literature-review-agent

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.