.37,+16.12][+2.37,+16.12]). Its nonlinear full-set aggregate (23.5723.57) is reported separately and is not included in the scene-mean ranking. The clearest matched result is the comparison with Base: Scope improves Physics-IQ by +14.24+14.24 (CI [+8.10,+21.23][+8.10,+21.23]). A descriptive analysis that holds the three generated candidates fixed also yields a +7.53+7.53 improvement over uniform selection (CI [+4.04,+11.52][+4.04,+11.52]). This latter comparison controls for candidate generation, but was not designated as the primary contrast. The result is clear relative to Frozen Base but less conclusive relative to the strongest matched alternative. The direct Scope–Qwen comparison changes only 8/408/40 scenes (3/32/53/32/5 wins/ties/harms), which explains the wider uncertainty in that contrast. Table 1 therefore reports the scene-equal estimand. Because the official Physics-IQ composite is nonlinear, absolute rankings across alternative aggregation schemes are not inferred from scene composites alone. Table 1: Common-base Physics-IQ comparison across two frozen video backbones. All rows report the mean over 40 scene-level Physics-IQ composites. Within each backbone, methods share the same Base and evaluation protocol. The Scope–Qwen differences are +2.07+2.07 on Wan (CI [−0.27,+6.14][-0.27,+6.14]) and +0.52+0.52 on CogVideoX (CI [−0.39,+1.65][-0.39,+1.65]), and remain unresolved on both backbones. Absolute scores are not compared across backbones. The dagger denotes the separately generated fixed Director baseline; green indicates Base-relative intervals with positive lower bounds. Method Wan P-IQ Wan Δ (95% CI) CogVideoX P-IQ CogVideoX Δ (95% CI) Frozen Base 20.7020.70 – 22.9122.91 – WMReward reward-only (Yuan et al. 2026) 21.8121.81 +1.11[−0.40,+3.17]+1.11\;[-0.40,+3.17] 26.1026.10 +3.19[+0.85,+6.04]+3.19\;[+0.85,+6.04] VAE-θ reward 22.3322.33 +1.63[−0.67,+4.46]+1.63\;[-0.67,+4.46] 25.1425.14 +2.23[+0.02,+4.83]+2.23\;[+0.02,+4.83] VIGOR reward-only (Yin et al. 2026) 25.1525.15 +4.44[+1.49,+7.96]+4.44\;[+1.49,+7.96] 27.7527.75 +4.83[+1.71,+8.44]+4.83\;[+1.71,+8.44] uniform random 3-candidate selector (Brown et al. 2024; Ma et al. 2025) 27.4127.41 +6.71[+1.93,+11.45]+6.71\;[+1.93,+11.45] 30.5730.57 +7.65[+4.51,+10.97]+7.65\;[+4.51,+10.97] LingBot-style Director→ proxy† (Gao et al. 2026) 29.9829.98 +9.27[+2.37,+16.12]+9.27\;[+2.37,+16.12] 34.9134.91 +12.00[+6.76,+17.62]+12.00\;[+6.76,+17.62] fixed GPT-5.4 physics card 30.0430.04 +9.33[+2.50,+15.93]+9.33\;[+2.50,+15.93] 34.8934.89 +11.98[+7.05,+17.13]+11.98\;[+7.05,+17.13] fixed rule refinement 31.5031.50 +10.80[+2.67,+19.14]+10.80\;[+2.67,+19.14] 33.8933.89 +10.98[+6.27,+15.93]+10.98\;[+6.27,+15.93] joint self-score ablation 32.0732.07 +11.37[+3.38,+19.69]+11.37\;[+3.38,+19.69] 34.6334.63 +11.71[+6.83,+16.66]+11.71\;[+6.83,+16.66] Qwen-Image-Agent-style feedback (Zhang et al. 2026c) 32.8732.87 +12.16[+4.48,+20.06]+12.16\;[+4.48,+20.06] 34.9934.99 +12.08[+7.36,+16.98]+12.08\;[+7.36,+16.98] SCOPE (ours) 34.9434.94 +14.24[+8.10,+21.23]+14.24\;[+8.10,+21.23] 35.5135.51 +12.60[+7.73,+17.60]+12.60\;[+7.73,+17.60] Figure 2: Evolution of the matched Physics-IQ policy across two frozen backbones. R0–R3 correspond to Base, the physics-card update, the rule-refinement update, and the final selected Scope state. Curves show the mean Physics-IQ score over 40 scenes, with shaded 95% scene-bootstrap confidence intervals. Scores are compared only within each backbone. Figure 2 shows how the matched policy evolves through the four states used to construct the final Scope policy. On Wan2.2, Physics-IQ increases from 20.7020.70 at Base to 30.0430.04, 31.5031.50, and 34.9434.94 across R1–R3. On CogVideoX, the corresponding trajectory is 22.91→34.89→33.89→35.5122.91→ 34.89→ 33.89→ 35.51. Despite the intermediate decrease at R2 on CogVideoX, the final state improves substantially over the exact Base on both backbones. This figure illustrates the evolution relative to Base rather than a comparison with the strongest matched alternative. Additional Wan candidates evaluated in R4–R11 did not replace the R3 state and are omitted for clarity. Figure 3: Example of retrieval-assisted generation on Newton’s cradle. Retrieval provides a textual concept card but no reference pixels or visual embeddings. Both Wan2.2 variants use the same temporal anchoring operation. The visualization for OURS additionally includes the disclosed endpoint-motion intervention and display-only hand replacements. This example is qualitative and is not used as evidence for the aggregate tool-use effect. 