cs.AIAug 18, 2026

SAGE: Self-Evolving Storyboard Skills via Attribution-Guided Rule Evolution

Authors: Maolin Ran, Xiaoyang Lu, Jiaqi Liu, Jian Wang, Weiwen Liu, Jianghao Lin, Yong Yu, Weinan Zhang

Organizations: Shanghai Jiao Tong University Shanghai, China · CreativeFitting Shanghai, China

Read PDF
Ask AI
Cite

Copy citation

DOINot available for this paper

Abstract

Storyboards turn screenplays into visual shot plans for automated short drama production. Professional storyboarding relies on tacit directorial expertise and remains an industrial bottleneck. Large language models can automate this step, but methods for supplying directing knowledge face three challenges: (1) Knowledge acquisition: the craft remains implicit in exemplars or must be written manually. (2) Knowledge refinement: authored knowledge is not evaluated against execution outcomes, and opaque generation prevents feedback attribution to the knowledge behind each decision. (3) Knowledge injection: injecting all knowledge exceeds usable context, while manual selection for every narrative group does not scale. We present SAGE (Skill with Attribution-Guided Evolution), a deployed framework that learns, attributes, evolves, and routes directing knowledge from expert demonstrations. SAGE derives rules that are independent of episode content by contrasting each training screenplay with its expert storyboard. During generation, the model records each narrative group's adopted rules. Combining these records with localized feedback enables targeted updates to individual rules. Evolved rules form scenario packages with a routing index, so each group retrieves only a bounded set appropriate to its situation without expert intervention. On 18 test episodes across three genres, SAGE scored 77.8 on a rubric validated by experts, versus 77.1 for professional directors. Deployed for 14 days on Virtual Film Studio, SAGE produced 1,344 narrative group outputs; 87.2 percent were accepted without substantive edits, and the production team recorded over 83 percent less authoring time per episode. We release PROSE, the first public dataset pairing screenplays with storyboards by professional directors across 68 episodes: https://github.com/creDreams/PROSE.

