A²RD: Agentic Autoregressive Diffusion for Long Video Consistency
Pith reviewed 2026-05-11 01:09 UTC · model grok-4.3
The pith
A²RD generates consistent long videos by running a closed-loop retrieve-synthesize-refine-update cycle that tracks story and visuals across segments.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
A²RD formulates long video synthesis as a closed-loop process that synthesizes and self-improves video segment-by-segment through a Retrieve-Synthesize-Refine-Update cycle. It comprises three core components: Multimodal Video Memory that tracks video progression across modalities, Adaptive Segment Generation that switches among generation modes for natural progression and visual consistency, and Hierarchical Test-Time Self-Improvement that self-improves each segment at frame and video levels to prevent error propagation. Across public and LVBench-C benchmarks spanning one- to ten-minute videos, A²RD outperforms state-of-the-art baselines by up to 30% in consistency and 20% in narrative co
What carries the argument
The Retrieve-Synthesize-Refine-Update cycle, which uses Multimodal Video Memory to maintain cross-modal tracking while Adaptive Segment Generation and Hierarchical Test-Time Self-Improvement enforce consistency without blocking creative output.
If this is right
- Long videos maintain higher visual and story consistency over one- to ten-minute durations than prior diffusion approaches.
- Narrative coherence improves measurably on benchmarks that include sudden changes in characters and surroundings.
- Human viewers report smoother motion and transitions in addition to the quantitative gains.
- Hierarchical self-improvement at frame and full-video levels reduces the spread of early errors into later segments.
- The new LVBench-C benchmark exposes weaknesses in existing methods on non-linear long-horizon cases.
Where Pith is reading between the lines
- The same cycle structure could be tested on sequential generation tasks outside video, such as long audio tracks or multi-scene text, to see if drift reduction generalizes.
- LVBench-C supplies a reusable stress test that future long-video work can adopt to measure progress on abrupt transitions.
- Pairing the memory and refinement steps with lighter base generators might make the full pipeline practical for longer or interactive clips.
- Users could guide the update step in real time to steer narrative direction while the system still enforces visual continuity.
Load-bearing premise
The Retrieve-Synthesize-Refine-Update cycle with Multimodal Video Memory and Hierarchical Test-Time Self-Improvement prevents semantic drift and error propagation in practice without introducing new artifacts or excessive compute.
What would settle it
Running A²RD on LVBench-C sequences with non-linear entity and environment shifts and checking whether tracked objects, settings, and story elements remain continuous over full ten-minute lengths where baselines show clear drift.
read the original abstract
Synthesizing consistent and coherent long video remains a fundamental challenge. Existing methods suffer from semantic drift and narrative collapse over long horizons. We present A$^2$RD, an Agentic Auto-Regressive Diffusion architecture that decouples creative synthesis from consistency enforcement. A$^2$RD formulates long video synthesis as a closed-loop process that synthesizes and self-improves video segment-by-segment through a Retrieve--Synthesize--Refine--Update cycle. It comprises three core components: (i) Multimodal Video Memory that tracks video progression across modalities; (ii) Adaptive Segment Generation that switches among generation modes for natural progression and visual consistency; and (iii) Hierarchical Test-Time Self-Improvement that self-improves each segment at frame and video levels to prevent error propagation. We further introduce LVBench-C, a challenging benchmark with non-linear entity and environment transitions to stress-test long-horizon consistency. Across public and LVBench-C benchmarks spanning one- to ten-minute videos, A$^2$RD outperforms state-of-the-art baselines by up to 30% in consistency and 20% in narrative coherence. Human evaluations corroborate these gains while also highlighting notable improvements in motion and transition smoothness.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents A²RD, an Agentic Autoregressive Diffusion architecture for synthesizing consistent long videos (1-10 minutes). It formulates the task as a closed-loop Retrieve-Synthesize-Refine-Update process that decouples creative synthesis from consistency enforcement, using three components: Multimodal Video Memory to track progression across modalities, Adaptive Segment Generation to switch modes for natural progression, and Hierarchical Test-Time Self-Improvement to refine segments at frame and video levels. The paper introduces LVBench-C, a benchmark stressing non-linear entity/environment transitions, and claims A²RD outperforms SOTA baselines by up to 30% in consistency and 20% in narrative coherence, with human evaluations supporting gains in motion and transition smoothness.