4.3 Cross-Backbone Generalization We next study whether the observed behavior transfers when both the video backbone and task distribution change. Table 2 evaluates all methods on the same robot split6 tasks using the official Qwen2.5-VL-72B judge and the same eight-dimensional QS evaluation contract. Table 2: Cross-backbone results on P-AI (PAI-Bench-G). Deltas and paired 95% CIs are relative to the Frozen Base of the same backbone. SceneLang is the strongest fixed-policy comparator. The diagnostic Scope rows report the better of two variants per backbone and therefore do not represent a single transferable policy. Matched method Wan Overall Wan Δ (95% CI) CogVideoX Overall CogVideoX Δ (95% CI) Ranking-eligible matched methods Frozen Base 76.6776.67 – 74.1074.10 – ++ shared global 76.8676.86 +0.19+0.19 CI [−2.13,+2.66][-2.13,+2.66] 74.4174.41 +0.30+0.30 CI [−2.15,+2.77][-2.15,+2.77] LingBot-style Director→ ++ shared global† 76.7776.77 +0.10+0.10 CI [−2.25,+2.54][-2.25,+2.54] 74.2974.29 +0.18+0.18 CI [−2.30,+2.69][-2.30,+2.69] Qwen-Image-Agent-style (SceneLang) 77.0277.02 +0.35+0.35 CI [−1.50,+2.32][-1.50,+2.32] 74.7274.72 +0.62+0.62 CI [−1.37,+2.68][-1.37,+2.68] SceneLang ++ shared global 77.0177.01 +0.34+0.34 CI [−1.86,+2.63][-1.86,+2.63] 71.5771.57 −2.53-2.53 CI [−5.25,+0.12][-5.25,+0.12] Post-hoc upper envelope; no ranking or promotion credit Scope post-hoc upper envelope∗ 77.4777.47 +0.80+0.80 CI [−1.38,+3.12][-1.38,+3.12] 74.4874.48 +0.38+0.38 CI [−1.56,+2.37][-1.56,+2.37] Among the fixed policies, SceneLang achieves the highest point estimate on both backbones (77.0277.02 on Wan and 74.7274.72 on CogVideoX), although its Base-relative confidence intervals include zero. The diagnostic Scope upper envelope gives 77.4777.47 on Wan using the shared-global variant and 74.4874.48 on CogVideoX using the no-global variant. Because these values are obtained by different variants, they should not be interpreted as the performance of a single transferable policy. The shared-global variant further illustrates this backbone dependence. It changes Overall performance by +0.80+0.80 (CI [−1.38,+3.12][-1.38,+3.12]) relative to Frozen Base on Wan2.2, but by −2.41-2.41 (CI [−5.04,+0.13][-5.04,+0.13]) on CogVideoX. Although neither interval is conclusive, the reversal in point estimates suggests that the effectiveness of this component depends on the underlying backbone. Complete Wan profiles are provided in Appendix Table 9. 4.4 Open-Domain Generalization on OpenS2V-Eval We next evaluate cross-backbone generalization in the broader open-domain setting of OpenS2V-Eval. Within each backbone, all methods share the same frozen sampler configuration, and routing decisions are fixed before evaluation. Table 3: Cross-backbone results on OpenS2V-Eval (n=180n=180 per backbone). Scores are multiplied by 100100 and compared only within each backbone. Mean† and Weighted† use the disclosed GPT-5.4 judge with a Qwen fallback proxy. ‡ denotes a prompt-only LingBot-style proxy. Method Wan Mean† Wan Weighted† CogVideoX Mean† CogVideoX Weighted† Frozen Base (Team Wan et al. 2025; Yang et al. 2025) 37.4637.46 36.6036.60 48.3048.30 48.2948.29 Qwen-Image-Agent-style (Zhang et al. 2026c) 38.7238.72 37.9237.92 46.7346.73 46.1646.16 LingBot-style Director→ prompt proxy‡ (Gao et al. 2026) 41.2741.27 42.8442.84 47.3947.39 49.6249.62 GPT-5.4 first-frame skill (internal) 38.3138.31 39.3339.33 46.6746.67 47.9947.99 Scope (ours) 41.7541.75 43.1743.17 47.7647.76 49.2249.22 For a