Accept any colour-picker format for the accent; replace examples with five stronger test-set posters

- normalize_accent(): #RGB/#RRGGBB/rgb()/rgba()/hsl() -> #RRGGBB, default on failure
(the picker could send rgba(...) and the old check raised 'Choose an accent color in #RRGGBB format')
- Examples: MAESTRO (ICLR 2023), Gradient Leakage (ICLR 2022), Humanoid Motion (ICLR 2024),
NExT-GPT (ICML 2024), ConforMix (NeurIPS 2025), each with PDF, PNG and self-contained HTML

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>

examples/{neurips_2024_4032 → 01_iclr_2023_11250}/meta.json

@@ -1,8 +1,8 @@
 {
- "title": "DiffuLT: Diffusion for Long-tail Recognition Without External Knowledge",
- "venue": "NeurIPS 2024",
- "paper_id": "NeurIPS_2024_4032",
- "short": "DiffuLT · NeurIPS 2024",
+ "title": "MAESTRO: Open-Ended Environment Design for Multi-Agent Reinforcement Learning",
+ "short": "MAESTRO · ICLR 2023",
+ "venue": "ICLR 2023",
+ "paper_id": "ICLR_2023_11250",
  "instruction": "Create a clear academic poster with a strong title, a short motivation, the main method, key quantitative results, and takeaways. Prioritize the most informative figures. Use concise text, readable fonts, and generous spacing. Do not invent results.",
  "canvas": "Landscape · 3:2",
  "columns": 3,

examples/{icml_2022_16583 → 01_iclr_2023_11250}/paper.pdf

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:ce8ae89247f386c4fa0f7bc72a70ab0eda1cf3cd0ba3e4371f8922c3355ea01d
-size 1586267
+oid sha256:0bf00ecfdd12ea586519eaf60a095f4d4bff6b94f00e11c8a631e8dca5156051
+size 3841999

examples/{iclr_2025_5006 → 02_iclr_2022_6934}/meta.json

@@ -1,8 +1,8 @@
 {
- "title": "Matcha: Mitigating Graph Structure Shifts with Test-Time Adaptation",
- "venue": "ICLR 2025",
- "paper_id": "ICLR_2025_5006",
- "short": "Matcha · ICLR 2025",
+ "title": "Bayesian Framework for Gradient Leakage",
+ "short": "Gradient Leakage · ICLR 2022",
+ "venue": "ICLR 2022",
+ "paper_id": "ICLR_2022_6934",
  "instruction": "Create a clear academic poster with a strong title, a short motivation, the main method, key quantitative results, and takeaways. Prioritize the most informative figures. Use concise text, readable fonts, and generous spacing. Do not invent results.",
  "canvas": "Landscape · 3:2",
  "columns": 3,

examples/{iclr_2025_5006 → 02_iclr_2022_6934}/paper.pdf

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b602b23c819046ab875dca439c16e512d799c1c89f3340e5bf11e24c51723198
-size 3939092
+oid sha256:63747365ae12a267e0add5f75eb122217cd2f05e49eff94b68e89e385c7e738f
+size 1653739

examples/{icml_2022_18241 → 03_iclr_2024_18728}/meta.json

@@ -1,8 +1,8 @@
 {
- "title": "Discovering Generalizable Spatial Goal Representations via Graph-based Active Reward Learning",
- "venue": "ICML 2022",
- "paper_id": "ICML_2022_18241",
- "short": "GEM · ICML 2022",
+ "title": "Universal Humanoid Motion Representations for Physics-Based Control",
+ "short": "Humanoid Motion · ICLR 2024",
+ "venue": "ICLR 2024",
+ "paper_id": "ICLR_2024_18728",
  "instruction": "Create a clear academic poster with a strong title, a short motivation, the main method, key quantitative results, and takeaways. Prioritize the most informative figures. Use concise text, readable fonts, and generous spacing. Do not invent results.",
  "canvas": "Landscape · 3:2",
  "columns": 3,

examples/{icml_2022_18241 → 03_iclr_2024_18728}/paper.pdf

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e3ca777da75c8b2f5e263786baec579cb877036fe5c0067d39aed04f8a921b60
-size 1344934
+oid sha256:cd435bb5c24db5ce04c4d05376903ec95fdf4c57d27b9e976d8d01360ba6649c
+size 7600191

examples/{icml_2022_16583 → 04_icml_2024_34200}/meta.json

@@ -1,8 +1,8 @@
 {
- "title": "Adaptive Second Order Coresets for Data-efficient Machine Learning",
- "venue": "ICML 2022",
- "paper_id": "ICML_2022_16583",
- "short": "AdaCore · ICML 2022",
+ "title": "NExT-GPT: Any-to-Any Multimodal LLM",
+ "short": "NExT-GPT · ICML 2024",
+ "venue": "ICML 2024",
+ "paper_id": "ICML_2024_34200",
  "instruction": "Create a clear academic poster with a strong title, a short motivation, the main method, key quantitative results, and takeaways. Prioritize the most informative figures. Use concise text, readable fonts, and generous spacing. Do not invent results.",
  "canvas": "Landscape · 3:2",
  "columns": 3,