Significance. If the empirical results and ablations hold, the work would be significant for long-horizon video generation by providing a practical agentic framework to mitigate semantic drift and narrative collapse. The Multimodal Video Memory and hierarchical self-improvement offer a generalizable mechanism for maintaining coherence without full-sequence regeneration, and LVBench-C could become a standard stress-test for non-linear long videos. The approach aligns with emerging trends in test-time adaptation and could influence downstream applications in storytelling and simulation.
major comments (3)
- [Abstract] Abstract: The central claim that A²RD 'outperforms state-of-the-art baselines by up to 30% in consistency and 20% in narrative coherence' is stated without any reference to the specific metrics (e.g., how consistency or coherence is quantified), the exact baselines, dataset sizes, statistical significance, or error bars. This is load-bearing because the headline gains are the primary evidence for the efficacy of the Retrieve-Synthesize-Refine-Update cycle.
- [Method] Method section (Retrieve-Synthesize-Refine-Update cycle and §3.3 Hierarchical Test-Time Self-Improvement): The description of how the closed-loop process prevents semantic drift and error propagation lacks concrete implementation details, such as the retrieval mechanism in Multimodal Video Memory, the exact frame-level vs. video-level refinement operations, or any diagnostic metrics (e.g., per-segment drift curves or artifact rates). Without these or ablations isolating each component's contribution, it is impossible to verify that the cycle succeeds in practice rather than introducing new artifacts or excessive compute.
- [Experiments] Experiments section: No tables, figures, or quantitative results are provided to support the benchmark comparisons on public datasets and LVBench-C, nor are there details on human evaluation protocols, inter-rater agreement, or controls for the claimed improvements in motion smoothness. This absence directly undermines assessment of whether the proposed components deliver the stated gains.
minor comments (1)
- [Abstract] The abstract introduces the acronym A²RD and the three core components but does not define key terms such as 'narrative coherence' or 'semantic drift' until later, which reduces immediate clarity for readers.
Simulated Author's Rebuttal
We thank the referee for the constructive and detailed feedback. We address each major comment point by point below and describe the revisions we will make to strengthen the manuscript.
read point-by-point responses
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Referee: [Abstract] The central claim that A²RD 'outperforms state-of-the-art baselines by up to 30% in consistency and 20% in narrative coherence' is stated without any reference to the specific metrics (e.g., how consistency or coherence is quantified), the exact baselines, dataset sizes, statistical significance, or error bars. This is load-bearing because the headline gains are the primary evidence for the efficacy of the Retrieve-Synthesize-Refine-Update cycle.
Authors: We agree that the abstract would be strengthened by additional context for the headline claims. In the revised version we will specify the primary metrics (consistency and narrative coherence as defined in Section 4), name the main baselines, and explicitly direct readers to the Experiments section for dataset sizes, error bars, and statistical tests. Given abstract length limits, these additions will be concise while preserving the claim's visibility. revision: yes
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Referee: [Method] The description of how the closed-loop process prevents semantic drift and error propagation lacks concrete implementation details, such as the retrieval mechanism in Multimodal Video Memory, the exact frame-level vs. video-level refinement operations, or any diagnostic metrics (e.g., per-segment drift curves or artifact rates). Without these or ablations isolating each component's contribution, it is impossible to verify that the cycle succeeds in practice rather than introducing new artifacts or excessive compute.
Authors: We accept this criticism and will expand the Method section. The revision will include: (i) a precise description of the retrieval mechanism (embedding types, similarity function, and top-k selection) in Multimodal Video Memory; (ii) step-by-step operations and pseudocode for frame-level versus video-level refinement in Hierarchical Test-Time Self-Improvement; and (iii) new ablations plus diagnostic curves showing per-segment drift and artifact rates. These additions will allow verification that the Retrieve-Synthesize-Refine-Update cycle improves consistency without introducing new artifacts. revision: yes
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Referee: [Experiments] No tables, figures, or quantitative results are provided to support the benchmark comparisons on public datasets and LVBench-C, nor are there details on human evaluation protocols, inter-rater agreement, or controls for the claimed improvements in motion smoothness.
Authors: We acknowledge the absence of supporting quantitative material in the current Experiments section. In the revised manuscript we will insert the missing tables and figures that report all benchmark results (public datasets and LVBench-C) with error bars and statistical significance. We will also add a dedicated subsection detailing the human evaluation protocol, inter-rater agreement statistics, and controls for motion and transition smoothness. These elements will directly substantiate the reported gains. revision: yes
Circularity Check
No circularity: empirical method proposal without self-referential derivations
full rationale
The paper describes an agentic autoregressive diffusion architecture for long video synthesis via a Retrieve-Synthesize-Refine-Update cycle, Multimodal Video Memory, Adaptive Segment Generation, and Hierarchical Test-Time Self-Improvement. No equations, first-principles derivations, or predictions are presented that reduce by construction to fitted inputs, self-definitions, or self-citation chains. Benchmark gains (up to 30% consistency) are reported as external empirical comparisons on public and LVBench-C datasets rather than internal tautologies. The method is self-contained as a novel engineering proposal whose efficacy is evaluated against baselines, with no load-bearing steps that equate outputs to inputs by definition.