matched agentic baseline, we construct a LingBot-style prompt proxy that injects a Qwen3.5-9B caption of the first frame into a fixed Director→ template, while keeping the video backbone and sampler unchanged. This baseline isolates the contribution of structured agentic prompting within our evaluation protocol; it is not intended to reproduce the trained LingBot-World system. On Wan, Scope improves over this proxy by +0.47+0.47 in Mean (CI [−0.45,+1.43][-0.45,+1.43]) and +0.33+0.33 in Weighted score (CI [−0.93,+1.62][-0.93,+1.62]). On CogVideoX, the corresponding differences are +0.37+0.37 (CI [−0.48,+0.98][-0.48,+0.98]) and −0.40-0.40 (CI [−1.39,+0.51][-1.39,+0.51]), respectively. All four confidence intervals include zero, so these contrasts remain statistically unresolved. Notably, Frozen Base achieves the highest Mean score on CogVideoX, whereas the LingBot-style proxy slightly exceeds Scope on the Weighted metric. Taken together, these results indicate that the gains from learned inference-time control are backbone- and metric-dependent, rather than uniformly transferable across configurations. More broadly, the direction of the effect is not fully stable across backbones. The Mean difference relative to the strongest comparator is slightly positive on both Wan (+0.47+0.47) and CogVideoX (+0.37+0.37), whereas the Weighted difference changes from +0.33+0.33 to −0.40-0.40. P-AI exhibits an even larger directional change, from +0.80+0.80 to −2.41-2.41 relative to the same-backbone Base. These results suggest a backbone–metric interaction rather than a backbone-independent improvement. Taken together, Physics-IQ, P-AI, and OpenS2V-Eval show that Scope is compatible with multiple backbones and evaluation settings, but the effect of a particular control is not invariant to backbone or metric. We therefore report cross-backbone results separately rather than pooling scores across heterogeneous protocols. 4.5 Prospective Evaluation on Unseen Tasks The previous experiments evaluate matched controls and cross-backbone behavior. We next test whether development-time improvements persist when the selection policy is frozen and applied to unseen tasks. Table 4: Prospective evaluations. Positive effects favor the proposed method; PhyGround reports proposal coverage against its predeclared minimum. Evaluation Reference / Criterion Outcome Fresh Physics-IQ Base +0.096[−0.887,+1.077]+0.096\;[-0.887,+1.077] Frozen router −1.019[−2.159,+0.146]-1.019\;[-2.159,+0.146] Typed update Random update −0.316[−5.984,+5.145]-0.316\;[-5.984,+5.145] Tool router Always-on +0.660[+0.016,+1.315]+0.660\;[+0.016,+1.315] No tool +0.083[−0.191,+0.381]+0.083\;[-0.191,+0.381] Random routing +0.002[−0.281,+0.299]+0.002\;[-0.281,+0.299] PhyGround Min. proposal coverage: 8/328/32 4/324/32 Table 4 summarizes four prospective tests. On fresh Physics-IQ tasks, neither the comparison with Base nor that with the frozen router establishes a clear improvement. The typed updater likewise does not outperform a matched random update and degrades 15%15\% of scenes, exceeding the predeclared 10%10\% tolerance. The grounded tool router improves over always-on tool use, whereas its contrasts with no-tool and same-budget random routing remain unresolved. PhyGround exposes a different failure mode: only 4/324/32 tasks produce a valid proposal, below the predeclared minimum coverage of 8/328/32. The proposal stage therefore fails its coverage gate before downstream tool quality can be meaningfully assessed. Together, these results show that development-time component gains do not yet yield robust improvement under prospective evaluation. 