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Academic Paper

Casebook Ivory — Bluebook Holding Statement

A law-journal slide structured around a Bluebook citation header and bold-serif holding statement, with numbered argument paragraphs, superscript footnote markers, and a hairline-ruled footnote block that mirrors the typographic discipline of a printed Harvard Law Review page.

This template treats the slide as a printed page, not a screen object — it borrows its entire visual grammar from American law-review typography: Bluebook citation strings, indented numbered paragraphs, and a hairline rule separating argument from authority. It refuses decorative imagery, colour washes, and bullet-point compression, because legal argument lives in structured prose, not fragments. The oxblood holding statement is the sole chromatic gesture, signalling where doctrine is declared without editorialising further. Built for speakers who need their slides to withstand the same scrutiny as the brief itself.

Palette — Warm Document

Prompt — Preview 1

Open in:ClaudeChatGPTGemini

Good for

  • · Moot court and appellate argument overviews
  • · Law school seminar case-analysis presentations
  • · Legal-brief companion slides with full Bluebook citation strings
  • · Academic conference panels dissecting specific court holdings

Avoid for

  • · Chart-heavy quarterly business or financial reviews
  • · Consumer-facing product or brand pitch decks
  • · Audiences unfamiliar with Bluebook or law-review citation conventions
  • · Fast-paced decks requiring visual variety and mixed layout types across slides

Tags

#bluebook-citation#legal-serif#footnote-rules#law-journal#holding-statement#hairline-rule

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Computer Modern — Theorem & Definition Scaffold

A LaTeX-faithful lecture canvas for math-heavy content: a thin royal-blue-ruled definition box at top, italic serif theorem statement, and parenthesized numbered proof steps — every glyph set in Computer Modern, exactly as the TeX tradition demands.

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Grand Rounds Dossier — Clinical Case Two-Pane

A journal-formatted grand-rounds slide dividing patient history from diagnostic imaging in a strict two-column layout, with a numbered differential list and a JAMA-style case header. Built for radiology and internal-medicine rounds where clinical evidence must live beside narrative without competing.

Academic Paper
PREPRINT · UNDER REVIEW AS A CONFERENCE PAPER AT NEURIPS 2026 Spectral Consistency Regularization for Low-Rank Adaptation in Diffusion Transformers Jane Doe1 A. Researcher1,2 B. Collaborator2 C. Reviewer3 D. Advisor1 1 Department of Computer Science, Example University 2 Institute for Machine Intelligence 3 Studio Research Lab { jane.doe, a.researcher } @ example.edu ABSTRACT We introduce Spectral Consistency Regularization (SCR), a training-time objective that constrains the singular-value spectrum of low-rank adapter updates to match a reference distribution induced by the pre-trained backbone. Given a frozen diffusion transformer fθ and a rank-r adapter ΔW = BA, we penalize the Wasserstein-2 distance between σ(ΔW) and the empirical spectrum of the corresponding backbone block, yielding a loss of the form ℒSCR = 𝔼[‖σ(BA) − σ*‖²]. The resulting adapters remain faithful to the base model's inductive priors while admitting task-specific deformation. Across four conditional generation benchmarks and three backbone scales (0.6B — 8.2B parameters), SCR recovers 94.1% of full fine-tuning FID at 0.41% of the trainable parameter budget, exceeding LoRA, DoRA, and orthogonal-init baselines by a consistent margin (Δ-FID ≥ 1.8). Ablations isolate the spectral term as the principal contributor and reveal that effective rank collapses monotonically without it. We release code, trained adapters, and evaluation protocols. Full derivations appear in Appendix A. Keywords — parameter-efficient fine-tuning · diffusion transformers · spectral regularization · low-rank adaptation · generative modeling arXiv:2604.01127v1 [cs.LG] 24 Apr 2026 NeurIPS 2026 · Submission #4412 · Page 1 / 18

Manuscript Grid — NeurIPS Two-Column Folio

A LaTeX-faithful two-column slide modelled on NeurIPS and ICML paper conventions, with justified serif body, centred numbered equations, and an inset results figure — the only ML-result template that treats typographic protocol as a design feature, not a constraint.

Academic Paper