latex-posters
Create professional research posters in LaTeX using beamerposter, tikzposter, or baposter. Support for conference presentations, academic posters, and scientific communication. Includes layout design, color schemes, multi-column formats, figure integration, and poster-specific best practices for visual communication.
What this skill does
# LaTeX Research Posters
## Overview
Research posters are a critical medium for scientific communication at conferences, symposia, and academic events. This skill provides comprehensive guidance for creating professional, visually appealing research posters using LaTeX packages. Generate publication-quality posters with proper layout, typography, color schemes, and visual hierarchy.
## When to Use This Skill
This skill should be used when:
- Creating research posters for conferences, symposia, or poster sessions
- Designing academic posters for university events or thesis defenses
- Preparing visual summaries of research for public engagement
- Converting scientific papers into poster format
- Creating template posters for research groups or departments
- Designing posters that comply with specific conference size requirements (A0, A1, 36×48", etc.)
- Building posters with complex multi-column layouts
- Integrating figures, tables, equations, and citations in poster format
## AI-Powered Visual Element Generation
**STANDARD WORKFLOW: Generate ALL major visual elements using AI before creating the LaTeX poster.**
This is the recommended approach for creating visually compelling posters:
1. Plan all visual elements needed (title, intro, methods, results, conclusions)
2. Generate each element using scientific-schematics or Nano Banana Pro
3. Assemble generated images in the LaTeX template
4. Add text content around the visuals
**Target: 60-70% of poster area should be AI-generated visuals, 30-40% text.**
---
### CRITICAL: Preventing Content Overflow
**⚠️ POSTERS MUST NOT HAVE TEXT OR CONTENT CUT OFF AT EDGES.**
**Common Overflow Problems:**
1. **Title/footer text extending beyond page boundaries**
2. **Too many sections crammed into available space**
3. **Figures placed too close to edges**
4. **Text blocks exceeding column widths**
**Prevention Rules:**
**1. Limit Content Sections (MAXIMUM 5-6 sections for A0):**
```
✅ GOOD - 5 sections with room to breathe:
- Title/Header
- Introduction/Problem
- Methods
- Results (1-2 key findings)
- Conclusions
❌ BAD - 8+ sections crammed together:
- Overview, Introduction, Background, Methods,
- Results 1, Results 2, Discussion, Conclusions, Future Work
```
**2. Set Safe Margins in LaTeX:**
```latex
% tikzposter - add generous margins
\documentclass[25pt, a0paper, portrait, margin=25mm]{tikzposter}
% baposter - ensure content doesn't touch edges
\begin{poster}{
columns=3,
colspacing=2em, % Space between columns
headerheight=0.1\textheight, % Smaller header
% Leave space at bottom
}
```
**3. Figure Sizing - Never 100% Width:**
```latex
% Leave margins around figures
\includegraphics[width=0.85\linewidth]{figure.png} % NOT 1.0\linewidth
```
**4. Check for Overflow Before Printing:**
```bash
# Compile and check PDF at 100% zoom
pdflatex poster.tex
# Look for:
# - Text cut off at any edge
# - Content touching page boundaries
# - Overfull hbox warnings in .log file
grep -i "overfull" poster.log
```
**5. Word Count Limits:**
- **A0 poster**: 300-800 words MAXIMUM
- **Per section**: 50-100 words maximum
- **If you have more content**: Cut it or make a handout
---
### CRITICAL: Poster-Size Font Requirements
**⚠️ ALL text within AI-generated visualizations MUST be poster-readable.**
When generating graphics for posters, you MUST include font size specifications in EVERY prompt. Poster graphics are viewed from 4-6 feet away, so text must be LARGE.