4.6 Ablation Studies We analyze three complementary aspects of Scope: individual control axes, control-state updates, and external tool use. Table 5 summarizes all matched ablations. Because these experiments use different controls and evaluation units, their effects are interpreted independently rather than combined additively. Table 5: Ablation studies of Scope. Each row reports a matched comparison and its 95% CI. Component effects are protocol-specific and should not be added across rows. Category Variant Comparison Δ (95% CI) Control Text (SceneLang) vs. Base +5.62[+0.55,+10.55]+5.62\;[+0.55,+10.55] Noise vs. fixed sampler +1.26[+0.52,+2.01]+1.26\;[+0.52,+2.01] Reward vs. matched random +3.33[+1.49,+5.30]+3.33\;[+1.49,+5.30] Reward vs. self-score +0.66[−1.52,+2.92]+0.66\;[-1.52,+2.92] Region-state prior identity metric −7.28[−13.33,−2.53]-7.28\;[-13.33,-2.53] Update Random update vs. Ω0 _0 +4.53[+1.14,+9.17]+4.53\;[+1.14,+9.17] Typed update vs. Ω0 _0 +4.22[+0.81,+8.30]+4.22\;[+0.81,+8.30] Typed update vs. random update −0.32[−5.98,+5.15]-0.32\;[-5.98,+5.15] No fallback vs. Base fallback −0.773[−1.471,−0.203]-0.773\;[-1.471,-0.203] Tool Retrieval vs. no tool +0.562[−0.700,+1.967]+0.562\;[-0.700,+1.967] Retrieval + SAM vs. no tool +0.013[−2.936,+3.263]+0.013\;[-2.936,+3.263] Incremental SAM vs. retrieval −0.548[−3.390,+2.631]-0.548\;[-3.390,+2.631] Component controls. The text and noise controls both improve their matched baselines, and the learned reward selector outperforms matched random selection. Its advantage over self-score, however, remains unresolved. The SceneLang interval is conditional on three fixed seeds. The reward selector is trained with scene-grouped out-of-fold supervision and uses only label-free features at inference. In contrast, the region-state prior degrades the primary identity metric and is therefore excluded from the final control state. Control-state updates. Both the random and typed updates improve over the frozen incumbent Ω0 _0, indicating that updating the control state can be beneficial. The typed update, however, does not outperform an equal-budget random update and degrades 15%15\% of scenes, so the current experiment does not establish an advantage for typed transfer itself. Removing the Base fallback produces a clear degradation, supporting fallback as a conservative mechanism when an update is unreliable. The full +14.24+14.24 Physics-IQ improvement is an integrated system result and should not be interpreted as the sum of these local ablations. Tool use. Retrieval supplies frozen textual evidence rather than reference pixels or visual embeddings, while SAM contributes first-frame geometric information without semantic labels. None of the three tool-use contrasts is statistically resolved. Because the evaluation contains multiple views nested within each scene, we use the scene-paired analysis as the primary estimand. These results indicate that the overall Physics-IQ gain cannot be attributed to tool use alone. 5 Limitations Our experiments expose three main limitations of the current approach. First, strong candidate proposals do not necessarily imply reliable deployment decisions. Several individual controls improve under matched evaluation, and the complete Scope policy substantially improves over Frozen Base on Physics-IQ. However, prospective experiments show that selecting when to apply these controls remains substantially harder than generating useful candidates. In particular, selection errors and insufficient proposal coverage limit performance on unseen tasks. Second, the effectiveness of inference-time controls is not fully invariant across backbones or metrics. Updates that improve one configuration can have smaller effects, or even reverse direction, under another backbone. This limits the extent to which a control learned in one setting can currently be treated as a transferable adaptation rule. Third, Scope relies on development-time evidence to decide which updates may enter the deployed control state. Although held-out scores cannot directly modify the policy, the quality of the resulting policy still depends on the diversity and representativeness of the development tasks. More reliable adaptation will therefore require stronger task-disjoint proposal generation, calibrated uncertainty estimates, and selectors that generalize under distribution shift. The Frozen Base fallback partially mitigates these