Literature Review Agent (Step 3)

Faithful implementation of the Hybrid Literature Agent from PaperOrchestra (Song et al., 2026, arXiv:2604.05018, §4 Step 3, App. D.3, App. F.1 p.46).

Cost: ~20–30 LLM calls. This is one of the two longest steps (the other is plotting). Wall-time floor is set by Semantic Scholar's 1 QPS verification limit.

Inputs

• workspace/outline.json — specifically intro_related_work_plan with the Introduction search directions and the 2-4 Related Work methodology clusters
• workspace/inputs/conference_guidelines.md — used to derive cutoff_date
• workspace/inputs/idea.md, workspace/inputs/experimental_log.md — for framing the Intro and grounding the Related Work positioning

Outputs

• workspace/citation_pool.json — verified Semantic Scholar metadata for every paper that survived verification
• workspace/refs.bib — BibTeX file generated from the verified pool
• workspace/drafts/intro_relwork.tex — drafted Introduction and Related Work sections, written into the template, with the rest of the template preserved verbatim

Two-phase pipeline (App. D.3)

PHASE 1 — Parallel Candidate Discovery
   For each search direction in introduction_strategy.search_directions:
   For each limitation_search_query in each related_work cluster:
     - Use the host's web search tool to discover up to ~10 candidate papers.
     - Run up to 10 discovery queries in parallel (host-permitting).
     - Collect (title, snippet, url) tuples — no verification yet.
   → PRE-DEDUP before Phase 2 (see Step 1.5 below)

PHASE 2 — Sequential Citation Verification (1 QPS, with cache)
   For each candidate (after pre-dedup), sequentially:
     0. Check s2_cache.json first (scripts/s2_cache.py --check).
        If HIT: use cached response, skip live S2 call. No throttle needed.
        If MISS: proceed with live request below.
     1. Query Semantic Scholar by title:
          GET https://api.semanticscholar.org/graph/v1/paper/search?query=<title>
              &fields=title,abstract,year,authors,venue,externalIds&limit=5
        (Public endpoint, no key. Throttle to 1 QPS for live requests only.)
     2. Store the S2 response in cache: s2_cache.py --store.
     3. Pick the top hit. Check Levenshtein title ratio against the original
        candidate title. If ratio < 70: discard.
     4. Bonus: if year and venue exactly align with hints, add a +5 point
        match-quality bonus.
     5. Require: abstract is non-empty.
     6. Require: paper.year (or month if known) strictly predates cutoff_date.
        Months default to day-1: e.g., "October 2024" → 2024-10-01.
     7. If all checks pass, add to verified pool.
   After all candidates are verified, dedup by Semantic Scholar paperId.

The host agent does the LLM/web work; the deterministic helpers in scripts/ do the math.

Step-by-step

0. Derive cutoff_date

Parse conference_guidelines.md for the submission deadline. The paper aligns research cutoff with venue submission deadline (App. D.1):

Encode as YYYY-MM-DD. Months default to day-1 (e.g., 2024-10-01).

1. Phase 1: Parallel Candidate Discovery

From outline.json:

• All introduction_strategy.search_directions (3-5 queries)
• For each cluster in related_work_strategy.subsections:
  • The cluster's sota_investigation_mission becomes a search query
  • All limitation_search_queries (1-3 each)

For each query, use your host's web search tool (e.g., WebSearch in Claude Code, @web in Cursor, the search tool in Antigravity). Collect the top ~10 candidates per query: title, abstract snippet, source URL.

If your host supports parallel sub-tasks, fire up to 10 concurrent search queries. If not, run sequentially — slower but functionally equivalent.

Optional: Exa as a Phase 1 backend

If your host has no native web search, OR you want a research-paper-focused backend with better signal-to-noise, you can use Exa via the bundled scripts/exa_search.py helper. It is opt-in and reads EXA_API_KEY from the environment — the repo never commits a key.

export EXA_API_KEY="your-key-here"   # get one at https://dashboard.exa.ai/
python skills/literature-review-agent/scripts/exa_search.py \
    --query "Sparse attention long context transformers" \
    --num-results 15 \
    --discovered-for "related_work[2.1]"

Output is a normalized candidate list ready to merge into raw_candidates.json. Phase 2 verification (Semantic Scholar fuzzy match, cutoff, dedup) is unchanged. See references/exa-search-cookbook.md for the full recipe, query patterns, cost estimates, and security notes.

Combine all discovered candidates into a single working list. Tag each with the originating query ID so you can later attribute it to "intro" vs "related_work[i]".

1.5. Pre-dedup before Phase 2

Always run this before starting Phase 2. Multiple search queries routinely return the same papers (e.g., "Attention is All You Need" appears in almost every NLP discovery query). Verifying duplicates wastes 30-40% of S2 quota at 1 QPS.

python skills/literature-review-agent/scripts/pre_dedup_candidates.py \
    --in workspace/raw_candidates.json \
    --out workspace/deduped_candidates.json
# Prints: "150 candidates → 97 unique (53 duplicates removed)"

Use workspace/deduped_candidates.json as input to Phase 2.

2. Phase 2: Sequential Verification via Semantic Scholar (with cache)

For each candidate in deduped_candidates.json, in sequential order:

Step A — check cache first (no S2 call, no throttle needed):

python skills/literature-review-agent/scripts/s2_cache.py \
    --cache workspace/cache/s2_cache.json \
    --check "<candidate title>"
# exit 0 + prints JSON → use cached response, skip Step B
# exit 1 → proceed to Step B

Step B — live S2 request (cache MISS only, throttle to 1 QPS):

Preferred: use the bundled scripts/s2_search.py helper — it handles auth, retries, and 429 back-off automatically:

python skills/literature-review-agent/scripts/s2_search.py \
    --query "<URL-decoded candidate title>" --limit 5
# If SEMANTIC_SCHOLAR_API_KEY is set the key is forwarded automatically.
# If not, the public unauthenticated endpoint is used (≤1 QPS, still works).

Check whether the key is configured before starting Phase 2:

python skills/literature-review-agent/scripts/s2_search.py --check-key

Fallback: if you prefer your host's URL fetch tool, GET:

https://api.semanticscholar.org/graph/v1/paper/search?query=<URL-encoded title>&limit=5&fields=title,abstract,year,authors,venue,externalIds

Add header x-api-key: <SEMANTIC_SCHOLAR_API_KEY> if the env var is set. Be polite: ≤1 request per second for live requests. Cache hits are free.

Step C — store in cache (after every successful live request):

python skills/literature-review-agent/scripts/s2_cache.py \
    --cache workspace/cache/s2_cache.json \
    --store "<candidate title>" \
    --response '<full S2 JSON response>'

For the top hit:

python skills/literature-review-agent/scripts/levenshtein_match.py \
    --candidate "Original candidate title" \
    --found "S2 returned title"
# prints integer 0-100. Discard if < 70.

Then check the temporal cutoff:

python skills/literature-review-agent/scripts/check_cutoff.py \
    --paper-year 2024 \
    --paper-month 9 \
    --cutoff 2024-10-01
# exit 0 if strictly predates, exit 1 if not

If both checks pass AND the abstract is non-empty, append the paper's full S2 metadata to the verified pool.

3. Dedup and assemble the pool

After all candidates are verified:

python skills/literature-review-agent/scripts/dedupe_by_id.py \
    --in raw_pool.json \
    --out workspace/citation_pool.json

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