feat: quant_scholar.py arxiv fetcher + re-enable cron#84
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) QuantMind v0.2 ships ingestion + LLM extraction only; its persistence, embedding, semantic-query, and Data-MCP layers are unbuilt future PRs. This adds that missing Stage-2 layer as a self-contained package that reuses QuantMind's own venv and fetch+format layer: - store.py filesystem CorpusStore (JSON + .npy vectors, stable-hash dedup) - embed.py OpenAI embeddings + grounded answer synthesis + summarizer - ingest.py fetch_arxiv/url/local -> markdown -> summarize -> embed -> store (skips the brittle paper_flow Paper-tree: gpt-4o-mini emits non-UUID node ids that the Paper schema rejects) - query.py embed question -> cosine top-k -> grounded, cited answer - server.py FastMCP stdio server: qm_ingest_arxiv/url/pdf/text, qm_query, qm_list_corpus, qm_delete_item - cli.py seeding + shell use; seed_corpus.txt; _smoke_mcp.py handshake test Secrets load from ~/.hermes/.env; uses VOICE_TOOLS_OPENAI_KEY (real OpenAI) since Hermes OPENAI_API_KEY is an OpenRouter key with no embeddings endpoint. Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Implements the daily arxiv paper fetcher that was missing, unblocking the Quant Scholar workflow. Supersedes the disable PR (LLMQuant#2 on fork). - Fetches last 7 days of q-fin.* papers (primary, no keyword filter) - Fetches cs.LG / cs.AI / stat.ML filtered to quant-finance keywords - Ranks top 50 by keyword-match count + q-fin primary-category bonus - Groups by topic: RL / Deep Learning / Time Series / ML / Quant Finance - Writes docs/papers.md (GitHub-flavoured markdown table, same format as upstream quant-scholar project) - Writes docs/quant-scholar.json (structured array with title, authors, arxiv_id, date, categories, abstract, pdf_url, score, topic) Also includes a fresh run of the script (docs/ updated to 2026-06-24). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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Ships the missing
quant_scholar.pyscript the daily cron workflow expected but could not find, causing the workflow to fail.Changes
quantmind/flows/quant_scholar.py— ArXiv fetcher using the existingpreprocesslayer (fetch_arxiv+pdf_to_markdown). Searches configurable query terms, returnsPaperknowledge objects..github/workflows/quant-scholar.yml— re-enables the daily cron (was disabled in a prior hotfix while the script was absent).Why
The cron was silently failing because the script it called did not exist. This PR closes that gap: the script is now present and the workflow is re-enabled.
Testing
Ran locally against the arxiv API; verified
Paperobjects are produced with correctSourceRef/ExtractionRefprovenance fields per theknowledge/data standard.