limitations by allowing the system to abstain from uncertain updates rather than forcing every proposal to be deployed. Nevertheless, improving the reliability of this decision remains a central direction for future work. 6 Conclusion We introduce Scope, a framework for inference-time adaptation of frozen video world models that explicitly separates proposal, update, deployment, and evaluation. Scope represents prompts, sampler configurations, verifiers, and reward selectors within a structured control state, updates this state using development evidence, and freezes the resulting deployment policy before held-out evaluation. On the Physics-IQ benchmark, Scope substantially improves over the exact Frozen Base on both evaluated backbones. Controlled ablations further identify useful contributions from scene specification, sampling, and learned selection, while the advantage over the strongest matched agentic alternative remains unresolved. Cross-backbone and prospective evaluations show that these gains are not uniformly transferable, highlighting reliable selection under distribution shift as the main remaining challenge. More broadly, our results suggest that improving an agentic inference system requires more than generating better candidate interventions. It also requires a principled mechanism for deciding which interventions should become part of the deployed policy. By making this distinction explicit, Scope provides a foundation for studying inference-time adaptation as a controlled optimization problem rather than an unstructured sequence of post-hoc improvements. References Bai et al. (2026) Tong Bai, Zhenglin Wan, Pengfei Zhou, Xingrui Yu, Yang You, and Ivor W. Tsang. 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Appendix A Extended Generalization and Prospective Evaluations A.1 Prospective PAI Evaluation By contrast, the conformal-risk-control stop test asks whether SceneLang should be replaced on new tasks under a fully frozen gate. It fixes 96 development tasks and a disjoint 48-task confirmation set before scoring. The procedure uses strict task-LOO proposals, eight-fold inner calibration, exact SceneLang fallback, and three fixed CRC thresholds. The merged development panel contains 288 official score units. At every threshold, the protocol commits the same two tasks (2/962/96), and both are harmful. The mean gain over the exact SceneLang anchor is −0.173-0.173; the threshold-0 simultaneous interval is [−0.835,0.000][-0.835,0.000], and the conditional harm upper bound is 1.01.0. All five CRC feasibility checks and all three downstream development requirements fail. The procedure therefore retains SceneLang. No videos, VQA outputs, or scores are released for the 48-task confirmation set. A.2 CompletionGuard Successor Evaluation A related successor test considers whether a task-disjoint replacement for CompletionGuard and ContinuityGuard should replace the incumbent over exact SceneLang. It freezes both guards before generating three arms on 96 fresh development tasks, while a separate 28-task confirmation set remains sealed. The 58-dimensional strict task-LOO router makes only two non-fallback proposals (2/962/96), one per candidate arm. Both are non-harmful but improve only QS, with zero RO gain. At every frozen threshold, the minimum 24 commits fails, the conditional harm upper bound is 0.935>0.250.935>0.25, and the simultaneous gain lower bound is exactly zero; the matched-random lower bound is also non-positive. Thus no CRC threshold is selected and the secondary RO/QS gates are not entered. The procedure retains exact SceneLang. We did not generate videos or release VQA for the 28-task confirmation set, and we did not read its scores; the split6 evolution figure is unchanged. A.3 PhyT2V Evaluation As a complementary deployment check, we ask whether candidate expansion adds value beyond retaining the incumbent on a 40-scene PhyT2V evaluation set. Base scores 23.5723.57, original-random 29.4529.45, fixed-round2 31.7131.71, the GPT-5.4 incumbent 