Deepxiv

Overrides (append to arguments):

• /deepxiv "agent memory" - max: 5 — top 5 results
• /deepxiv "2409.05591" - brief — quick paper summary
• /deepxiv "2409.05591" - head — metadata + section overview
• /deepxiv "2409.05591" - section: Introduction — read one section only
• /deepxiv "trending" - days: 14 - max: 10 — trending papers
• /deepxiv "karpathy" - web — DeepXiv web search
• /deepxiv "258001" - sc — Semantic Scholar metadata by ID

Setup

DeepXiv is optional. If the CLI is not installed, tell the user:

pip install deepxiv-sdk

On first use, deepxiv auto-registers a free token and stores it in ~/.env.

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS for:

• Query or ID: a paper topic, arXiv ID, or Semantic Scholar ID
• - max: N: override MAX_RESULTS
• - brief: fetch paper brief
• - head: fetch metadata and section map
• - section: NAME: fetch one named section
• - trending or query trending: fetch trending papers
• - days: 7|14|30: trending time window
• - web: run DeepXiv web search
• - sc: fetch Semantic Scholar metadata by ID

If the main argument looks like an arXiv ID and no explicit mode is given, default to - brief.

Step 2: Locate the Adapter

Prefer the ARIS adapter:

python3 tools/deepxiv_fetch.py --help

If tools/deepxiv_fetch.py is not available, fall back to raw deepxiv commands.

Step 3: Execute the Minimal Command

Search papers

python3 tools/deepxiv_fetch.py search "QUERY" --max MAX_RESULTS

Fallback:

deepxiv search "QUERY" --limit MAX_RESULTS --format json

Brief summary

python3 tools/deepxiv_fetch.py paper-brief ARXIV_ID

Fallback:

deepxiv paper ARXIV_ID --brief --format json

Section map

python3 tools/deepxiv_fetch.py paper-head ARXIV_ID

Fallback:

deepxiv paper ARXIV_ID --head --format json

Specific section

python3 tools/deepxiv_fetch.py paper-section ARXIV_ID "SECTION_NAME"

Fallback:

deepxiv paper ARXIV_ID --section "SECTION_NAME" --format json

Trending

python3 tools/deepxiv_fetch.py trending --days 7 --max MAX_RESULTS

Fallback:

deepxiv trending --days 7 --limit MAX_RESULTS --output json

Web search

python3 tools/deepxiv_fetch.py wsearch "QUERY"

Fallback:

deepxiv wsearch "QUERY" --output json

Semantic Scholar metadata

python3 tools/deepxiv_fetch.py sc "SEMANTIC_SCHOLAR_ID"

Fallback:

deepxiv sc "SEMANTIC_SCHOLAR_ID" --output json

Step 4: Present Results

When searching, present a compact table:

| # | ID | Title | Year | Citations | Notes |
|---|----|-------|------|-----------|-------|

When reading a paper, show:

• title
• arXiv ID
• authors
• venue/date if available
• TLDR or abstract summary
• suggested next step: brief → head → section

Step 5: Escalate Depth Only When Needed

Use this progression:

1. search
2. paper-brief
3. paper-head
4. paper-section
5. full paper only if necessary

Do not jump to full-paper reads when a brief or one section answers the question.

Key Rules

• Prefer the adapter script over raw deepxiv commands when available.
• DeepXiv is optional. If unavailable, give the install command and suggest /arxiv or /research-lit "topic" - sources: web.
• Use section-level reads to save tokens.
• Treat DeepXiv as complementary to /arxiv and /semantic-scholar, not a replacement.
• If the result overlaps with a published venue paper from Semantic Scholar, keep the richer venue metadata in the final summary.