Step 4 of the PaperOrchestra pipeline (arXiv:2604.05018). ONE single multimodal LLM call that drafts the remaining paper sections (Abstract, Methodology, Experiments, Conclusion), extracts numeric values from experimental_log.md into LaTeX booktabs tables, splices the generated figures from Step 2, and merges everything into the template that already contains Intro + Related Work from Step 3. TRIGGER when the orchestrator delegates Step 4 or when the user asks to "write the methodology and experiments sections" or "fill in the rest of the paper".
Bioinformatics workflows — RNA-seq and scRNA-seq analysis pipelines, enrichment analysis (GO/KEGG/GSEA), variant interpretation, protein structure analysis, and key database queries. Use when analyzing genomic, transcriptomic, or proteomic data.
Experimental and ecological biology — experimental design with controls/replicates, biology-specific statistical tests, diversity indices, cell biology assays (IC50, ELISA, flow cytometry), imaging analysis, and survival analysis. Use when working with biological experimental data.
Causal inference methods — DAG-based causal thinking, distinguishing observational from experimental data, IV, DiD, RDD, propensity score matching, and sensitivity analysis. Use when making causal claims from data.
Cheminformatics and computational chemistry — SMILES/InChI parsing, molecular property prediction, spectroscopy interpretation, DFT workflow, materials characterization (XRD, SAXS), and key chemistry databases. Use when analyzing chemical or materials data.
CS theory for research — algorithm complexity analysis, data structure selection, rigorous benchmarking discipline, distributed systems fundamentals, and formal verification concepts. Use when reasoning about algorithmic correctness, efficiency, or system design.
Computer vision workflows — image data characterization, preprocessing and augmentation, architecture selection (CNN vs ViT), and evaluation metrics (mAP, IoU, FID, SSIM). Use when working with image or video data.
Engineering systems analysis — control theory (PID, transfer functions, Bode plots), signal processing, reliability engineering, engineering optimization (LP/MIP), sensor data processing, and FEA concepts. Use when working with engineering, control, or sensor data.