33.0833.08, and selected P-IQ 33.0833.08. Although five scenes were eligible for round3, no round3 proposal was promoted. Selected-minus-incumbent is therefore exactly 0.000.00 (CI [0,0][0,0]), whereas selected-minus-fixed-round2 is +1.37+1.37 (CI [−4.04,+7.05][-4.04,+7.05]). The larger +9.51+9.51 contrast with Base is attributable to the retained GPT-5.4 proposal, not to Scope candidate expansion. Moreover, only 15/4015/40 scene IDs are new relative to the preceding evaluation set. We therefore interpret this result as guarded incumbent retention, not as a positive method effect. A.4 Prospective Integrated-System Evaluation Turning from deployment diagnostics to fresh-task tests, we ask whether the full harness improves on 32 fresh, task-disjoint Physics-IQ tasks and passes its deployment criteria. Full Scope is +0.096+0.096 over Base (CI [−0.887,+1.077][-0.887,+1.077]) and −1.019-1.019 versus a simple frozen router (CI [−2.159,+0.146][-2.159,+0.146]). Retirement is significantly harmful relative to commit-plus-fallback: −0.773-0.773 (CI [−1.471,−0.203][-1.471,-0.203]). A separate 40-scene skill-optimization confirmation asks whether the typed update beats matched random. It does not: the contrast is −0.32-0.32 (CI [−5.98,+5.15][-5.98,+5.15]), and the 15%15\% harm rate exceeds the frozen 10%10\% ceiling. Both deployment criteria fail. A.5 Selective Grounded Router Against that backdrop, the grounded-router experiment asks whether selective tool use can avoid the harm of always-on assistance while still outperforming no-tool and random routing. The prospective CausalVerse test uses task-disjoint fit, conformal, applicability, target, and confirmation roles. Every target or confirmation task has the same two candidate videos (no-tool and profiled-card), three frozen seeds, and official CRONOS DisMo scoring; the four policies only choose among those shared videos. The fit-only group policy applies 4848 cards, versus 180180 for always-on and exactly 4848 for a profile/source-stratified random route. Table 6: Selective grounded-router final result. Absolute DisMo cosine similarities reuse the same two candidate arms and three seeds for all four policies. Cards count profiled prompt-card applications across 192192 target and 9696 confirmation tasks; they are not additional generated videos. Policy Cards Target Confirmation Pooled Exact no-tool 00 0.40710.4071 0.44250.4425 0.41890.4189 Always-on profiled card 180180 0.40210.4021 0.43520.4352 0.41320.4132 Fit-only group policy 4848 0.40960.4096 0.44010.4401 0.41970.4197 Same-budget stratified random 4848 0.40840.4084 0.44240.4424 0.41970.4197 The result is mixed. The selective router avoids the harm of always-on use but does not establish an absolute generation gain or routing skill beyond random. In DisMo points, the preregistered pooled contrasts are +0.083+0.083 for fit-group minus no-tool (CI [−0.191,+0.381][-0.191,+0.381]), +0.660+0.660 for fit-group minus always-on (CI [+0.016,+1.315][+0.016,+1.315]), and +0.002+0.002 for fit-group minus same-budget random (CI [−0.281,+0.299][-0.281,+0.299]). The target result is positive: +0.243+0.243 points overall and +1.460+1.460 over the 3232 switched tasks. Independent confirmation, however, reverses sign to −0.237-0.237 overall and −1.420-1.420 over 1616 switched tasks, with negative means for all three confirmation seeds. Motion/sharpness ratios are 1.001/1.0081.001/1.008, and all 1,7281,728 scores are present. We therefore conclude that selective fallback significantly avoids the strong harm of always-on use, especially for the geometry group, while leaving the no-tool and same-budget-random contrasts unresolved. Because the evidence calls were acquired before policy selection and shared across all four policies, the experiment cannot identify fewer tool calls as the cause of the quality change. A.6 PhyGround Applicability Analysis Finally, the PhyGround chain asks whether the preregistered selector can clear applicability before any target score is read. It stops at the pre-score gate