DeepXiv Paper Search & Progressive Reading

Search topic or paper ID: $ARGUMENTS

Role & Positioning

DeepXiv is the progressive-reading literature source:

Use DeepXiv when you want to inspect papers incrementally instead of loading the full text immediately.

Constants

• FETCH_SCRIPT — $ARIS_REPO/tools/deepxiv_fetch.py from the ARIS repo recorded by the Codex install manifest. If unavailable, fall back to the raw deepxiv CLI.
• MAX_RESULTS = 10 — Default number of search results.

Overrides (append to arguments): - /deepxiv "agent memory" - max: 5 - /deepxiv "2409.05591" - brief - /deepxiv "2409.05591" - head - /deepxiv "2409.05591" - section: Introduction - /deepxiv "trending" - days: 14 - max: 10 - /deepxiv "karpathy" - web - /deepxiv "258001" - sc

Setup

DeepXiv is optional:

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:

• a paper topic, arXiv ID, or Semantic Scholar ID
• - max: N
• - brief
• - head
• - section: NAME
• - trending
• - days: 7|14|30
• - web
• - sc

If the input looks like an arXiv ID and no explicit mode is provided, default to brief.

Step 2: Locate the Adapter

Locate the adapter. Prefer the Codex managed install manifest when present, then fall back to the same project/global copy-install lookup style as the Claude skill:

ARIS_REPO="${ARIS_REPO:-$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills-codex.txt 2>/dev/null)}"
SCRIPT=""
[ -n "$ARIS_REPO" ] && [ -f "$ARIS_REPO/tools/deepxiv_fetch.py" ] && SCRIPT="$ARIS_REPO/tools/deepxiv_fetch.py"
[ -z "$SCRIPT" ] && [ -f tools/deepxiv_fetch.py ] && SCRIPT="tools/deepxiv_fetch.py"
[ -z "$SCRIPT" ] && [ -f ~/.codex/skills/deepxiv/deepxiv_fetch.py ] && SCRIPT="$HOME/.codex/skills/deepxiv/deepxiv_fetch.py"
[ -n "$SCRIPT" ] && python3 "$SCRIPT" --help

If the adapter is unavailable, fall back to raw deepxiv commands.

Step 3: Execute the Minimal Command

[ -n "$SCRIPT" ] && python3 "$SCRIPT" search "QUERY" --max MAX_RESULTS
[ -n "$SCRIPT" ] && python3 "$SCRIPT" paper-brief ARXIV_ID
[ -n "$SCRIPT" ] && python3 "$SCRIPT" paper-head ARXIV_ID
[ -n "$SCRIPT" ] && python3 "$SCRIPT" paper-section ARXIV_ID "SECTION_NAME"
[ -n "$SCRIPT" ] && python3 "$SCRIPT" trending --days 7 --max MAX_RESULTS
[ -n "$SCRIPT" ] && python3 "$SCRIPT" wsearch "QUERY"
[ -n "$SCRIPT" ] && python3 "$SCRIPT" sc "SEMANTIC_SCHOLAR_ID"

Fallbacks:

deepxiv search "QUERY" --limit MAX_RESULTS --format json
deepxiv paper ARXIV_ID --brief --format json
deepxiv paper ARXIV_ID --head --format json
deepxiv paper ARXIV_ID --section "SECTION_NAME" --format json
deepxiv trending --days 7 --limit MAX_RESULTS --output json
deepxiv wsearch "QUERY" --output json
deepxiv sc "SEMANTIC_SCHOLAR_ID" --output json

Step 4: Present Results

For search results, present a compact literature table. For paper reads, summarize the title, authors, date, TLDR, and the next recommended depth step.

Step 5: Escalate Depth Only When Needed

Use the progression:

1. search
2. paper-brief
3. paper-head
4. paper-section

Only read the full paper when the user explicitly needs it.

Step 6: Update Research Wiki (if active)

If the project has an active research wiki and the user is building a literature set, add DeepXiv findings as source-backed entries with arXiv/Semantic Scholar IDs, retrieved sections, and the recommended next depth step.

Follow shared-references/integration-contract.md. If the wiki path or schema is unclear, ask before writing.

Key Rules

• Prefer the adapter script over raw deepxiv commands when available.
• If DeepXiv is missing, give the install command and suggest /arxiv or /research-lit "topic" - sources: web.
• Use DeepXiv as an additive source, not a replacement for existing ARIS literature tooling.
• If the result overlaps with a published venue paper from Semantic Scholar, keep the richer venue metadata in the final summary.