Jarvis Paper Interpreter

Problem

Recommender system and computational advertising papers often scatter method components, offline metrics, online gains, and architecture diagrams across sections. Turning them into reusable notes easily drops key indicators or forces method modules into overly specific or overly generic labels. Industrial papers from companies like Meta, ByteDance, and Alibaba are especially variable in metric naming and module structure, making generic summarizers hard to standardize.

How It Works

jarvis-paper-interpreter takes a paper_arxiv_id, reads the ar5iv HTML version first, and generates a Markdown analysis using a fixed template. The workflow covers retrieval, reading, extraction, templating, validation, and archiving, which fits batch organization of arXiv paper notes.

• Complete structure: title, TLDR, problems, methods, gains, architecture, and experiments are validated in order.
• Source-driven extraction: top-level category names and gain metrics are taken from the paper instead of hardcoded terms.
• Figure handling: Figure 1 is saved as {short_name}_Figure1.png and inserted under [Model Architecture].
• Failure handling: network retries occur 3 times, then the skill requests a PDF; if no clear architecture figure exists, it states that explicitly.

Boundaries

Use it for deep analysis of recommender system, computational advertising, or recommendation algorithm papers; do not use it when only a TLDR, abstract, or non-domain paper reading is needed.