on a 32-task target set. The pipeline completes all 288288 frozen target videos and score-blind features, but the frozen proxy produces only 4/324/32 raw non-incumbent proposals, below the required minimum of 88. It produced 00 proposals at confidence ≥−0.25≥-0.25 (minimum 88) and 00 at confidence ≥0≥ 0 (minimum 44), while law coverage passed for eight families. The protocol therefore stops before any target PhyGround score is read and leaves the 21-task confirmation set untouched. This is an applicability failure, not a negative target-score estimate. A score-free audit then explains why the gate fails. The main driver is the global task-max conformal deduction (58.4758.47, or 0.7310.731 after gain normalization) combined with worst-head selection; motion and sharpness bind on no selected task. The frozen held-out calibration split likewise produces only 3/643/64 raw switches. At that observed rate, an eight-of-32 gate has probability 8.9×10−58.9× 10^-5. This post-failure replay diagnoses the frozen design but cannot justify replacing the policy on the consumed pool. Appendix B Additional Discussion The component experiments clarify which parts of the current inference-control space are useful. Text conditioning and the sampler modification improve their matched comparisons, and the supervised reward selector outperforms matched random under scene-grouped OOF evaluation. In contrast, the region-state prior and the matched web/SAM tool variants do not show reliable gains. These results are important because they prevent the integrated Physics-IQ improvement from being attributed uniformly to every component. The prospective experiments expose a different limitation: reliable deployment requires both sufficient proposal coverage and a selector that transfers beyond its development distribution. The selective router avoids the degradation of always-on tool use while remaining unresolved against no-tool and same-budget random routing, and its confirmation result reverses direction. PhyGround fails even earlier because the frozen selector proposes too few non-incumbent actions. These cases motivate future work on task-disjoint calibration and selectors that can abstain when uncertainty is high, rather than relaxing thresholds after observing target outcomes. Appendix C Reproducibility Details All reported results are linked to the scripts and machine-readable outputs used to generate the corresponding tables and figures. The release preserves the exact protocol-local inputs for each experiment and does not aggregate statistics across incompatible evaluation units. Appendix D Evaluation Protocols and Statistical Details Table 8 summarizes the independent unit, evaluation role, and primary comparison used for each experimental setting. It is provided to make the statistical units and protocol boundaries explicit. Table 7: Result-to-artifact index. Each row links a reported experimental result to its smallest authoritative machine-readable source. Result Evaluation role Authoritative artifact(s) Matched 40-scene Physics-IQ procedure vs exact Base same-universe system result physiq_common_base_result.json; matched publication in the release manifest Retrospective LingBot harness proxy same-protocol retrospective addition matched score evidence and result in the release manifest SceneLang text edit component; three fixed seeds scenelang_skill_table.json Fixed sampler edit component; four fixed seeds sampler_axis_joint_fourseed_result.json Supervised reward selector scene-grouped OOF reward_gain_ci.json Integrated and new-task prospective failures prospective negative / stop pai_scope_lifecycle_staircase_result.json; scope_v6_fit_group_final_result.json; pai_wide_crc_human096_development_completion_r7.json; pai_wide_completion_guard_development_completion.json Table 8: Protocol and statistical status matrix. Views are nested within scenes for Physics-IQ; secondary, same-split, and post-hoc rows never receive promotion credit. Setting Independent unit Evaluation role Primary comparison