Figures & tables

Figure 1. Camera language decisions on one screenplay beat from His Toyboy EP001, a held-out test episode, under an identical screenplay, backbone LLM, and storyboard schema. SAGE alone selects a low-angle push-in that reinforces Victor’s dominance, whereas Few-shot and CoT keep the angle flat and Vanilla widens to a composition that weakens the power dynamic. Only the textual records are system outputs; thumbnails and threat labels are post-hoc readings, excluded from evaluation. Five stacked rows compare four systems on one screenplay beat. The top row states the input screenplay line. The next four rows correspond to SAGE, Few-shot, CoT, and Vanilla. Each of these rows holds a textual storyboard record, three camera language fields for shot scale, camera angle, and camera movement, a greyscale thumbnail, and a threat level indicator. The SAGE row lists close-up, low angle, and push-in. The other three rows list flat angles and a static camera. A closing row summarizes the comparison.
Figure 2. Overview of SAGE. Stage 1 (Evolution) : for each training episode, a four-phase loop generates storyboards with per-group rule attribution, evaluates them against the expert reference, and revises the rule set through attribution-guided diagnosis. Stage 2 (Consolidation) : rules evolved across all episodes are deduplicated, embedded, and clustered into scenario packages with a routing index. Stage 3 (Inference) : on unseen screenplays, narrative groups are routed to their matched scenario packages, whose rules are injected into generation; no expert reference is required. A horizontal pipeline of four blocks. The leftmost block holds the screenplay and the expert storyboard. The Evolution block encloses a cycle of four numbered phases, namely rule extraction, attributed generation, alignment evaluation, and attribution-guided diagnosis, with rule-adoption records at the center and a loop labeled T rounds. The Consolidation block lists deduplication, condition embedding, clustering, and scenario labeling. The Knowledge Base block holds a routing index and scenario packages. The rightmost Inference block lists grouping, top-k routing, rule-injected generation, and merging into a storyboard.
Figure 3. One round of attribution-guided rule evolution, on a real trace from Beyond the Wall . Attributed generation (Phase 2) records which rules each group adopted; diagnosis (Phase 4) joins these records with dimension-level feedback to decide, per rule, whether to revise, add, or retire. The upper part shows the four phases of one evolution round in sequence, annotated with a real trace in which a camera angle rule scores 63 out of 100 and is marked for revision. The lower part expands rule-level credit assignment. A rule-adoption record and a localized feedback item meet at a join operation, which produces a rule-level diagnosis. Three outcomes branch from the diagnosis, namely a revised rule, a newly added rule, and a retired rule.
MethodRhythmVisualScaleAngleMove.Overall
Director80.175.181.776.871.677.1
Vanilla68.267.474.462.153.865.2
Few-shot73.173.378.268.064.171.2
CoT79.480.879.572.168.476.0
EvoSkill73.972.274.362.859.768.7
SkillOpt78.974.979.574.468.175.1
Table 1. Main comparison on the 18-episode test set (quality scores, 100-point scale). Bold : best among AI systems; underline : exceeds the human director.
 ConfigurationOverallΔ\Delta
AVanilla (no external knowledge)65.2−-
B+ rule warm-start (no iteration)74.2+9.0+9.0
C+ iteration (no attribution)75.4+1.2+1.2
D+ attribution (full SAGE)77.8+2.4+2.4
Table 2. Ablation on the 18-episode test set. Each row adds one component.
Figure 4. Quality on the test set over 10 evolution rounds. Both settings share the same round-1 rule set at 74.2. With attribution, quality rises to 77.8 by round 5 and stays within 77.4 to 77.7 through round 10; without attribution, it peaks at 75.4 in round 3 and degrades to 73.8 by round 10. A line chart with evolution round from 1 to 10 on the horizontal axis and overall quality score from 73 to 78 on the vertical axis. Both lines start together at 74.2, marked by a dotted horizontal reference line. The solid line for the setting with attribution climbs to a peak of 77.8 at round 5 and then stays close to that level. The dashed line for the setting without attribution peaks at 75.4 at round 3 and then declines to 73.8, ending below the starting level.
Figure 5. Cross-model transfer of the consolidated knowledge base, evolved entirely with Claude Opus 4.6. Injecting the unchanged scenario packages improves every transfer target; labels above the SAGE bars report relative improvements over Vanilla. A grouped bar chart over three backbone models, namely GPT-5.4, GLM-5.2, and DeepSeek V4-Pro. Each model has a Vanilla bar and a bar for the same model with SAGE rules injected. The Vanilla scores are 71.6, 60.4, and 59.0. The scores with SAGE rules are 77.4, 71.7, and 71.5. Relative improvements of 8.1 percent, 18.7 percent, and 21.2 percent are printed above the second bar of each group.
DimensionClaude vs. human consensusInter-human
Shot rhythm0.7170.689
Visual description0.8930.664
Shot scale0.6740.708
Camera angle0.8730.809
Camera movement0.8730.627
Mean0.8060.699
Table 3. Scorer validity on 18 director storyboards: agreement of Claude Opus 4.6 with the consensus of three professional directors, vs. inter-human agreement, measured by Lin’s CCC.
 Prod. AProd. BProd. COverall
Narrative group outputs5594533321,344
Accepted outputs4603803321,172
Acceptance (%)82.383.9100.087.2
Table 4. Production acceptance on three ongoing dramas. Each output corresponds to one narrative group and is a segment that may contain multiple shots.
Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Figure 6. Rule consolidation. (a) UMAP projection of rule-condition embeddings, colored by cluster. (b) A representative scenario package shown in translation: a routable natural-language description over rules grouped by professional dimension. The package contains 38 rules in total; representative rules are shown for space. Panel (a) is a two-dimensional UMAP scatter plot of rule condition embeddings. Points form many small compact groups, each drawn in a distinct color, separated by empty space. Panel (b) shows one scenario package as a document. A header names the package and gives its applicability description, the rule count of 38, the three contributing series, and the rule counts per dimension. Three framed entries follow, one each for camera angle, shot scale, and shot rhythm, and each entry states a condition and a practice. A footer notes that 35 further rules are omitted.
Figure 7. A real routing example from Beyond the Wall EP001, a held-out test episode, translated. Each shot is marked with the primary package whose rules it adopted, and one representative rule per package is shown. A left to right flow in three columns. The left column describes one narrative group, a two-person confrontation, with its dialogue lines. The middle column lists the three scenario packages it was routed to, each with a identifier, a name, a condition, and a practice. The right column shows the generated shots, each labeled with its shot scale, camera angle, and camera movement. Colored connecting lines trace which package informed which shot.
Figure 8. The skill file behind falignf_{\mathrm{align}} , translated and condensed to fit the column. Section headings and the wording of every directive follow the original. The director storyboard is the gold standard and stays hidden from the generator. A framed transcript of the alignment review skill file, set in a monospaced font under a colored title bar. Sections appear in order. A role section casts the reader as a storyboard review expert. A review dimensions section enumerates the five professional dimensions and instructs the reviewer to ignore table format, column order, and wording. Further sections state the scoring procedure and the report format.
Figure 9. The scoring prompt behind fqualf_{\mathrm{qual}} , translated and condensed to fit the column. The five bands are shown for the rhythm dimension; the other four share the same structure and are abridged to their core criteria. A framed transcript of the quality scoring prompt, set in a monospaced font under a colored title bar. A role section casts the reader as a senior storyboard director and states that no reference storyboard is available. A grounding paragraph lists Murch's six criteria for editing with their weights, the function of a storyboard, and the traits of short drama. A score anchors section then gives five bands of twenty points for the rhythm dimension, each band stating the observable evidence expected at that level.
More in this topic

