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Claude CodedocsData & ML5 files · +755 −163

Rewrite HyperCluster Euro-Par paper and generate figures with PaperBanana

Session from traceweave on Hugging Face by Samarth0710 · MIT · condensed by Coders Talk

38msession
0interventions
GOAL

Rewrite an existing LaTeX paper (Euro_Par_Hypercluster) to match a reference paper's structure/style, incorporate new codebase features, add placeholder tables/figures, then generate real figures with the PaperBanana tool and compile a final PDF.

  1. Prompt · +0
    Use the reference arXiv paper for structure/style, review the codebase for new features, update my old Euro_Par paper with tables and figure placeholders, then compile the PDF.
  2. Agent did · +0m
    Launched three parallel background agents to read the reference paper, the old paper, and the codebase, then synthesized findings into a plan for the rewrite.
  3. Agent did · +2m
    Rewrote samplepaper.tex in LLNCS format with 4 contributions (ring-pipeline, sharding, selective layer loading, weight-tying), added comparison table and figure placeholders.
  4. Agent did · +7m
    Ran pdflatex twice, checked warnings/errors, produced a clean 15-page PDF with no reference errors.
  5. Prompt · +15m
    Use paperbanana to generate all the required figures for the paper, add them, and recompile.
  6. Fail · +18m
    Figure generation script failed: wrong Python environment, then a hardcoded MEDIUM thinking-level config in visualizer.py caused a Gemini API error during plot generation.
  7. Agent did · +31m
    Fixed the thinking_config bug in visualizer.py, rewrote the generation script to skip the already-produced architecture diagram, and reran to generate memory and latency figures via PaperBanana.
  8. Fail · +37m
    AI-generated latency comparison plot code had a matplotlib bug and failed to render correctly.
  9. Agent did · +37m
    Manually wrote a clean latency_comparison.py script and ran it directly, producing working PDF/PNG output.
  10. Outcome · +38m
    Session ended mid-task: architecture, memory, and latency figures were generated, but the final recompile request hit a repeated API error (prompt token count exceeding the model's 128000 limit), leaving the PDF uncompiled with the new figures.
VERDICT

Next time I'd keep the session shorter and more scoped — splitting 'update the paper text' from 'generate all figures and recompile' into separate sessions so the context window doesn't balloon and kill the final compile step. Manually rewriting the latency plot script after PaperBanana's generated code failed is what actually got that figure working; the fix to the hardcoded thinking-level config in visualizer.py was necessary just to get any plots out at all.

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