**⚠️ COMMON PROBLEM: Content Overflow and Density**
The #1 issue with AI-generated poster graphics is **TOO MUCH CONTENT**. This causes:
- Text overflow beyond boundaries
- Unreadable small fonts
- Cluttered, overwhelming visuals
- Poor white space usage
**SOLUTION: Generate SIMPLE graphics with MINIMAL content.**
**MANDATORY prompt requirements for EVERY poster graphic:**
```
POSTER FORMAT REQUIREMENTS (STRICTLY ENFORCE):
- ABSOLUTE MAXIMUM 3-4 elements per graphic (3 is ideal)
- ABSOLUTE MAXIMUM 10 words total in the entire graphic
- NO complex workflows with 5+ steps (split into 2-3 simple graphics instead)
- NO multi-level nested diagrams (flatten to single level)
- NO case studies with multiple sub-sections (one key point per case)
- ALL text GIANT BOLD (80pt+ for labels, 120pt+ for key numbers)
- High contrast ONLY (dark on white OR white on dark, NO gradients with text)
- MANDATORY 50% white space minimum (half the graphic should be empty)
- Thick lines only (5px+ minimum), large icons (200px+ minimum)
- ONE SINGLE MESSAGE per graphic (not 3 related messages)
```
**⚠️ BEFORE GENERATING: Review your prompt and count elements**
- If your description has 5+ items → STOP. Split into multiple graphics
- If your workflow has 5+ stages → STOP. Show only 3-4 high-level steps
- If your comparison has 4+ methods → STOP. Show only top 3 or Our vs Best Baseline
**Content limits per graphic type (STRICT):**
| Graphic Type | Max Elements | Max Words | Reject If | Good Example |
|--------------|--------------|-----------|-----------|--------------|
| Flowchart | **3-4 boxes MAX** | **8 words** | 5+ stages, nested steps | "DISCOVER → VALIDATE → APPROVE" (3 words) |
| Key findings | **3 items MAX** | **9 words** | 4+ metrics, paragraphs | "95% ACCURATE" "2X FASTER" "FDA READY" (6 words) |
| Comparison chart | **3 bars MAX** | **6 words** | 4+ methods, legend text | "OURS: 95%" "BEST: 85%" (4 words) |
| Case study | **1 case, 3 elements** | **6 words** | Multiple cases, substories | Logo + "18 MONTHS" + "to discovery" (2 words) |
| Timeline | **3-4 points MAX** | **8 words** | Year-by-year detail | "2020 START" "2022 TRIAL" "2024 APPROVED" (6 words) |
**Example - WRONG (7-stage workflow - TOO COMPLEX):**
```bash
# ❌ BAD - This creates tiny unreadable text like the drug discovery poster
python scripts/generate_schematic.py "Drug discovery workflow showing: Stage 1 Target Identification, Stage 2 Molecular Synthesis, Stage 3 Virtual Screening, Stage 4 AI Lead Optimization, Stage 5 Clinical Trial Design, Stage 6 FDA Approval. Include success metrics, timelines, and validation steps for each stage." -o figures/workflow.png
# Result: 7+ stages with tiny text, unreadable from 6 feet - POSTER FAILURE
```
**Example - CORRECT (simplified to 3 key stages):**
```bash
# ✅ GOOD - Same content, split into ONE simple high-level graphic
python scripts/generate_schematic.py "POSTER FORMAT for A0. ULTRA-SIMPLE 3-box workflow: 'DISCOVER' → 'VALIDATE' → 'APPROVE'. Each word in GIANT bold (120pt+). Thick arrows (10px). 60% white space. NO substeps, NO details. 3 words total. Readable from 10 feet." -o figures/workflow_overview.png
# Result: Clean, impactful, readable - can add detail graphics separately if needed
```
**Example - WRONG (complex case studies with multiple sections):**
```bash
# ❌ BAD - Creates cramped unreadable sections
python scripts/generate_schematic.py "Case studies: Insilico Medicine (drug candidate, discovery time, clinical trials), Recursion Pharma (platform, methodology, results), Exscientia (drug candidates, FDA status, timeline). Include company logos, metrics, and outcomes." -o figures/cases.png
# Result: 3 case studies with 4+ elements each = 12+ total elements, tiny text
```
**Example - CORRECT (one case study, one key metric):**
```bash
# ✅ GOOD - Show ONE case with ONE key number
python scripts/generate_schematic.py "POSTER FORMAT for A0. ONE case study card: Company logo (large), '18 MONTHS' in GIANT text (150pt), 'to discovery' below (60pt). 3 elements total: logo + number + caption. 50% white space. Readable from 10 feet." -o figures/case_single.png
# Result: Clear, readable, impactful. Make 3 separate graphics if you need 3 cases.
```
**Example - WRONG (key findings too complex):**
```bash
# BAD - too many items, too much detail
python scripts/generate_schematic.py "Key findings showing 8 metrics: accuracy 95%, precision 92%, recall 94%, F1 0.93, AUC 0.97, training time 2.3 hours, inference 50ms, mRelated in Design
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