examples/{neurips_2022_63389 → 04_icml_2024_34200}/paper.pdf

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:21c8594705d06c371d6c320bf1d720bba3fcb4d375930ff57a88ed148bc53b07
-size 267114
+oid sha256:f04f130acd112fa73b1d762ab00da69fad254ec3a6e48a39a5367b2693b7846c
+size 9056510

examples/05_neurips_2025_17956/meta.json

@@ -0,0 +1,10 @@
+{
+ "title": "Unlocking Hidden Biomolecular Conformational Landscapes in Diffusion Models at Inference Time",
+ "short": "ConforMix · NeurIPS 2025",
+ "venue": "NeurIPS 2025",
+ "paper_id": "NeurIPS_2025_17956",
+ "instruction": "Create a clear academic poster with a strong title, a short motivation, the main method, key quantitative results, and takeaways. Prioritize the most informative figures. Use concise text, readable fonts, and generous spacing. Do not invent results.",
+ "canvas": "Landscape · 3:2",
+ "columns": 3,
+ "accent": "#584287"
+}

examples/05_neurips_2025_17956/paper.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a07e79ac27221cc4e746f2d347589db903481ce21520388624cf4d2701e8a0de
+size 24473079

examples/neurips_2022_63389/meta.json

@@ -1,10 +0,0 @@
-{
- "title": "Optimized Global Perturbation Attacks For Brain Tumour ROI Extraction From Binary Classification Models",
- "venue": "NeurIPS 2022",
- "paper_id": "NeurIPS_2022_63389",
- "short": "Global Perturbation · NeurIPS 2022",
- "instruction": "Create a clear academic poster with a strong title, a short motivation, the main method, key quantitative results, and takeaways. Prioritize the most informative figures. Use concise text, readable fonts, and generous spacing. Do not invent results.",
- "canvas": "Landscape · 3:2",
- "columns": 3,
- "accent": "#584287"
-}

examples/neurips_2024_4032/paper.pdf

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:25e258b685407444c4f181dc019427dc2ad6d5590eab72af7ebddc203f1e1da0
-size 2809890

pdf_pipeline.py

@@ -23,6 +23,30 @@ CANVASES = {'Landscape · 3:2': (2400,1600), 'Landscape · 16:9':(2400,1350),
 def scrub(value):
     return re.sub(r'<[^>]{0,80}>',lambda m:m.group(0).replace('<','‹').replace('>','›'),value or '')
 
+def normalize_accent(value, default='#584287'):
+    """Accept whatever the colour picker sends (#RGB, #RRGGBB, rgb()/rgba(), hsl()) and return #RRGGBB."""
+    v=str(value or '').strip()
+    m=re.fullmatch(r'#?([0-9a-fA-F]{6})',v)
+    if m: return '#'+m.group(1).lower()
+    m=re.fullmatch(r'#?([0-9a-fA-F]{3})',v)
+    if m: return '#'+''.join(c*2 for c in m.group(1)).lower()
+    m=re.fullmatch(r'rgba?\(([^)]*)\)',v,re.I)
+    if m:
+        try:
+            parts=[p.strip() for p in m.group(1).replace('/',',').split(',')][:3]
+            rgb=[max(0,min(255,round(float(p[:-1])*2.55 if p.endswith('%') else float(p)))) for p in parts]
+            if len(rgb)==3: return '#%02x%02x%02x'%tuple(rgb)
+        except ValueError: pass
+    m=re.fullmatch(r'hsla?\(([^)]*)\)',v,re.I)
+    if m:
+        try:
+            import colorsys
+            parts=[p.strip().rstrip('%') for p in m.group(1).replace('/',',').split(',')][:3]
+            h,sat,l=float(parts[0])/360.0,float(parts[1])/100.0,float(parts[2])/100.0
+            r,g,b=colorsys.hls_to_rgb(h,l,sat); return '#%02x%02x%02x'%(round(r*255),round(g*255),round(b*255))
+        except (ValueError,IndexError): pass
+    return default
+
 def prepare(pdf, job, instruction, canvas, accent, columns, custom_spec=''):
     from layout_runtime import extract
     pdf=Path(pdf);job=Path(job)
@@ -33,8 +57,7 @@ def prepare(pdf, job, instruction, canvas, accent, columns, custom_spec=''):
             raise ValueError('The uploaded file is not a PDF.')
     if len(instruction)>4000 or len(custom_spec)>3000:
         raise ValueError('Please shorten your design instruction / layout specification.')
-    if not re.fullmatch(r'#[0-9a-fA-F]{6}',accent):
-        raise ValueError('Choose an accent color in #RRGGBB format.')
+    accent=normalize_accent(accent)
     columns=max(1,min(4,int(columns))); width,height=CANVASES[canvas]
     extracted=job/'paper'
     extract(pdf, extracted)