literature-review-agent

name

literature-review-agent

description

Step 3 of the PaperOrchestra pipeline (arXiv:2604.05018). Execute the literature search strategy from outline.json — discover candidate papers via web search, verify them through Semantic Scholar (Levenshtein > 70 fuzzy title match, temporal cutoff, dedup by paperId), build a BibTeX file, and draft Introduction + Related Work using ≥90% of the verified pool. Runs in parallel with the plotting-agent. TRIGGER when the orchestrator delegates Step 3 or when the user asks to "find citations for my paper", "draft the related work", or "build the bibliography".

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):

Venue	Cutoff
CVPR 2025	Nov 2024
ICLR 2025	Oct 2024
Other	One month before the stated submission deadline

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

The dedupe script keys on paperId (Semantic Scholar's internal unique ID), falling back to externalIds.DOI, then externalIds.ArXiv, then a normalized title.

The script also computes and writes min_cite_paper_count = floor(0.9 * len(papers)) — the minimum number of papers the writing step must cite (the paper's ≥90% integration rule, App. D.3).

Immediately after dedupe_by_id.py, validate and auto-fix the pool schema:

python skills/literature-review-agent/scripts/validate_pool.py \
    --pool workspace/citation_pool.json --fix
# Catches and fixes authors-as-strings, reports missing required fields.
# Must pass before proceeding to Step 4.

4. Build the BibTeX file

python skills/literature-review-agent/scripts/bibtex_format.py \
    --pool workspace/citation_pool.json \
    --out workspace/refs.bib

The script generates citation keys deterministically from `firstauthor + year

• first significant word of title(e.g.,vaswani2017attention). It writes out only @article/@inproceedings/@miscentries — never invents fields. It also writes the canonicalbibtex_keyback into each paper record incitation_pool.json`.

Immediately after bibtex_format.py, sync keys in intro_relwork.tex:

python skills/literature-review-agent/scripts/sync_keys.py \
    --pool workspace/citation_pool.json \
    --tex  workspace/drafts/intro_relwork.tex \
    --inplace
# Replaces every \cite{agent_key} with \cite{canonical_bibtex_key}.
# Eliminates citation_coverage gate failures caused by key mismatch.

These two steps replace the manual Python snippets that were previously required. The pipeline is now:

dedupe_by_id → validate_pool --fix → bibtex_format → sync_keys

5. Draft Introduction + Related Work

This is where you (the host agent) actually write text. Load the verbatim Literature Review Agent prompt at references/prompt.md. Substitute the template placeholders:

Placeholder	Value
intro_related_work_plan	full JSON object from outline.json
project_idea	contents of idea.md
project_experimental_log	contents of experimental_log.md
citation_checklist	the BibTeX keys from refs.bib
collected_papers	list of {key, title, abstract} from citation_pool.json
paper_count	len(citation_pool.papers)
min_cite_paper_count	from citation_pool.json
cutoff_date	the date you derived in Step 0

Also prepend the Anti-Leakage Prompt from ../paper-orchestra/references/anti-leakage-prompt.md.

Run your LLM with the combined prompt against template.tex. The agent's job is to fill in the empty Introduction and Related Work sections of the template and leave everything else untouched. Output: the full template.tex with those two sections filled. Save to workspace/drafts/intro_relwork.tex.

6. Verify ≥90% citation coverage

python skills/literature-review-agent/scripts/citation_coverage.py \
    --tex workspace/drafts/intro_relwork.tex \
    --pool workspace/citation_pool.json
# exit 0 if ≥90% of pool is cited; exit 1 otherwise

If the gate fails, re-prompt the writing step explicitly listing the missing keys and asking the agent to integrate them where contextually appropriate.

Critical rules from the prompt

These are excerpted from references/prompt.md. The host agent MUST honor them on the writing call:

• Cite ONLY from collected_papers. Never invent BibTeX keys, never reference papers not in the pool.
• Cite at least min_cite_paper_count of them in Intro + Related Work combined.
• TIMELINE RULE: Do not treat any papers published after cutoff_date as prior baselines to beat. They are concurrent work only.
• EVALUATION RULE: Do not claim our method beats / achieves SOTA over a specific cited paper UNLESS that paper is explicitly evaluated against in experimental_log.md. Frame other recent papers strictly as concurrent, orthogonal, or conceptual work.
• Output format: return the full code for the updated template.tex, with the two empty sections (Introduction and Related Work) filled in, and all the other code (packages, styles, other sections) identical to the original template.tex.
• Wrap output in ```latex ... ``` fences.
• Do not change \usepackage[capitalize]{cleveref} to cleverref (there is no cleverref.sty).

Degraded mode (no web search)

If your host has no web search tool, switch to degraded mode:

1. If the user has placed a pre-built workspace/inputs/refs.bib in the workspace, load it directly into workspace/refs.bib and skip Phase 1 and Phase 2.
2. Otherwise, emit workspace/drafts/intro_relwork.tex containing the template with two TODO markers in the Intro and Related Work sections, and tell the user the pipeline cannot complete Step 3 without web search.

Resources

• references/prompt.md — verbatim Literature Review Agent prompt from App. F.1
• references/discovery-pipeline.md — Phase 1 + Phase 2 explained in detail
• references/verification-rules.md — Levenshtein cutoff, year alignment, dedup
• references/citation-density-rule.md — the ≥90% integration rule
• references/s2-api-cookbook.md — Semantic Scholar URLs, fields, rate limits
• references/exa-search-cookbook.md — optional Exa backend for Phase 1 (research-paper-focused web search)
• scripts/pre_dedup_candidates.py — NEW dedup Phase 1 candidates before Phase 2 (saves 30-40% S2 quota)
• scripts/s2_cache.py — NEW persistent S2 response cache (eliminates re-verification on re-runs)
• scripts/validate_pool.py — NEW validate & auto-fix citation_pool.json schema (authors format)
• scripts/sync_keys.py — NEW sync cite keys in .tex with canonical bibtex_keys after bibtex_format.py
• scripts/levenshtein_match.py — fuzzy title match (ratio > 70)
• scripts/check_cutoff.py — date cmp w/ month → day-1 default
• scripts/dedupe_by_id.py — dedup verified pool by S2 paperId
• scripts/bibtex_format.py — build refs.bib from JSON pool
• scripts/citation_coverage.py — ≥90% citation coverage gate
• scripts/s2_search.py — NEW Semantic Scholar title-search helper; reads SEMANTIC_SCHOLAR_API_KEY from env (optional — falls back to unauthenticated)
• scripts/exa_search.py — optional Exa Phase 1 backend (reads EXA_API_KEY from env)