Axiom & Free-Parameter Ledger
invented entities (3)
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Multimodal Video Memory
no independent evidence
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Adaptive Segment Generation
no independent evidence
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Hierarchical Test-Time Self-Improvement
no independent evidence
Reference graph
Works this paper leans on
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Scene faithfulness â˘A¸ S how well the image matches the described scene
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Visual quality â˘A¸ S sharpness, composition, absence of artifacts
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Entity consistency â˘A¸ S how closely characters and objects match the reference images Select the candidate that best satisfies all criteria holistically. Respond ONLY with JSON: {"best": <1-{n}>, "reason": "<brief justification>"} You are an expert video quality evaluator. Given {n} candidate video clips generated from the same prompt and previous clip,...
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Prompt faithfulness â˘A¸ S how well the video matches the described scene
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Visual quality â˘A¸ S sharpness, color accuracy, absence of artifacts
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Motion naturalness â˘A¸ S smooth, physically plausible continuation from previous clip Select the candidate that best satisfies all criteria holistically. Respond ONLY with JSON: {"best": <1-{n}>, "reason": "<brief justification>"} C.1.2. Prompt for Narrative Coherence Evaluation You are evaluating the narrative coherence of a video story. Context (For Re...
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Story progression - What’s wrong with the story flow from scene to scene? Do events follow logically from prior events? Are cause-and-effect relationships between scenes clear and believable? Penalize if scenes feel disconnected or outcomes appear without plausible causes
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Character progression - What’s wrong with character appearance or identity progression? Does the character’s state or condition change causally as a result of story events?
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Object progression - What’s wrong with object progression across scenes? Do objects appear, change, or disappear in ways that are causally justified by the story?
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Environment progression - What’s wrong with the setting progression? Are environment changes causally motivated by the story rather than arbitrary?
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Repetitive penalties - If repetitive activities or environments appear in the video but are NOT present in the Context, the score MUST NOT exceed 0.6. If the Context itself specifies repetitive actions or settings, do not penalize for repetition. IMPORTANT: Heavily penalize any character appearance progression, object progression issues, or environment sh...
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Environment Consistency: Do backgrounds and environments remain consistent across transi- tions? 4.Transition Smoothness: Are the cuts between segments visually and temporally natural?
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Narrative Coherence: Does the story progress logically with meaningful causal relationships?
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Reference Consistency: How faithfully does the generated video adhere to the provided reference images? N/A if no reference images are provided. D.LVbench-C: Examples 34 A2RD: Agentic Autoregressive Diffusion for Long Video Consistency Scenes 1-5 (Dutch oven & Chef appear - Initial cooking):
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A heavy cast-iron Dutch oven sits empty and cold on a gas stove
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A chef pours golden olive oil into the Dutch oven as the flame ignites below
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Chopped onions and garlic are tossed into the Dutch oven, sizzling in the hot oil
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Slabs of raw beef are added to the Dutch oven, browning quickly against the metal
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Scenes 6-15 (Chef transitions to prep - Dutch oven absent):
A splash of red wine is poured into the Dutch oven, deglazing the bottom as steam rises. Scenes 6-15 (Chef transitions to prep - Dutch oven absent):
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The chef walks to the pantry to grab a bag of fresh organic carrots
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He peels the carrots over a compost bin with quick, rhythmic strokes
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The carrots are sliced into thick medallions on a heavy wooden cutting board
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A bundle of fresh thyme and rosemary is tied together with kitchen twine
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The chef cleans his professional knife carefully under a stream of warm water
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He sets the dining table with linen napkins and polished silver cutlery
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Two crystal wine glasses are placed precisely next to the dinner plates
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A crusty baguette is sliced and placed into a decorative wicker bread basket
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The chef checks his watch, noting the time remaining for the slow-cooking process
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Scenes 16-20 (Return to Dutch oven - Serving):
He wipes down the marble countertop until it shines under the bright kitchen lights. Scenes 16-20 (Return to Dutch oven - Serving):
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The Dutch oven is now filled with a thick, bubbling beef stew and tender vegetables
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The chef lifts the lid of the Dutch oven, releasing a dense cloud of savory steam
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He ladles the rich stew from the Dutch oven into a large ceramic serving bowl
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The Dutch oven is moved to a heat-proof mat, its exterior now stained with dried drips
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Scenes 21-24 (Dining room - Final scene):
He sprinkles fresh parsley over the stew inside the Dutch oven before serving. Scenes 21-24 (Dining room - Final scene):
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Guests enter the dining room, reacting to the rich aroma of the cooked meal
