&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](https://exa.ai) 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. ```bash 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. ```bash 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): ```bash 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: ```bash 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: ```bash 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): ```bash 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: ```bash 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: ```bash 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: ```bash 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: ```bash 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 ```bash 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` / `@misc` entries — never invents fields. It also writes the canonical `bibtex_key` back into each paper record in `citation_pool.json`. **Immediately after bibtex_format.py**, sync keys in `intro_relwork.tex`: ```bash 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 ```bash 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

--- 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= &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](https://exa.ai) 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. ```bash 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. ```bash 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): ```bash 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: ```bash 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: ```bash 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): ```bash 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: ```bash 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: ```bash 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: ```bash 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: ```bash 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 ```bash 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` / `@misc` entries — never invents fields. It also writes the canonical `bibtex_key` back into each paper record in `citation_pool.json`. **Immediately after bibtex_format.py**, sync keys in `intro_relwork.tex`: ```bash 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 ```bash 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)