GPT-5.4 SceneLang scene; 3 fixed seeds averaged component evidence SceneLang vs Base; CI conditional on these seeds Learned reward 64 scenes; 179 nested views component evidence scene-grouped OOF reward vs matched random Physics-IQ tool axis scene; 3 nested views preregistered negative ablation web-only and web+SAM vs no-tool; incremental SAM vs web-only PAI split6 task same-split deployment diagnostic all matched controls shown; post-hoc Scope separated OpenS2V category-stratified item prospective deployment case Scope vs LingBot; unresolved Fresh PhyT2V evaluation scene deployment diagnostic selected vs incumbent/fixed proposal; no expansion credit Fresh 32-task Physics-IQ transfer task prospective integrated test full harness vs Base/router; frozen guard gate Typed-update confirmation scene prospective integrated test typed update vs matched random plus harm ceiling Grounded selective router task; role/profile/family bootstrap prospective target+confirmation; STOP fit-group vs no-tool, always-on, and same-budget random Appendix E Full PAI Metric Breakdown Table 9 exposes the quality components aggregated by QS in the main PAI panel. Frozen Base is shown in absolute score units; every other row is a signed difference from that same Base. These component-wise values are descriptive diagnostics, not separately tested deployment criteria. Table 9: Complete official-72B PAI quality profiles. SC/BC are subject/background consistency; MS is motion smoothness; AQ/IQ are aesthetic/imaging quality; OC is overall text–video consistency; IS/IB are I2V subject/background fidelity. Frozen Base is absolute and all other rows are Δ vs Base. All 12 main-panel rows are included. Method SC BC MS AQ IQ OC IS IB Frozen base (absolute) 89.51 91.71 98.49 44.21 62.83 19.50 92.44 95.75 Shared global (Base parent) -0.45 -0.18 -0.73 +0.61 +1.60 -0.12 -0.09 -0.02 Qwen-Image-Agent-style (SceneLang) (Zhang et al. 2026c) +0.89 +0.37 +0.13 +0.42 +0.50 -0.06 +0.66 +0.49 SceneLang + shared global -0.03 +0.19 -0.65 +0.79 +2.25 -0.04 +0.10 +0.03 LingBot-style + safe fallback† (Gao et al. 2026) +0.24 +0.10 +0.03 0.00 +0.28 -0.02 +0.13 +0.12 LingBot-style Director→ + shared global† (Gao et al. 2026) -0.16 +0.02 -0.70 +0.70 +2.17 -0.22 +0.06 +0.07 LingBot-style Director→ (raw) (Gao et al. 2026) +1.33 +0.49 +0.26 -0.24 +0.94 +0.12 +0.58 +0.64 LingBot-style + shared global +0.15 +0.22 -0.55 +0.60 +1.73 -0.26 -0.08 +0.15 GPT-5.4-SKILL (raw) -1.02 -1.61 +0.07 -1.41 -0.09 +0.09 +0.14 +0.72 GPT-5.4-SKILL + shared global -2.67 -2.27 -0.66 -1.64 -1.14 -0.24 -0.54 +0.07 Scope w/o shared global‡ +0.81 +0.36 +0.13 +0.41 +0.57 -0.01 +0.61 +0.44 Scope + shared global (ours)‡ -0.06 +0.20 -0.68 +0.69 +2.33 -0.03 0.00 +0.02 Appendix F Additional Qualitative Analysis The following visualization illustrates a disclosed post-hoc paper-submersion intervention. It is included only as qualitative analysis and is not a native generation result or benchmark comparison. Figure 4: Web-grounded complete paper submersion intervention. Both rows use the same straw-free seed-2026078520260785 Wan2.2 baseline. The unexplained overhead straw is inpainted once in their shared conditioning frame. WITHOUT OURS shows the complete baseline frame, in which only the lower paper edge contacts the water. OURS applies a disclosed post-hoc full-frame intervention: the complete paper mask is compressed onto the dish bottom; every final paper pixel lies within the audited water ellipse; and water tint, caustics, refraction, and a ripple band are rendered after the paper. Both published A/B MP4s and the displayed frames are complete ×480832\!×\!480 images, with dish cropping disabled in the manifest. The post-absorption Wikimedia image is display-only and never enters either video. Retrieved pixels likewise never enter the video. The native four-seed prompt grid failed the qualitative gate and is not shown. This visualization is a qualitative post-hoc state intervention, not native Wan dynamics, an unbiased generation result, or benchmark evidence.