Visual Storytelling

See all 23 papers

Explore similar work

CardsList
Jan 13, 2026

STAGE: A Full-Screenplay Benchmark for Reasoning over Evolving Stories

Movie screenplays are rich long-form narratives that interleave complex character relationships, temporally ordered events, and dialogue-driven interactions. While prior benchmarks target individual subtasks such as question answering or dialogue generation, they rarely evaluate whether models can construct a coherent story world and use it consistently across multiple forms of reasoning and generation. We introduce STAGE (Screenplay Text, Agents, Graphs and Evaluation), a unified benchmark for narrative understanding over full-length movie screenplays. STAGE defines four tasks: knowledge graph construction, scene-level event summarization, long-context screenplay question answering, and in-script character role-playing, all grounded in a shared narrative world representation. The benchmark provides cleaned scripts, curated knowledge graphs, and event- and character-centric annotations for 150 films across English and Chinese, enabling holistic evaluation of models' abilities to build world representations, abstract and verify narrative events, reason over long narratives, and generate character-consistent responses.

Qiuyu Tian, Zequn Liu, Yiding Li +9

Southeast University, Nanjing, China · Beijing Zhongguancun Academy, Beijing, China · ZhuiWen Technology Co., Ltd., Beijing, China +1

KG ConstructionLLM EvaluationLong-Context QA
AskPDF
Sep 7, 2026

Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation

Long-form narrative-to-film generation requires shot-level controllability and cross-clip consistency in both visual identity and character behavior-requirements that remain difficult to satisfy with current prompt-based workflows. A core reason existing workflows remain brittle is the lack of a structured intermediate layer between scripts and video models, especially when screenplays are underspecified at key cinematic decision points. We introduce a structured orchestration layer for film-oriented script-to-video generation, implemented as a multi-agent framework that operates between scripts and off-the-shelf video generators. The layer is centered on FilmDSL, a film-oriented domain-specific language that makes cinematic constraints explicit, including shot and camera directives, asset and continuity requirements, and persona cues, so that agents coordinate through a shared structured specification for planning, generation, critique, and repair. Specifically, a generation agent constructs asset packs and storyboard keyframes that anchor composition before clip-by-clip synthesis, while a critic agent produces structured QA signals and triggers targeted refinement without retraining the base model. Experiments on TV-style segments show improved controllability and consistency over text-only and reference-only baselines.

Jiaben Chen, Sixun Dong, Qinhong Zhou +4

University of Massachusetts Amherst, Amherst, MA 01003, USA · Massachusetts Institute of Technology, Cambridge, MA 02139, USA

Identity-Preserving Video GenerationLong Video GenerationAgentic Video GenerationT2V GenerationMulti-Shot Video GenerationTemporal Consistency in Video Generation+1
AskPDF
Aug 30, 2026

CineForge: Self-Improving Agents for Long-Horizon Video Generation

Long-horizon story-driven video generation requires a production agent to coordinate narrative decomposition, state tracking, shot design, prompt construction, rendering, and revision across interdependent scenes. Existing adaptive video systems primarily refine requests or reusable skills, leaving recurring production failures disconnected from persistent, stage-targeted improvements across stories. We introduce CineForge, a self-evolving video-production agent framework that couples CineForge-Produce for video generation with CineForge-Evolve for cross-story policy evolution. CineForge-Produce organizes each source story into typed narrative, character, spatial, and cinematic states, uses them to coordinate asset and clip generation, and records the process as a canonical production trajectory. CineForge-Evolve applies Case-to-Pattern-to-Policy Evolution (CPPE) to review trajectory evidence, consolidate recurrent findings into bounded stage-local patches, and deploy validated updates through structural replay and confidence-controlled paired evaluation. To measure complete story realization, we introduce CineScope, which combines a 100-script CineScope-Data suite with a human-aligned, multiscale CineScope-Metric spanning causal state, directorial orchestration, pacing and resource allocation, and character arc. Across CineScope-Data and two public benchmarks, the evolved CineForge policy improves CineScope-Metric from 4.024 to 4.380, outperforms three long-video baselines with consistent gains under ScriptAgent, and reduces review LLM calls by 37.0% on new stories. These results establish production trajectories as actionable experience for video agents that improve cumulatively across long-form storytelling tasks.

Junxiang Liu, Lin Wang, Haiyu Shi +10

Tongji University, Shanghai, China · Kuaishou Technology, Beijing, China · Fudan University, Shanghai, China

Long-Horizon Video GenerationAgentic Video GenerationMulti-Shot Video GenerationSelf-Improving AgentsLong-Horizon LLM AgentsVideo Generation Evaluation
AskPDF