Research: prior art for agent-authored stand-in hulls (render-critique loops, part libraries, self-judging limits)
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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# Prior art for agent-authored stand-in hulls (aetheria-release, 2026-10-19)
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Serves: the stand-in hull prompt bench for the demo cast and freelancer factions
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(operator 2026-10-19: "Let's see if we can figure out a prompt that gets Opus to
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spit out some decent stand-in hulls for the demo cast (including ships from
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non-present factions to be piloted by other freelancers)"). Gathered by Eyes
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pass `eyes-ag7-llm-cad-prior-art`, session `self-2026-10-19-ag7`. Companion to
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`2026-10-18-agent-built-hull-art-prior-art.md` and
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`2026-10-18-faction-form-language-prior-art.md`.
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Evidence levels: **read** (HTML or abstract page read), **snippet** (search
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excerpt only).
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## Loops and their measured gains
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- **SceneCraft** (ICML 2024, arXiv 2403.01248, read). Removing the render-review
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inner loop dropped constraint score 88.9 to 26.1, the largest loss in its
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ablation. Removing the learned code-skill library dropped it 88.9 to 64.5 and
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CLIP 69.8 to 48.3. Human eval put its aesthetics win rate at 74.5% against
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BlenderGPT's 14.5%. It measures scene layout from given assets, not part form.
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- **CADCodeVerify** (ICLR 2025, arXiv 2410.05340, read). Two refinement rounds;
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no gain observed beyond round 2. Point-cloud distance 0.156 to 0.126. Dropping
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the reference views or the few-shot questions made it worse. The VLM's own
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verification answers were correct only 64.6% and 68.2% of the time; human
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gold feedback (0.120) beat VLM feedback (0.137) on a 50-sample subset.
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- **BlenderGym** (CVPR 2025, arXiv 2504.01786, read). VLM verifier agreement
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with human pairwise judgments 0.66 (Claude 3.5 Sonnet) against 0.79
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human-to-human; Claude was the only verifier that consistently gave full
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reasoning. Scarce compute favours generation, abundant compute favours
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verification: a scaled small verifier over 32 Claude candidates beat unscaled
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GPT-4o and Claude. Generators hallucinate differences, and edits mismatch the
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diagnosis in scripts over about 80 lines. Metrics miss physical
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implausibility such as floating objects.
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- **LL3M** (arXiv 2508.08228, read). Planner, retrieval, coder, critic and
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verifier agents; the critic sees 5 renders, the verifier checks each fix was
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applied, refinement loops until the verifier approves. Blender-API retrieval
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raised complex-operation use 1.20 to 5.86 and cut execution errors 26%.
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Without retrieval the coder sometimes deleted offending lines to silence
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errors. Without shared prior code the coder regenerates a substantially
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different asset. Disconnected handles survive auto-refinement; VLMs struggle
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with spatial artifacts. Agent ablations are qualitative only; no user study.
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- **Procedura** (arXiv 2608.26238, read). Named parts with typed mates, per-part
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compile, mate and connectivity gates, and a vision critic that fixes one
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diagnosed issue at a time. Ablation on MechBench-36 (judge score): full 0.828,
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no refine 0.800, no planning 0.791, no 3D feedback 0.819; dropping planning
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caused the biggest aesthetics drop, 0.827 to 0.775. A floater at 70% span was
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1.9% of volume, so a volume check misses it. Case study: 24 to 3 to 2 HIGH
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issues over three critic cycles. CSG suits hard-surface better than organic.
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- **P3D-Bench** (arXiv 2606.11152, abstract). Multi-part code generation is much
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harder than single-part: models get identity right, dimensions, part count
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and part geometry wrong.
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- **ModelRift Pantheon benchmark** (vendor blog, one run per cell, read). Opus
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4.7 scored 3.0/5 with "uniform colour and weight"; Sonnet 4.6 3.4/5 with the
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cleanest massing. Explicit measured dimensions helped; human annotations drawn
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on the render beat text corrections. A preview PNG looked better than the
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exported STL, which had non-manifold surfaces.
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## Self-judgement
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- Huang et al., ICLR 2024 (arXiv 2310.01798, snippet): intrinsic
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self-correction without external feedback often fails and can hurt; reported
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gains partly came from oracle labels or weak first prompts.
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- Panickssery et al., NeurIPS 2024 (arXiv 2404.13076, snippet): LLM evaluators
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recognise and favour their own generations; text domain.
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- Multimodal self-judging leans on text priors over the image (arXiv 2406.11280,
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2508.20655, snippets).
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## Failure modes in CAD code
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- BenchCAD and ASSEMCAD (snippets): successful renders still show floating
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components; union without face glue gives floating subpieces; polar patterns
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are an error cluster. ASSEMCAD mitigates with safe-union and overlap
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validators.
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- Shape-grammar ships (CESCG 2016; 2022 Hacettepe thesis; snippets): primitive
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sets plus rules give similar outlines with varied detail; unconstrained
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derivation tends toward unstructured results.
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## Game-art practice (criteria a critic can check)
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- Stellaris Dev Diary 230 (search excerpt): silhouette and proportions read
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first at distance; big shapes settled before small; shared motifs unify a
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family.
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- Fractured Space art direction, Creative Bloq (search excerpt): an arrowhead
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silhouette reads as a threat across the map; three manufacturers each get a
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distinct vocabulary (stacked cylinders, brutalist cubes, naval modular
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shells); human figures dropped in as scale checks.
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- "Moth" analysis, John Liberto, ArtStation (search excerpt): 3 large, 4 mid
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and many small volumes; the large volumes dictate the silhouette.
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- Primary/secondary/tertiary shapes (steveyang.readthedocs.io, read, after Neil
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Blevins): primary defines silhouette, secondary individuality, tertiary story.
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## Gaps
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No controlled study of design-language or style-spec conditioning for
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LLM-authored CAD; no measurement of a parts library against floating-part or
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symmetry errors (nearest: SceneCraft's skill library, LL3M's retrieval); no
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sweep of render-critique round counts for aesthetic quality; no direct
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measurement of a model praising its own render (inferred from text-domain
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work); no source on boxy "lego" look or scale confusion in LLM 3D code; no
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authoritative game-art source for 60/30/10 ship proportions. 3D-PreMise
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(2401.06437) and MeshCoder (2508.14879) seen as abstracts only.
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