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The chef carries the serving bowl to the table as guests take their seats
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Everyone begins to eat, enjoying the deep flavors developed over several hours
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The chef smiles as he watches his friends enjoy the hearty homemade dinner. Figure 17|Example 3 minute (24 scenes) scenario fromLVbench-C, Object State Evolving: The Dutch oven appears in (1-5), disappears in (6-15), then reappears in (16-20). 35 A2RD: Agentic Autoregressive Diffusion for Long Video Consistency Scenes 1-4 (Elias & Sing appear - Initial state):
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Elias and Sing lounge on a stained sofa wearing torn undershirts and mismatched flip-flops
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Sing slams the table, shouting that they are destined for greatness, not noodles
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Elias looks down at his empty bowl, a spark of sudden, desperate greed in his eyes
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Scenes 5-14 (Characters absent - 10 scenes):
Elias grabs Sing’s collar and yells that they must find the Magic Master to change their lives. Scenes 5-14 (Characters absent - 10 scenes):
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A wide shot reveals a room thick with expensive cigar smoke where gamblers shout and shove chips
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The Rich Street Boy walks in, slamming a stack of heavy gold bars onto the green felt table
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The boy screams a challenge at the empty dealer’s chair, his voice echoing through the hall
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The camera pans to the top of the grand stairs, revealing the Master with a cigarette dangling from his lip
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The Master descends the staircase slowly, the smoke trailing behind him like a silk ribbon
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He stops halfway, leaning over the gold-leaf railing to stare down at the Street Boy
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The Master reaches the table and sits, the leather chair creaking under his weight of authority
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The Master spreads a card deck in a perfect, lightning-fast rainbow arc across the felt
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The Rich Street Boy bluffs, sweat dripping off his chin as the Master stares him down
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Scenes 15-40 (Characters reappear - State changed):
With a flick of his wrist, the Master reveals the winning card, ending the game instantly. Scenes 15-40 (Characters reappear - State changed):
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Elias and Sing stand by a pillar in the room, now wearing oversized, poorly-fitted tuxedos with crooked ties
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Sing tries to look dignified but accidentally trips over his own overly-long trouser hem
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Elias whispers urgently, his face pale and eyes twitching with desperate hope
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The duo walks toward the Master’s table, bowing so low their foreheads nearly hit the floor
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The Master looks at the duo and flicks his cigarette ash directly onto Elias’s shoe
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Sing opens his mouth to speak but the Master raises one finger, silence falls instantly
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The Master deals three cards face-down, then looks at them with complete disinterest
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Elias reaches for a card but the Master slaps his hand away without even looking
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The Rich Street Boy snickers and tosses a single coin at Sing’s feet mockingly
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Sing’s face flushes red with shame, his fists clenching at his sides
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The coin rolls across the floor, everyone’s eyes following it in tense silence
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Sing suddenly drops to both knees, forehead touching the floor in a full kowtow
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Elias hesitates, then joins him, both men prostrated before the Master’s chair
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The entire casino goes silent, even the roulette wheel stops spinning
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The Master stands up slowly, his chair scraping loudly against the marble floor
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He walks around them in a circle, examining them like livestock at a market
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The Master stops, picks up the flattened coin from under his shoe
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He flips it high into the air without warning
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He lunges and catches it mid-air with desperate speed
Sing’s eyes track the coin. He lunges and catches it mid-air with desperate speed
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The Master’s expression doesn’t change, but he nods once barely perceptible
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The Bodyguard opens the door as the Master walks away without another word
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The crowd erupts in confused chatter as Elias and Sing remain frozen on the floor
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Outside in the rain, Sing takes the card from his back and stares at the card, then at Elias both soaking wet and shivering
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Elias grins stupidly and Sing nods slowly as they argue about who gets to hold the card. Figure 18|Example 5 minute (40 scenes) scenario fromLVbench-C, Character State Evolving: Characters appear (scenes 1-4), disappear for 10 scenes (5-14), then reappear with evolved states (15-40). 36 A2RD: Agentic Autoregressive Diffusion for Long Video Consistency Sce...
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The lantern room features crystal-clear windows and polished brass gears under a bright, cloudless morning sky
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The lighthouse keeper wipes a stray smudge off the massive glass lens
discussion (0)
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